i

Understanding and optimising decision-making

about chemotherapy for older adults with cancer

Erin Beverley Moth

This thesis is submitted in full satisfaction of the requirements for the

degree of Doctor of Philosophy, The University of Sydney

2019

Sydney Medical School,

Faculty of Medicine and Health

The University of Sydney

ii

Abstract

This thesis presents a collection of work aiming to better understand and optimise decision-

making about treatment with chemotherapy for older adults with cancer.

The background focuses on decision-making about chemotherapy in oncology. A tumour-

specific example is provided by a review of treatment considerations for older adults with colon

cancer. A paucity of evidence to guide chemotherapy recommendations, competing

comorbidity, frailty, and age-related changes in physiology that affect treatment tolerance, and

patients’ preferences and priorities add complexity to decision-making in this setting.

Australian oncologists (n=98) were surveyed about how they make chemotherapy

recommendations for their older patients. Use of geriatric assessments (GA) or screening tools

was infrequent. Considered most important when making a recommendation was patient

performance status, with other considerations varying with treatment intent. Estimated survival

benefit of treatment and life expectancy in the absence of cancer were important for curative-

intent chemotherapy, and patient preference and quality of life were important for palliative-

intent chemotherapy. Hypothetical patient scenarios revealed oncologists were less likely to

recommend chemotherapy as patient age and toxicity increased.

The Cancer and Aging Research Group’s (CARG) Toxicity Score was tested in a prospective

observational study of 126 older adults commencing chemotherapy for a solid organ cancer.

Neither the CARG Score (OR 1.04, 95%CI 0.92-1.18, p=0.54, AU-ROC 0.52), nor

oncologists’ estimates based on clinical judgement (OR 1.00, 95%CI 0.98-1.02, p=0.82, AU-

ROC 0.52) predicted severe chemotherapy-related toxicity in this population. For 30 patients

recruited consecutively, their treating oncologists (n=8) were surveyed following receipt of the

CARG Score and GA. Whilst oncologists found the CARG Score and GA useful, they were

unlikely to use them to modify chemotherapy recommendations. The GA provided oncologists

with new information that prompted a supportive intervention in approximately 1-in-4 patients.

The nature and accuracy of oncologists’ estimates of survival time was assessed for 102 older

adults receiving palliative chemotherapy for advanced cancer. Oncologists’ point estimates of

survival time were imprecise, but were well calibrated, and simple multiples of these estimates

accurately described best-case, typical, and worst-case scenarios for survival.

Older adults with advanced cancer (n=179) who had made a decision about treatment with

chemotherapy were surveyed regarding their preferred and perceived roles in decision-making,

their priorities and information needs. Preferred decision-making roles were active in 39%,

collaborative in 27%, and passive in 35%, and matched the perceived role for 63% of

respondents. Most frequently ranked as the most important consideration when deciding about

chemotherapy were “doing everything possible”, “my doctor’s recommendation”, “quality of

life”, and “living longer”. A minority anticipated cure (14%) and fewer received quantitative

prognostic information (49%) than desired this information (67%).

In conclusion, decision-making about chemotherapy for older adults with cancer is complex.

Individualised consideration of anticipated benefits and harms of the treatment is needed.

Shared understanding of patient priorities, goals of care, and provision of tailored information

about likely outcomes, including prognosis, are required. Further evidence is needed to support

the use of additional assessments or prediction tools in guiding decision-making about

treatment with chemotherapy for older adults.

iii

Certificate of originality

This thesis is submitted to the University of Sydney in fulfilment of the regulations for the

degree of Doctor of Philosophy. The work presented in this thesis is, to the best of my

knowledge and belief, original except as acknowledged in the text. I hereby declare that I have

not submitted this material, either in full or in part, for a degree at this or any other institution.

………………………… ………………………

Erin Moth Date

iv

Author’s contribution

I undertook this PhD as a full-time PhD student between March 2015 and January 2018, and

as a part-time PhD student between February 2018 and July 2019 at the Sydney Medical School

(Faculty of Medicine) University of Sydney under the supervision of Dr Prunella Blinman, Dr

Belinda Kiely, Prof Vasikaran Naganathan, and A/Prof Andrew Martin.

This thesis is presented as a hybrid thesis with a combination of traditional chapters (chapters

1, 2, 3, and 10) and publications (chapters 4, 5, 6, 7, 8, and 9). For the traditional chapters, I

was responsible for the literature review, data synthesis, interpretation of findings, and drafting

and revision of the manuscripts for these chapters.

For the studies presented as chapters 4, 5, 6, 7, 8, and 9, I was responsible for the development

of the research proposal and research methods, submissions to ethics, data collection and

analysis, interpretation of findings, and the drafting and revision of the manuscripts.

v

Acknowledgements

I would like to thank:

Dr Prunella Blinman, my primary supervisor, for your endless enthusiasm, encouragement, and hard

work; for your emotional sensibility; for balancing my doubts with reassurance and optimism. I am

incredibly grateful for everything you have done to help produce this work and in challenging me to

grow as a clinician.

My associate supervisors, Dr Belinda Kiely, Prof Vasi Naganathan, and A/Prof Andrew Martin, for

your sound advice and expertise. Belinda, for your scientific sensibility and attention to detail; Vasi,

for your common sense and curiosity; Andrew, for your patience and guidance.

My research assistant and friend, Natalie Stefanic, for your enthusiasm, hard work, and support.

Professor Martin Stockler, for taking the time to share your wisdom.

The patients and clinicians who participated so willingly.

My dear friend, Darshi, for your encouragement and guidance, and nearly twenty years of friendship.

My parents, Bev and Les, for your generosity, love, and support over the many years of education in

Medicine. For always ensuring I had every opportunity to succeed. I hope this makes you proud.

My daughters, Mairead and Claire, for your hugs, smiles, and antics. Mummy did it, and I did it in

part for you. To show you that learning is a lifelong journey, and education is a privilege to be

treasured. I love you both dearly, more than you know.

And finally, my husband, Adam. For being everything for our family that I have not been, and am not

able to be. For always recognising my achievements when I see none, hearing my doubts when I have

many, and being a source of unconditional and unwavering love. This accomplishment is as much

yours as it is mine.

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Publications arising from this thesis

Peer-reviewed journal articles

1. Moth EB, Vardy J, Blinman P. Decision-making in geriatric oncology: systemic treatment

considerations for older adults with colon cancer. Expert Rev Gastroenterol Hepatol,

2016. 10(12): 1321-1340.

2. Moth EB, Kiely BE, Naganathan V, Martin A, Blinman P. How do oncologists make

decisions about chemotherapy for their older patients with cancer? A survey of Australian

oncologists. Support Care Cancer, 2018. 26(2): 451-460.

3. Moth EB, Kiely BE, Stefanic N, Naganathan V, Martin A, Grimison P, Stockler MR,

Beale P, Blinman P. Predicting chemotherapy toxicity in older adults: Comparing the

predictive value of the CARG Toxicity Score with oncologists' estimates of toxicity based

on clinical judgement. J Geriatr Oncol, 2018 10(2): 202-209.

4. Moth EB, Kiely BE, Stefanic N, Naganathan V, Martin A, Grimison P, Stockler MR,

Beale P, Blinman P. Oncologists' perceptions on the usefulness of geriatric assessment

measures and the CARG toxicity score when prescribing chemotherapy for older patients

with cancer. J Geriatr Oncol, 2018 10(2): 210-215.

5. Moth EB, Kiely BE, Stefanic N, Naganathan V, Martin A, Grimison P, Stockler MR,

Beale P, Blinman P. Estimating survival time in older adults with advanced cancer. J

Geriatr Oncol, 2019. Doi: 10.1016/j.jgo.2019.08.013. [Epub ahead of print]

6. Moth EB, Kiely BE, Martin A, Naganathan V, Della-Fiorentina S, Honeyball F, Zielinski

R, Steer C, Mandaliya H, Ragunathan A, Blinman P. Older adults’ preferred and

perceived roles in decision-making about palliative chemotherapy, their decision

priorities, and information preferences. J Geriatr Oncol, 2019. Doi:

10.1016/j.jgo.2019.07.026. [Epub ahead of print]

vii

Presentations and awards arising from this thesis

Invited speaker

2019 International Society of Geriatric Oncology (SIOG) ASM, Geneva

The use of clinical prediction tools in decision-making about chemotherapy

2019 Australasian Lung Cancer Trials Group (ALTG) ASM, Sydney

Tailoring the approach to older adults with lung cancer

Oral presentations

2019 Sydney Catalyst Early Career Researchers’ Symposium, Sydney

Older adults’ preferred and perceived roles in decision-making about chemotherapy

2018 Medical Oncology Group of Australia (MOGA) ASM, Adelaide

Predicting chemotherapy toxicity in older adults with cancer: comparing the CARG

Toxicity Score to oncologists’ estimates of toxicity based on clinical judgement

2018 Sydney Catalyst Early Career Researchers’ Symposium, Sydney

Predicting chemotherapy toxicity in older adults with cancer: comparing the CARG

Toxicity Score to oncologists’ estimates of toxicity based on clinical judgement

2016 Sydney Catalyst Early Career Researchers’ Symposium, Sydney

Comparing the risk of chemotherapy for older adults as estimated using the CARG

Toxicity Score with oncologists’ clinical judgement

Poster presentations

International

2019 American Society of Clinical Oncology (ASCO) ASM, Chicago

2018 Australian and New Zealand Society of Geriatric Medicine (ANZSGM) ASM,

Sydney

2016 MASCC Annual Scientific Meeting on Supportive Care in Cancer, Adelaide

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2016 SIOG International Society Of Geriatric Oncology Annual Scientific Meeting, Milan

2015 MASCC Annual Scientific Meeting on Supportive Care in Cancer, Geneva

National

2019 Medical Oncology Group of Australia (MOGA) ASM, Canberra

2018 Medical Oncology Group of Australia (MOGA) ASM, Adelaide

2017 Clinical Oncology Society of Australia (COSA) ASM, Sydney

2016 Medical Oncology Group of Australia (MOGA) ASM, Hobart

Awards

2019 Best Rapid Fire Oral Presentation, Sydney Catalyst Early Career Researchers’

Symposium

2018 Best Young Oncologist Oral Presentation, MOGA ASM

2018 Best T2/T3 Oral Presentation, Sydney Catalyst Early Career Researchers’ Symposium

2016 Best Pet Project Oral Presentation, Sydney Catalyst Early Career Researchers’

Symposium

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Grants and scholarships arising from this thesis

Sydney Local Health District Cancer Services Research Grant (CIA Dr EM)

The University of Sydney Australian Postgraduate Award (APA)

PhD Top-Up Scholarship from Sydney Catalyst: the Translational Cancer Research Centre of

Central Sydney and regional NSW, The University of Sydney, NSW, Australia and Cancer

Institute NSW

x

Table of contents

Abstract………………………………………………………………………………… ii

Certificate of originality………………………………………………………………... iii

Author’s contribution…………………………………………………………………... iv

Acknowledgements………………………………………………………………… …. v

Publications arising from this thesis…………………………………………………… vi

Presentations and awards arising from this thesis……………………………………… vii

Grants and scholarships arising from this thesis……………………………………….. ix

Table of contents……………………………………………………………………….. x

List of tables…………………………………………………………………………… xv

List of figures……………………………………………………………………….…. xvii

Supplementaries……………………………………………………………………..… xix

List of frequently used abbreviations………………………………………………….. xxi

1. Introduction……………………………………………………………………….. 1

1.1 Rationale for the thesis……………………………………………………...1

1.2 Key terms…………………………………………………………………... 3

1.3 Selection of studies for the thesis………………………………………….. 4

1.4 Aims and objectives……………………………………………………….. 5

1.5 Outline of chapters…………………………………………………………. 6

1.6 Projects as they relate to chapters presented………………………………. 9

2. Background……………………………………………………………………… 10

2.1 Overview…………………………………………………………………… 10

2.2 Decision-making in oncology……………………………………………… 11

xi

2.3 Decision-making considerations unique to the older adult………………… 17

2.4 The field of geriatric oncology…………………………………………….. 21

2.5 The approach of older adults and their oncologists to decision-making about

chemotherapy……………………………………………………………… 36

2.6 Predicting chemotherapy toxicity in older adults…………………………. 42

2.7 Summary…………………………………………………………………… 54

3. Methodological considerations………………………………………………….. 55

3.1 Overview………………………………………………………………….. 55

3.2 Design and use of the geriatric assessment in this thesis…………………. 56

3.3 Evaluating prediction tools to inform treatment decisions………………… 62

3.4 Estimating survival time to inform treatment decisions…………………… 69

3.5 Evaluating patient and physician aspects of decision-making……………. 74

3.6 Summary…………………………………………………………………… 79

4. Principles of geriatric oncology: systemic treatment considerations for older

adults with colon cancer…………………………………………………………. 80

4.1 Overview…………………………………………………………………… 80

4.2 Abstract…………………………………………………………………….. 81

4.3 Introduction………………………………………………………………… 82

4.4 Adjuvant chemotherapy considerations……………………………………. 83

4.5 Palliative chemotherapy considerations……………………………………. 89

4.6 The assessment of older adults with cancer for chemotherapy……………. 96

4.7 Making a decision about treatment………………………………………… 100

4.8 Expert commentary………………………………………………………… 104

xii

5. How do oncologists make decisions about chemotherapy for older adults with

cancer?...................................................................................................................... 117

5.1 Overview………………………………………………………………….... 117

5.2 Abstract…………………………………………………………………….. 119

5.3 Introduction………………………………………………………………… 120

5.4 Methods……………………………………………………………………. 121

5.5 Results……………………………………………………………………… 124

5.6 Discussion………………………………………………………………….. 126

5.7 Conclusion…………………………………………………………………. 132

6. Predicting chemotherapy toxicity in older adults: comparing the predictive value

of the CARG Toxicity Score with oncologists’ estimates of toxicity based on

clinical judgement………………………………………………………………… 141

6.1 Overview…………………………………………………………………… 141

6.2 Abstract…………………………………………………………………….. 143

6.3 Introduction………………………………………………………………… 145

6.4 Methods……………………………………………………………………. 146

6.5 Results……………………………………………………………………… 149

6.6 Discussion………………………………………………………………….. 152

6.7 Conclusion…………………………………………………………………. 157

7. Oncologists’ perceptions on the usefulness of geriatric assessment measures and

the CARG Toxicity Score when prescribing chemotherapy for older patients

with cancer………………………………………………………………………… 173

7.1 Overview…………………………………………………………………… 173

7.2 Abstract…………………………………………………………………….. 175

7.3 Introduction………………………………………………………………… 177

7.4 Methods……………………………………………………………………. 178

xiii

7.5 Results……………………………………………………………………… 180

7.6 Discussion………………………………………………………………….. 182

7.7 Conclusion…………………………………………………………………. 185

8. Estimating survival time in older adults with advanced cancer……………….. 194

8.1 Overview…………………………………………………………………… 194

8.2 Abstract…………………………………………………………………….. 196

8.3 Introduction………………………………………………………………… 198

8.4 Methods……………………………………………………………………. 200

8.5 Results……………………………………………………………………… 203

8.6 Discussion………………………………………………………………….. 205

8.7 Conclusion…………………………………………………………………. 210

9. Older adults’ preferred and perceived roles in decision-making about palliative

chemotherapy, their decision priorities, and information preferences……….. 220

9.1 Overview…………………………………………………………………… 220

9.2 Abstract…………………………………………………………………….. 223

9.3 Introduction………………………………………………………………… 225

9.4 Methods……………………………………………………………………. 226

9.5 Results……………………………………………………………………… 230

9.6 Discussion………………………………………………………………….. 233

9.7 Conclusion…………………………………………………………………. 237

10. Discussion…………………………………………………………………………. 248

10.1 Overview………………………………………………………………….. 248

10.2 Principal findings………………………………………………………….. 249

10.3 Significance of findings…………………………………………………… 254

10.4 Strengths…………………………………………………………………… 259

xiv

10.5 Limitations………………………………………………………………… 261

10.6 Concluding remarks………………………………………………………. 264

11. References………………………………………………………………………… 265

Appendices

Appendix A Australian oncologist questionnaire (Chapter 5)………………....... 310

Appendix B Geriatric assessment used in this thesis……………………………. 320

Appendix C Geriatric assessment results summary (Chapter 7)………………… 330

Appendix D Oncologist questionnaire (Chapter 7) ……………………………... 332

Appendix E Participant questionnaire (Chapter 9)……………………………… 334

Appendix F Publications………………………………………………………... 345

xv

List of tables

Chapter 2

Table 1. Systematic reviews on the use and value of the geriatric assessment in older

adults in oncology………………………………………………………….. 31

Table 2. Grading of chemotherapy-related toxicities using the Common Terminology

Criteria for Adverse Events (CTCAE)……………………………………... 43

Table 3. Prospective studies evaluating predictors of chemotherapy toxicity in older

adults with solid organ cancer………………………………………………44

Table 4. Chemotherapy Risk Assessment for High-Age Patients (CRASH) Score… 49

Table 5. Rates of severe chemotherapy toxicity according to CRASH Score………. 49

Table 6. Predictive model for calculation of the CARG Toxicity Score in 500 older adults

commencing chemotherapy………………………………………………... 50

Table 7. Predictive ability of the CARG Toxicity Score……………………………. 51

Chapter 3

Table 1. Abbreviated Geriatric Assessment………………………………………… 56

Table 2. Consequences of a prediction tool for chemotherapy toxicity in practice… 66

Table 3. The Control Preferences Scale…………………………………………….. 75

Chapter 4

Table 1 Key elderly-specific colon cancer chemotherapy trials, subgroup analyses, and

large population-based studies in the adjuvant setting…………………….. 106

Table 2 Elderly-specific prospective colon cancer trials and large subgroup analyses in

the palliative setting………………………………………………………... 110

xvi

Chapter 5

Table 1 Participant demographics and clinical practice……………………………. 133

Table 2 Ranking of factors important in decision-making about chemotherapy….. 134

Table 3 Predictors of chemotherapy recommendation in hypothetical scenarios… 135

Chapter 6

Table 1. Participant characteristics…………………………………………………. 158

Table 2. Baseline geriatric assessment results………………………………………. 159

Table 3. Predictors of severe chemotherapy-related toxicity……………………….. 161

Chapter 7

Table 1. Patient (n=30) characteristics………………………………………………. 187

Table 2. Baseline geriatric assessment results (N=30)……………………………… 188

Table 3. Comments from Oncologists on barriers to use of the Geriatric Assessment

and Cancer and Aging Research Group’s (CARG) Toxicity Score………. 190

Chapter 8

Table 1. Characteristics of 102 participants…………………………………………. 211

Table 2. Geriatric assessment measures at baseline on 102 participants…………… 212

Table 3. Factors associated with observed survival time……………………………. 213

Chapter 9

Table 1. Respondent characteristics (n=179)……………………………………….. 238

Table 2. Preferred versus perceived roles in decision making about palliative

chemotherapy………………………………………………………………. 239

Table 3. Rating and ranking of factors considered important by older adults in making a

decision about palliative chemotherapy……………………………………. 240

xvii

List of figures

Chapter 3

Figure 1. Distribution of test scores in two populations, those who have the outcome of

interest and those who do not……………………………………………….63

Figure 2. The Receiver Operating Characteristic (ROC) Curve……………………... 64

Figure 3. Area under the Receiver Operating Characteristic (ROC) Curve…………. 65

Figure 4. Pathway to day-to-day use of a clinical prediction tool…………………… 68

Figure 5. Scenarios for survival derived from the Kaplan-Meier survival curve. …… 70

Figure 6. Precision, and calibration of estimates (or measurements)………………… 72

Chapter 4

Figure 1. Key factors influencing systemic treatment decisions for older adults with

colon cancer………………………………………………………………... 116

Chapter 5

Figure 1. Assessments routinely performed by oncologists for older adults with

cancer………………………………………………………………………. 136

Figure 2. Attitudes towards chemotherapy decision-making………………………… 137

Figure 3. Relationship between chemotherapy recommendation, age, treatment toxicity,

and setting………………………………………………………………….. 138

Chapter 6

Figure 1. Distribution of the CARG Toxicity Score (A) and Oncologists’ estimates (B)

in study population (n=126)……………………………………………….. 162

Figure 2. Relationship between CARG Toxicity Score and Oncologists’ estimates of the

likelihood of severe chemotherapy-related toxicity……………………….. 163

xviii

Figure 3. Predictive value for toxicity of the CARG Toxicity Score (A), Oncologists’

estimates (B), and a combined model of the two (C) as modelled by Receiver

operating characteristic (ROC) curves…………………………………….. 164

Chapter 7

Figure 1. Perceived clinical value and impact on chemotherapy prescribing of the

Cancer and Aging Research Group’s (CARG) Score and Geriatric

Assessment…………………………………………………………………. 191

Figure 2. Oncologist ratings of ease of use of the Cancer and Aging Research Group’s

(CARG) Score and Geriatric Assessment………………………………….. 192

Chapter 8

Figure 1. Distribution of oncologists’ estimates of expected survival time (EST) for 102

older adults commencing palliative chemotherapy…………………………215

Figure 2. Relationship between observed and estimated survival times for each

individual patient………………………………………………………….. 216

Figure 3. Kaplan Meier curves of: A. observed and estimated survival times for 102

older adults starting palliative chemotherapy; B. observed survival times by

frailty; C. estimates of expected survival time by frailty………………….. 217

Chapter 9

Figure 1. Distribution of preferred and perceived roles in decision-making………… 241

Figure 2. Importance of the opinion of significant others…………………………… 242

Figure 3. Patient expectations of palliative chemotherapy……………………………243

Chapter 10

Figure 1. Main findings……………………………………………………………….253

xix

Supplementaries

Chapter 5

Suppl. Figure A. Mean importance rating of factors influencing chemotherapy

prescribing for older adults in the curative and palliative settings… 139

Suppl. Figure B. Likelihood of prescribing chemotherapy according to age in each

treatment setting……………………………………………………. 140

Chapter 6

Suppl. Table 1. Comparison of Study Population versus Hurria et al Population by

components of the CARG Score…………………………………… 165

Suppl. Table 2. Most common severe (grade 3 to 5) chemotherapy-related

toxicities………………………………………………………….... 166

Suppl. Table 3. All-grade chemotherapy-related toxicities………………………… 167

Suppl. Table 4. Hospitalisations and completion of planned treatment by toxicity risk

groups………………………………………………………………. 168

Suppl. Table 5. Details of 67 Hospitalisations in 126 older adults receiving

chemotherapy……………………………………………………… 169

Suppl. Table 6. Association between the 11-Items of the CARG Score and toxicity.170

Suppl. Figure 1. Severe chemotherapy toxicity according to risk group by CARG Score

(A) and oncologists’ estimate (B)…………………………………. 171

Suppl. Figure 2. Relationship between CARG Toxicity Score and all-grade toxicity. 172

Chapter 7

Suppl. Table 1. Patients’ Cancer and Aging Research Group’s (CARG) Toxicity Risk

Group by Oncologist-rated Clinical Frailty Rating………………... 191

xx

Chapter 8

Suppl. Table 1. ECOG Performance Status by CSHA Clinical Frailty Scale………. 218

Suppl. Figure 1. Schema…………………………………………………………….. 219

Chapter 9

Suppl. Table 1. The Control Preferences Scale…………………………………….. 244

Suppl. Table 2. Discrepancy between preferred and perceived decision-making

roles………………………………………………………………… 244

Suppl. Table 3. Associations with preferred role in decision-making about palliative

chemotherapy………………………………………………………. 245

Suppl. Table 4. Associations with concordance between preferred and perceived role in

decision-making……………………………………………………. 246

Suppl. Figure 1. Agreement charts showing receipt and desire for five items of

information…………………………………………………………. 247

xxi

List of frequently used abbreviations

GA Geriatric assessment

CARG Cancer and Aging Research Group

ECOG-PS Eastern Cooperative Oncology Group Performance Status

AUC Area under the curve

ROC Receiver operating characteristic curve

HR Hazard ratio

OS Overall survival

ADL Activities of daily living

IADL Instrumental activities of daily living

CSHA Canadian Study on Health and Aging

EST Estimated survival time

OST Observed survival time

CPS Control Preferences Scale

SDM Shared decision making

1

1. Introduction

1.1 Rationale for thesis

Due to the ageing population, oncologists are seeing increasing numbers of older adults with

cancer for whom a decision about cancer treatment needs to be made. Traditional cytotoxic

chemotherapy is still widely used in clinical practice despite the advent of new classes of anti-

cancer drug therapies. The decision to recommend or accept treatment with chemotherapy is

ideally made by oncologists and their patients after consideration of the balance of its benefits

and harms, goals and priorities, and patient preferences for the treatment. Older adults are less

likely than their younger counterparts to receive chemotherapy, and, when received, the

chemotherapy regimen is less intense. Parallel to concerns about undertreatment are concerns

about overtreatment, with older adults receiving chemotherapy considered at increased risk or

less tolerant of chemotherapy toxicity.

Heterogeneity in treatment patterns observed in older adults alludes to the complexity of

decisions about chemotherapy in this group. Consideration of the factors unique to older adults

is required when making decisions about chemotherapy, including underrepresentation of older

adults on clinical treatment trials that serve to guide oncologists’ recommendations, and

increased comorbidity, frailty, and age-related physiologic impairments that affect tolerance of

the treatment. The priorities of older adults considering chemotherapy may be different to

younger adults and may also contribute to observed treatment disparities.

Assessment of fitness of older adults for cancer treatment may include a geriatric assessment,

a relatively recent addition to clinical practice recommendations in oncology. A geriatric

assessment is performed at the time of the initial review of the patient, and provides holistic

2

information about an older adult’s health. Information from the geriatric assessment has been

used to derive clinical risk prediction tools for use in the oncology setting, specifically to

predict the likelihood of occurrence of severe chemotherapy-related toxicity in older adults.

Such tools may improve decision-making about treatment, and therefore outcomes, for older

adults.

The work presented in this thesis was motivated by the desire to better understand and improve

decision-making about chemotherapy for older adults with cancer. Principal focus is given to

the value of a clinical prediction tool for chemotherapy toxicity in the local setting, contributors

to oncologists’ chemotherapy recommendations, and their older patients’ decision-making

experiences and preferences. The work presented narrows decision-making about

chemotherapy to older adults who have been referred to a medical oncologist to discuss the

treatment, acknowledging that some judgements about their suitability for the treatment may

have already been made by referring clinicians.

3

1.2 Key terms

‘Older adult’ in this thesis refers to an adult aged 65 years or older. This chronologic definition

was chosen for consistency with the literature at the time of design, particularly to enable

comparison of the performance of chemotherapy toxicity predictive tools by using inclusion

criteria identical to studies in which they were derived.

‘Decision-making’ in this thesis refers to the process undertaken to come to a choice between

alternatives having considered their relative benefits and harms. This mostly refers to a choice

to receive (or not receive), or recommend (or not recommend) chemotherapy, unless otherwise

specified.

‘Cancer’ for the purposes of this thesis refers to solid organ malignancies, unless otherwise

specified.

‘Chemotherapy’ in this thesis refers to traditional cytotoxic chemotherapy (alkylating agents,

antimetabolites, alkaloids, and antitumour antibiotics), and not newer targeted agents

(monoclonal antibodies, kinase inhibitors) endocrine therapy, or immunotherapy.

‘Geriatric syndromes’ refers to a group of clinical conditions associated with, but not specific

to aging, that do not fall into discrete disease categories, are multifactorial, and are associated

with significant morbidity. Geriatric syndromes include, but are not limited to: falls, urinary

incontinence, pressure ulcers, delirium, functional decline, and frailty.

4

1.3 Selection of studies for the thesis

This thesis is presented as a hybrid thesis, and, as such, combines both traditional thesis

chapters with journal publications. A series of related journal publications are presented as

Chapters 4, 5, 6, 7, 8, and 9, and are unified by the traditional chapters: Chapter 2 (background),

Chapter 3 (methodological considerations), and Chapter 10 (discussion).

The theme of this thesis is understanding and optimising decision-making about treatment with

chemotherapy for older adults with cancer. The studies selected for presentation in this thesis

relate to different aspects of the decision-making process and from different perspectives, yet

all inform the theme. The body of work presented adds original data to the research on

chemotherapy treatment decision-making for older adults.

5

1.4 Aims and objectives

The general aim of the work presented in this thesis is to better understand and optimise

decision-making about chemotherapy for older adults with cancer. The specific objectives were

to:

a. Review the available literature on aspects of treatment decision-making about

chemotherapy in older adults with solid organ malignancies, using colon cancer as an

example.

b. Determine how oncologists make decisions to recommend chemotherapy to older adults

with cancer, and the impact of age and likelihood of treatment toxicity on those decisions.

c. Determine the predictive value of a chemotherapy toxicity risk prediction tool in a local

population, and compare it to the predictive value of oncologists’ clinical judgement of the

risk of toxicity.

d. Determine oncologists’ perceptions on the usefulness of an existing chemotherapy toxicity

risk prediction tool and geriatric assessment when recommending chemotherapy to their

older patients, including their impact on chemotherapy prescribing.

e. Determine the accuracy and nature of oncologists’ estimates of expected survival time for

older adults with advanced cancer.

f. Explore potential predictors of severe chemotherapy toxicity and observed survival from a

geriatric assessment.

g. Determine older adults’ preferred and perceived roles in decision-making about treatment

with chemotherapy, factors influencing their decision about treatment, and their

information needs and preferences.

6

1.5 Outline of chapters

The traditional chapters of this thesis are Chapters 1, 2, 3, and 10, and journal publications are

Chapters 4, 5, 6, 7, 8, and 9.

Chapter 2 provides background for the thesis. Themes discussed are the complexities of

decision-making about chemotherapy for older adults, the concept of ‘fitness for

chemotherapy’, the role of the geriatric assessment and risk prediction tools in informing

decisions about treatment, factors influencing oncologists’ and older patients’ decisions about

treatment with chemotherapy, balancing the benefits and harms of chemotherapy, and

informing treatment decisions through an understanding of expected outcomes, including

survival time.

Chapter 3 provides an overview of the statistical considerations and methodology utilised in

Chapters 5, 6, 7, 8, and 9 of the thesis. Detailed methods are otherwise presented within each

published chapter.

Chapter 4 is a narrative review of the literature, addressing chemotherapy treatment

considerations for older adults with colon cancer. This complements the background to the

thesis by discussing some of the concepts outlined with specific application to a single tumour

type (colon cancer). Specifically, the translation of evidence for both adjuvant and palliative

chemotherapy to an older population with colon cancer, the assessment of older adults prior to

chemotherapy, the role of the geriatric assessment and tools to aid in predicting chemotherapy

toxicity, and considering the balance between the benefits and harms of treatment and patient

preferences to aid treatment decision-making.

7

Chapter 5 is a published cross-sectional survey study of Australian oncologists with the aim to

determine the factors considered important by oncologists in making a chemotherapy treatment

recommendation for their older patients. Methods of clinical assessment used to guide

treatment decisions, and the effect of age and risk of severe treatment toxicity on the likelihood

to recommend chemotherapy in both the adjuvant and palliative settings were also explored.

Chapter 6 is a published prospective observational study of 126 older adults commencing

chemotherapy with the aim to determine the clinical value of an existing chemotherapy toxicity

risk prediction tool (the Cancer and Aging Research Group’s Toxicity Score) in the local

population, and comparing it to the value of an estimate of the likelihood of chemotherapy

toxicity provided by treating oncologists based on clinical judgement. Additional predictors of

chemotherapy toxicity from a Geriatric Assessment were also explored.

Chapter 7 is a companion publication to Chapter 6, with the aim to evaluate the value of the

CARG Toxicity Score and Geriatric Assessment to oncologists when making a decision about

chemotherapy for their patients with cancer, and the potential impact of these assessments on

chemotherapy prescribing and patient management.

Chapter 8 is a published observational study with the aim to evaluate the accuracy of

oncologists’ estimates of expected survival time for older adults with advanced, incurable

cancer, and the prognostic information gained from a Geriatric Assessment. Oncologists’

estimates of expected survival time are used to model best-case, typical, and worst-case

prognostic scenarios.

8

Chapter 9 is a published cross-sectional survey study with the aim to determine the treatment

decision-making preferences of older adults with advanced cancer. Older adults who had

recently made a decision regarding treatment with palliative chemotherapy were surveyed to

determine their preferred role in decision-making about treatment, their perceived role played

in decision-making about treatment, factors influencing their decision about treatment, and

their information needs and preferences.

Chapter 10 provides a discussion of the work in this thesis as a whole, including a summary of

the main findings, a discussion of the work in the context of existing studies, strengths and

limitations, clinical and research implications, and concluding remarks.

9

1.6 Projects as they relate to chapters presented

Cross-sectional

survey

Cross-sectional

survey

Prospective

observational

study

Older adults (≥65 years)

with advanced cancer for

whom a treatment

decision about

palliative chemotherapy

has been made with their

oncologist

Determining the

decision-making and

information

preferences of older

adults with advanced

cancer

n=177

Chapter 9

Australian oncologists Determining the

factors that influence

oncologists’ decisions to

recommend

chemotherapy to older

adults

n=177

Chapter 5

Older adults (≥65 years)

commencing an initial or

new line of

chemotherapy for a

solid organ cancer

(any type or stage)

n=156

Comparing the CARG

Toxicity Score with

oncologists’ clinical

judgement for

predicting

chemotherapy toxicity

n=126

Evaluating how useful

the CARG Toxicity Score

and GA were to

oncologists, and their

impact on prescribing

n=30

Evaluating oncologists’

estimates of survival

time, in those with

advanced, incurable

cancer

n=102

Chapter 8

Chapter 7

Chapter 6

10

2. Background

2.1 Overview

This chapter provides a background for the thesis as a whole. Relevant concepts are covered in

more detail than within the published papers presented in Chapters 4 to 9.

Section 2.2 provides an overview of key concepts in treatment decision-making in oncology.

Section 2.3 addresses considerations in treatment decision-making relevant to the older adult.

Section 2.4 provides a background to the subspecialty of geriatric oncology, and introduces the

geriatric assessment as a key concept in the evaluation of the older adult with cancer. Section

2.5 provides the rationale and background to predicting chemotherapy toxicity in older adults,

relevant to the main prospective study included in the thesis. Section 2.6 provides background

to the approach of older adults and their oncologists to decision-making about chemotherapy.

Areas of research of the PhD studies presented as published papers in Chapters 5 to 9 are

highlighted throughout the background chapter.

11

2.2 Decision-making in oncology

2.2.1 Decision-making about chemotherapy

For the treating oncologist, decision-making about treatment with chemotherapy is a multi-step

process, involving consideration of whether to recommend chemotherapy, which

chemotherapy regimen to recommend, and the appropriate regimen intensity and dose. For the

patient with cancer, decision-making about treatment with chemotherapy refers to the process

of deciding whether or not to have the treatment, and in some circumstances may involve a

choice between regimens. Many factors influence these decisions for both patient and

oncologist.

Considering benefits and harms

The potential benefits and harms of chemotherapy are of paramount concern for both patient

and oncologist when making a decision about treatment, and are related to the setting in which

the treatment is being considered. For patients with potentially curable early stage solid organ

cancers, chemotherapy is largely given as neoadjuvant therapy prior to, or adjuvant therapy

after definitive local treatment (usually surgery), with the aims of reducing the risk of

recurrence of the cancer and improving overall survival. In this setting, patients are generally

well without evidence of cancer and so the tangible inconveniences and toxicities of

chemotherapy must be weighed up against longer term, imperceptible survival gains. For

patients with late stage, incurable solid organ cancers, chemotherapy is given as ‘palliative’

treatment with the aim of improving quality of life by alleviating cancer-related symptoms or

prolong survival but not curing the cancer. In this setting, the benefits of chemotherapy on

survival and quality of life are more tangible, but the potential harms can readily impair quality

of life for people already living with the effects of advanced cancer. The challenge here is

maximising both quality and quantity of life.

12

The potential benefits and harms of chemotherapy extend beyond gains in survival, relief of

cancer-related symptoms, and chemotherapy-related toxicity, and will be patient-dependent in

terms of their relative importance and impact on quality of life. Additional considerations

include (but are not exclusive of) the psychological impact of treatment (or declining

treatment), time spent away from friends and family, financial toxicity, and the inconveniences

of frequent medical appointments, including associated travel and disease monitoring with

venepuncture and imaging.

2.2.2 Shared decision-making

Shared Decision Making (SDM) is a theoretical model applicable to decision-making in

oncology. Through the model of SDM, a patient and their oncologist work together to come to

an agreed decision about treatment that considers a patient’s preferences. A patient’s preference

for a treatment refers to their evaluation of the relative benefits and harms of the treatment

compared with given alternatives. (1) In SDM, both doctor and patient:

a. are involved,

b. share information,

c. express their treatment preferences, and

d. are in agreement with the final treatment decision (2, 3)

The information provided by doctors within this model should include information about the

disease, available options for treatment, the expected benefits and harms of all treatment

options, and outcomes, including prognosis. (3)

SDM contrasts to other models of decision-making. In the ‘paternalistic model’, decisions are

made by the doctor on behalf of the patient using their knowledge and expertise, akin to “doctor

knows best” without consideration of the patient’s preferences for the treatment. (4) In the

13

model of ‘informed decision-making’, the doctor acts to provide information to the patient that

is relevant to the decision at hand, but leaves the final decision up to the patient. (5) In the

‘doctor as agent’ model, the patient gives authority to the doctor to make decisions for them

using their expertise and incorporating their treatment preferences (that is, making the same

decision the patient would make if the patient had the same knowledge as the doctor). (6) In

clinical practice, oncologists may move from use of one model to another, dependent on the

clinical situation, the treatment decision at hand, and the willingness or ability of the patient to

engage in the decision. (4)

2.2.3 Roles in decision-making

SDM requires both oncologists and patients being involved in the decision-making process,

however, patients have their own preferences with regard to the role they wish to play.

Described by Degner and Sloan, levels of involvement in decision-making about treatment

vary from active, where the patient makes the decision about treatment, to collaborative, where

the doctor and patient make the decision together, and passive, where the patient leaves the

decision up to the doctor. (7, 8) Patients with cancer have varied preferences for involvement

in decision-making about treatment, with most preferring to play a collaborative role. (9) This

preferred role can change over time, (10-14) mostly to a more active role, (11, 13-15) and can

be influenced by the decision-setting. (10, 13, 16) For example, patients have been observed to

prefer less involvement in treatment decisions at times of progression of their cancer, (10) and

opt for more involvement when decisions concern end of life care. (13) Patients with early

breast or prostate cancer show a preference for a more active role when compared to those with

gastrointestinal or lung cancers. (16)

14

Whether patients are able to play the role in decision-making that they prefer was evaluated in

a systematic review by Tariman et al (16) of 22 studies evaluating the preferred and perceived

decision-making roles of patients with cancer. In all studies there was a discrepancy between

preferred and perceived roles (rates of concordance between 42 and 72%), with most finding

patients wanted more involvement in decision-making than had occurred. Most studies have

demonstrated improved decision-making outcomes when patients play their preferred role, (5,

17-19) though in one study of patients with early breast cancer, those who played a more active

role, even if more active than desired, had greater decision satisfaction, and less decisional

conflict. (17)

2.2.4 Considering expected survival time

Oncologists are frequently asked to provide patients with information about their prognosis.

(20) When given, this information is most often communicated at or close to the time of

decision-making about treatment at either the time of diagnosis or during an initial consultation

with an oncologist. (21, 22) Prognostic information is desired by most patients with cancer,

(21, 23) and can modify decisions about treatment. (24, 25) In the setting of decisions about

adjuvant chemotherapy for an early stage cancer, sufficient anticipated life expectancy is

required to realise the absolute benefits in recurrence-free and overall survival. Here, expected

survival time in the absence of a diagnosis of malignancy becomes important. In the setting of

consideration of palliative chemotherapy for incurable cancer, understanding of expected

survival time facilitates informed decision-making, allows for the setting goals, and the making

of plans regarding future end-of-life care. Here, the main driver of expected survival time is

the presence of advanced, incurable cancer.

15

Information about prognosis can be either qualitative, that is, whether or not the cancer is able

to be cured, or quantitative, that is, the duration of time a person is expected to live. Studies of

patients’ preferences for prognostic information have been done mainly in the setting of

advanced, incurable cancer, where most patients have preferred to receive information from

their oncologist about their expected survival time but not all have received it. (21, 26-28) For

example, Butow et al surveyed 148 Australian patients with breast cancer and melanoma.

Whilst 57% of patients wanted information about their expected survival time, only 27%

recalled having discussed this with their oncologist. (26) Schofield et al surveyed 131

Australian patients with melanoma of varied stage, and found 61% of patients had wanted

information about expected survival time at diagnosis, but only 28% recalled having received

it. (28) Surveys of oncologists (29) and recordings of clinic consultations (30) have shown

oncologists frequently provide qualitative statements about cure, but rarely a quantitative

estimate of survival time.

Sharing of information about expected survival time can modify decisions about treatment.

(24, 25) Weeks et al studied prognostic understanding and treatment preferences of 917

hospitalised patients with advanced lung and colon cancers in the United States. Patients who

thought they were going to live at least 6 months were more likely to favour life extending

therapy over comfort care than those who thought there was at least a 10% chance they would

not live beyond 6 months. Those who preferred life-extending therapy were more likely to be

readmitted to hospital, have an attempted resuscitation, or death while receiving ventilatory

assistance. (24) In a study of 332 outpatients with varied advanced cancers, Wright et al found

that patients who had had discussions about prognosis with their doctors were more likely to

prefer medical treatment with the aim of relieving pain and discomfort, and were less likely to

receive aggressive interventions close to the end of life. (25) In the setting of early stage

16

cancers, factors associated with reduced life expectancy in the absence of malignancy, such as

advanced age and comorbidity, are associated with reduced likelihood of receipt of adjuvant

chemotherapy, (31, 32) though the effect of patient understanding of expected survival time on

treatment decisions has not been similarly evaluated.

Patients prefer to receive information about their prognosis that matches their information

preferences, (33) offers hope, honesty and realism. (34, 35) The work of Kiely and Stockler

provides guidance for oncologists to communicate expected survival time in a format that

meets these expectations while acknowledging the unpredictable nature of survival and the

inaccuracy of a single point estimate of survival time (for example, a median survival time).

(36-41) A method of estimating and communicating survival time in the format of

individualised best-case, typical, and worst-case scenarios, based on an oncologist’s estimate

of expected survival time, (37, 41) or based on the median survival of similar patients from a

clinical trial, (39, 42, 43) has been found to be accurate in a range of cancer types. In a survey

of 505 Australian adults with a cancer diagnosis, Kiely et al found that, compared with a single

estimate of median survival time, most patients preferred the format of the three scenarios,

finding it offered more reassurance and hope. (38)

The following section addresses considerations in decision-making about chemotherapy

unique to the older adult with cancer.

17

2.3 Decision-making considerations unique to the older adult

2.3.1 Who are “older adults” with cancer?

There is no prescriptive definition of an “older adult” with cancer, however the most widely

used approach in the cancer setting to date has been based on chronological age. Limits of age

≥65 years or ≥70 years have been used most frequently, however are recognised as arbitrary

and influenced by geographic location, tumour type, and clinician bias. (44) Such age limits

change over time due to increased population longevity, advances in healthcare, and

emergence of less toxic cancer treatments and better supportive care. An example is provided

in the setting of haematologic malignancies requiring stem cell transplant. The boundary for

upper age limit has changed over the past four decades from adults ≥40 years considered “old”,

to more recently adults ≥65 years considered as such. (45)

Older adults defined by their chronological age are heterogeneous with regard to their

physiology and overall health. (46) Most adults with increased health needs related to aging

will be captured using a chronological age-based definition, however those who have a

physiological age that is much younger, or those chronologically younger patients who have

advanced physiologic age and similar complex health needs may be overlooked using such

definitions. International guidelines on the care of the older adult with cancer, such as those

published by the National Comprehensive Cancer Network (NCCN), (47) do not specify

chronologic age based criteria for considering the “older adult”, but rather advocate a

comprehensive assessment of all aspects of an adult’s health to identify areas of need and

vulnerability.

Terminology in this area has evolved. “Older adult” is the term now most widely adopted,

moving away from more pejorative terms such as “aged”, “ageing”, and “elderly”. Despite the

18

limitations of age limits to define “older adults” with cancer, for consistency with pivotal prior

studies in older adults receiving chemotherapy, (48, 49) the term “older adult” used in this

thesis refers to adults aged ≥65 years.

2.3.2 Limited evidence about older adults to guide oncologists

Oncologists use evidence from clinical trials to guide decisions about cancer treatment. Clinical

trials of cancer treatments frequently exclude patients based on chronological age. Additional

exclusion criteria such as impaired end-organ function, a history of prior malignancies, or

significant comorbidities, act disproportionately to exclude the participation of older adults.

For example, the pivotal MOSAIC trial of adjuvant chemotherapy with 5-fluorouracil and

oxaliplatin (FOLFOX) for early-stage colon cancer excluded patients aged >75 years. (50) In

an evaluation of eligibility for three key lung cancer clinical trials in 199 patients seen at two

Sydney cancer centres, up to two-fifths of patients did not meet eligibility criteria based on

comorbidity and performance status, (51) both of which increased with increasing age. In a

recent analysis of data from the Food and Drug Administration in the United States, (52) rates

of enrolment in clinical trials of 224,766 adult cancer patients were determined by age group

(<65 years, 65-69 years, 70-74 years, 75-79 years, and ≥80 years). The majority of clinical trial

participants (60%) were aged <65 years, and whilst adults aged ≥75 years accounted for 29%

of new cancer cases, they made up only 12% of patients enrolled in trials. This under-

representation of older adults in clinical trials means lack of relevant evidence for oncologists

to use to help guide treatment decisions for their older patients in everyday clinical practice.

19

2.3.3 Physiology and pharmacology

Changes in physiology and increasing comorbidity that occurs with aging impacts the

pharmacokinetics and pharmacodynamics of anti-cancer drugs in older adults. (53-55) The

presence of polypharmacy increases the potential for drug interactions. (56) Increasing age is

associated with decreased gastrointestinal blood flow and production of digestive enzymes,

and reduced gastrointestinal motility; decreased hepatic mass and levels of cytochrome p450;

increased total body fat; and decreased renal mass and renal blood flow. (54, 57, 58) These

changes may affect drug absorption, distribution, metabolism, and clearance. For example, an

increase in the AUC and decrease in the clearance of paclitaxel given at 175mg/m2 every 3

weeks, with corresponding increase in grade 3 neutropenia, was seen with increasing age in a

study of 153 adults (aged 53 to 86 years) receiving the drug for mixed cancer types. (59) There

may be age related differences in the effects of a chemotherapy drug (pharmacodynamics) in

the absence of observed differences in pharmocokinetics. This is often related to the presence

of comorbidity, and reduced bone marrow reserve. For example, in a study of temazolamide in

445 adults (aged 18 to 82 years), there were no age related differences in pharmacokinetics of

temazolamide, however increasing age was associated with myelosuppression. (60) In a

prospective study of the use of continuous ECG monitoring in patients receiving 5-fluorouracil,

cardiac toxicity was observed to be greater in those with a history of cardiac disease compared

to those without a history of cardiac disease. (61) Oncologists need to be mindful of potential

age or comorbidity-related effects on pharmacokinetics and pharmacodynamics when making

recommendations about chemotherapy.

2.3.3 Other health problems and frailty

Older adults with cancer frequently have other health problems. (62) Comorbidity, functional

and sensory deficits, impairments in mood and cognition, poor social supports, polypharmacy,

20

and geriatric syndromes such as frailty, falls, and incontinence are frequently present in older

adults with cancer. (62-64) For example, Extermann and Hurria (62) identified between 50 to

75% of older adults with cancer to have dependency in at least one instrumental activity of

daily living, 25 to 50% to have impairment on cognitive screening, 20 to 50% to have positive

screening for depression, and between 30 to 50% to have a geriatric syndrome. These other

health problems may act as competing causes of mortality (particularly in the setting of early

stage cancers), (65, 66) and impact tolerability of chemotherapy. (67)

2.3.5 Chemotherapy tolerance

Prospective observational studies in older adults with varied solid organ cancers have found

approximately half will experience a severe chemotherapy-related toxicity (grade 3 to 5) over

their course of treatment. (49, 68) Treatment toxicity has been identified as one of the most

frequently cited challenges in caring for older adults with cancer by their oncologists. (69)

Whilst severe toxicities usually lead to unplanned hospitalisation, interruption or early

discontinuation of treatment, (or even death), lower grade toxicities in older adults can also

have significant impact on treatment delivery. Kalsi et al observed (70) the chemotherapy

course of 108 adults aged ≥65 years with mainly gastrointestinal or gynaecological cancers. Of

the 56% (n=60) of patients who had a treatment modification due to toxicity, 35% (21 of 60)

had no greater than grade 2 toxicity.

The following section provides an overview of geriatric oncology, and background to the use

of the geriatric assessment in oncology to inform and guide treatment decisions.

21

2.4 The field of geriatric oncology

Geriatric oncology is a field that brings together clinicians who share interest, motivation, and

expertise in the care of older adults with cancer. The field is multi-disciplinary, made up of

medical oncologists, radiation oncologists, palliative care physicians, geriatricians, allied

health care professionals and nursing staff, and has grown in response to the aging population

worldwide and recognition of the needs of older adults with cancer. Growth in geriatric

oncology has been evident over the last decade, with development of National Comprehensive

Cancer Network (NCCN) and European Society for Medical Oncology (ESMO) guidelines for

the management of older adults with cancer, (47, 71) formation of the International Society of

Geriatric Oncology (SIOG) and its journal, the Journal of Geriatric Oncology, and visibility of

the specialty through international conferences and dedicated issues in high impact oncology

journals.

Geriatric oncologists have adopted a geriatric medicine approach to the care of older adults

with cancer. This is a multidisciplinary approach with a focus on the individual beyond that of

the cancer and the recognition of other health problems, including comorbidity and existing

functional impairments, and frailty. The benefits of a ‘geriatric medicine approach’ to address

the needs of older adults is evident in other specialities, namely orthopaedic and vascular

surgery, where a shared approach to care between geriatrician and surgeon has improved the

outcomes for older inpatients. (72-74)

2.4.1 The geriatric assessment

The core tool of a ‘geriatric medicine approach’ is a geriatric assessment. A geriatric

assessment (GA) is a multidimensional assessment of an older adult’s health which identifies

medical, social and functional needs, and develops a care plan to meet those needs. (75) It has

22

been a key component of the interaction between geriatric medicine and other specialties.

Health domains evaluated include functional status, comorbidity, polypharmacy, psychological

health, cognition, social supports, and nutrition. A GA may also include the presence or

absence of geriatric syndromes, such as falls. In its traditional form, as a Comprehensive

Geriatric Assessment (CGA), a multidisciplinary health care team led by a geriatrician, and

involving allied health professionals such as a physiotherapist, speech pathologist, dietitian,

and occupational therapists, perform the assessment. For each health domain, different clinical

assessment tools may be used. For example, assessment of cognition may involve initial

screening using a Mini Mental Status Examination, (76) which if abnormal may lead to more

detailed cognitive assessment. Functional assessment may involve a number of indices, such

as the Katz Index of Activities of Daily Living, (77) and may require in-home assessment by

an occupational therapist. Management recommendations are then made based on recognised

health problems and needs, rendering the CGA a form of intervention as well as evaluation. A

systematic review and meta-analysis of orthogeriatric co-care models for older inpatients with

hip fracture that utilised the CGA in this way demonstrated significant reductions in in-hospital

mortality, long term mortality, and length of hospital stay. (73) More recently, the adoption of

such an approach for older inpatients undergoing vascular surgery has shown shorter length of

hospital stay, lower complications, and less chance of discharge with dependency. (74)

2.4.2 The geriatric assessment in oncology

The use of the GA in oncology to date has largely been:

a. to better describe the heterogeneity of older adults with cancer

b. to identify other health problems that may benefit from supportive interventions

c. to identify older adults who may be at risk for poor outcomes, for example early mortality

or treatment-related toxicity, and

23

d. to inform decisions about cancer treatment

The time and resources required to undertake a complete CGA have led to variations of the

CGA being used in oncology, with up to 16 different terms for related assessments noted in

one systemic review by Puts et al. (64) This has also led to a focus on geriatric assessments as

descriptive and screening tools, rather than as a comprehensive evaluation with intervention,

as is the CGA. Frequently encountered terminology for such abbreviated assessments include

the following:

Geriatric Assessment

Geriatric assessment (GA) has been an umbrella term to refer to an assessment of all ‘geriatric’

health domains, and is generally used for descriptive purposes and not linked to a management

plan. Where used, the term is often qualified by a description of the health domains assessed.

The International Society for Geriatric Oncology (SIOG) published consensus guidelines on

the use of the GA in oncology in 2014, outlining the health domains to be included: functional

status, comorbidity, cognition, mental health status, fatigue, social supports, nutrition, and

presence of geriatric syndromes. (78)

Abbreviated Geriatric Assessment

The abbreviated geriatric assessment (aGA) is a shorter evaluation of an older adult’s health

and may be used as a screening assessment to identify patients who would most benefit from a

complete CGA. (79) A varied number of health domains are assessed, often with the use of

screening assessments or clinical tools for each health domain that can be performed by a single

clinician. Component assessments or tools used have varied across studies, and are often

screening tools for that domain used for descriptive purposes. (64)

24

Abbreviated Geriatric Assessment, or Geriatric Assessment with management

An aGA or GA may be paired with interventions, usually referral to another clinician or health

care provider with relevant expertise for further evaluation. This is distinctly different from the

comprehensive management interventions and care plans that form part of a CGA.

Geriatric Screening Tools

A geriatric screening tool is an instrument that is used to identify older adults who might be

vulnerable to a particular adverse outcome, or who might benefit from more comprehensive

assessment with a CGA. These tools are quick to perform and may be undertaken as a self-

completed screening survey or with a clinician. An example is the Vulnerable Elders’ Survey

(VES-13), (80) a function-based questionnaire that can be used as a screening tool to detect

older adults with cancer who are likely to benefit from a CGA. (81) An abbreviated geriatric

assessment (aGA) may also be thought of as a screening tool if performed in a two-stage

process that leads to CGA or is used to predict outcome.

Variations in methodology

Whilst a CGA adopts a multidisciplinary approach, the above-mentioned adaptations of the

CGA in oncology have varied with respect to how they have been undertaken, in what study

population, and by whom. (64, 82) Completion of assessments face-to-face with a clinician or

trained researcher, as well as use of patient-completed surveys or a combination of these

approaches have all been reported. The clinical tools or measures chosen as component parts

of a geriatric assessment have varied across studies and are often dependent on ease of use,

clinician familiarity, and time to perform. (78) Study populations in which the geriatric

assessment has been applied in oncology have also varied. This is important because the

frequency with which problems or deficits are detected on a geriatric assessment when applied

25

in a population of older all-comers with cancer, will differ from what is found when the same

assessment is applied to older adults with cancer who, for example, have been referred for and

are receiving treatment.

The NCCN Guidelines for Older Adults with Cancer recommends all older adults with cancer

undergo an assessment of all health domains in the form of a geriatric assessment, (47) but is

not prescriptive on the component parts or format of the assessment. Recently, the evident

dilution of the CGA in the field of geriatric oncology has been recognised, and a case made for

reinstating its role as an evaluation with intervention, particularly in light of strengthening the

evidence for its use. (83) Certainly in its abbreviated forms, the benefits seen in surgical

applications of the GA have not yet been shown in oncology.

The geriatric assessment in Australia

The frequency of use of the geriatric assessment by Australian oncologists is an identified area

for enquiry. Prior to the inception and design of this thesis, there was no data on the frequency

with which Australian oncologists utilised a geriatric assessment or its components in routine

clinical practice. The Geriatric Oncology interest group within the Clinical Oncology Society

of Australia (COSA) since surveyed 69 Australian oncologists and found that only 19% had

ever used a geriatric screening tool, and just over half (56%) had ever referred an older patient

for a geriatric assessment, though the majority (71%) perceived value in such an assessment.

(84)

2.4.3 The geriatric assessment to inform treatment decisions

Evidence for the use of the GA in oncology continues to evolve, and can be conceptualised in

four broad areas:

26

a. Describing the population and identifying problems

b. Predicting outcomes

c. Influence on treatment decision-making

d. As an intervention to improve patient outcomes

Several systematic reviews have evaluated the role of the GA in the evaluation of the older

adult with cancer in one or more of these areas. (62, 64, 82, 85-92) Their scope and results are

summarised in Table 1.

Describing the population

Early reviews by Extermann et al (62, 85) were pivotal in describing the potential application

of the GA in oncology based on its success in other disciplines, particularly for recognising

health problems and vulnerability in older adults not easily appreciable on usual clinical

assessment. Since then, the growth in the number of studies in older adults with cancer utilising

a CGA or GA has been exponential. Two separate systematic reviews by Puts et al (64, 82)

describe the feasibility and properties of the GA utilised in older adults with cancer. The earlier

of these reviews (64) included 73 studies that most frequently used a GA to evaluate patients’

health and functional status. Identified domains covered by the GA were: functional status by

basic and instrumental activities of daily living (ADLs), comorbidity, cognition, depression,

nutrition, performance status, and falls, and the median time to complete assessments was

between 10 to 45 minutes. Variations in assessment measures and cut-off values used did not

allow for pooling of data to inform the overall incidence of impairments across studies,

however the review provided evidence that a GA could detect abnormalities in health domains

in cancer populations heterogeneous for cancer type, treatment planned (or received), and

timing of assessment. Extermann and Hurria summarised the health profiles of older adults

with cancer as described using a GA in 7 cohorts across the US, Canada, and Italy. (62) Cohorts

27

again varied according to cancer type, treatment planned (or received), and the timing of

assessment. Although the majority of older adults with cancer were of good performance status

(ECOG-PS of 0 or 1), more than half required assistance for at least one instrumental activity

of daily living; between 20 and 50% had an abnormal result on screening for cognitive

impairment; and between 20 and 40% had an abnormal result on screening for depression. (62)

Handforth et al (93) evaluated the prevalence of frailty in a systematic review including 20

observational studies or trials in older adults with cancer of any type. Frailty was defined here

as having impairments on at least 3 health domains on GA, or by meeting the criteria of an

established model of frailty. A median of 42% of patients across studies were frail (range 6%–

86%).

Fewer studies have evaluated whether the information obtained by GA in older adults with

cancer is new to the treating oncologist, or over and above what a usual clinical assessment

would reveal. (63, 84) A large cohort study by Kenis et al (63) of 1967 adults aged ≥70 years

with varied cancers gave results of a GA performed at baseline back to treating oncologists,

who were then asked if the assessment revealed any new information about their patient. For

931 of 1820 patients (51.2%), the treating oncologist reported that the GA had revealed

previously unknown geriatric problems, mostly relating to impaired functional status and

nutrition. To et al (84) surveyed 69 Australian oncologists about the use of the GA in clinical

practice, and found most (71%) agreed that a GA would add to their clinical assessment, though

prospective evaluation in the local setting is lacking.

Predicting outcomes

There have been a plethora of studies evaluating the GA, or its component parts, for

associations with outcomes such as mortality, chemotherapy-related toxicity, and unplanned

28

hospitalisation in older adults with cancer. Study heterogeneity has been a recognised limitation

preventing unified conclusions relevant to inform clinical practice. (91, 92, 94)

The most recent evaluation of the value of the GA in predicting patient outcomes was a 2019

systematic review by Bruijnen et al (94) that included 46 studies, mostly prospective in design,

addressing associations between the health domains of a GA and the outcomes of mortality,

post-operative complications, and chemotherapy-related toxicity in older adults with any

cancer type, of any stage, and in any treatment setting. The domains most consistently

associated with all-cause mortality and chemotherapy-related toxicity across studies were

physical function and nutritional status; and with post-operative complications was physical

function. Physical function was most commonly assessed by ADLs and/or IADLs.

Interestingly, each of the eight included GA health domains (functional status, nutritional

status, cognition, mood, physical function, fatigue, social supports, and falls) were associated

with at least one outcome of interest in at least one study. Overall the strength and nature of

the observed associations across the included studies varied considerably, as did the health

domains included on the GA, the assessment measures and cut-off values used to define each

health domain, and patient populations in terms of tumour type, treatment received, and stage

of disease.

An earlier systematic review by Versteeg et al (92) limited their evaluation of the GA in older

adults with cancer to 13 studies where patients were receiving treatment with chemotherapy.

Six of the included studies tested for associations with chemotherapy-related toxicity, and five

tested for associations with mortality. The review revealed no consistent predictors of

chemotherapy-related toxicity across studies, however poor nutritional status was recognised

as a consistent predictor of mortality.

29

Influence on treatment decision-making

Two systematic reviews by Hamaker et al (86, 87) have evaluated the influence of the GA on

treatment decision-making. The most recent of these included 35 studies on the impact of a

geriatric evaluation (geriatric consultation or GA) on the oncologic treatment plan; non-

oncologic interventions; and treatment-related outcomes. (87) Characteristics of patients for

whom the treatment decision was altered (either to less or more intensive treatment) were not

described. Treatment choice before and after geriatric evaluation was evaluated in 11 studies,

with the treatment plan changing for a median of 28% (range 8 to 54%) of patients, most to

less intensive treatment. Implementation of non-oncologic interventions was evaluated in 19

studies, with one or more interventions recommended based on GA in a median of 72% of

patients (range 26 to 100%). The methodology varied between included studies with regard to

the baseline management plan used as a comparator for patients, raising uncertainty as to the

added value of the GA over and above usual clinical assessment and recommendation by a

treating oncologist. For example, baseline management plans based on standard treatment

guidelines or MDT discussion prior to review by an oncologist might over-estimate the impact

of the GA on management decisions. The included study by Kenis et al (63) that used the

oncologist’s proposed treatment as the baseline management had lower rates of change in

cancer treatment (25%) and non-oncologic interventions (26%).

Impact on patient outcomes

A small number of randomised controlled trials (95-97) have evaluated the impact of the GA

on outcomes for older adults with cancer receiving chemotherapy. Puts et al conducted a

randomised, single-blinded phase II trial of the feasibility and effect on quality of life of

geriatric assessment with pre-defined, specified intervention in 61 adults aged ≥70 years

starting chemotherapy for stage II to IV gastrointestinal, genitourinary, or breast cancer. Pre-

30

defined interventions consisted mainly of referral to allied health or geriatric medicine services

for further evaluation and care where there were deficits detected on GA. A quality of life

benefit that approached clinical significance (10 point difference considered significant on

QLQ-30) was seen at 3 months for the intervention arm in the subgroup of patients who

survived at least 6 months (difference in QLQ-30 of 9.28; 95% CI 10.35 to 28.91). (97)

Magnuson et al evaluated the effect of a geriatric assessment with intervention on the incidence

of grade 3 to 5 chemotherapy-related toxicity in 71 patients aged ≥70 years starting

chemotherapy for a stage III or IV solid organ cancer. Again, interventions were pre-defined,

developed through a consensus approach, and consisted mainly of referral to allied health or

geriatric medicine services, or advice to reduce falls. The incidence of toxicity did not differ

between the intervention and control arms (61% versus 57%, p = 0.74), nor did the rates of

hospitalisation, dose delays, treatment termination, or hospice enrolment. (98) Interestingly,

treating oncologists implemented only 35.4% of the 409 interventions recommended following

geriatric assessment.

In a different approach, Corre et al compared outcomes in 494 patients aged ≥70 years with

advanced non-small-cell lung cancer assigned to a chemotherapy regimen based on age and

performance status (akin to usual care), with those assigned to a chemotherapy regimen based

on geriatric assessment (intervention). The primary end point was treatment failure free

survival (TFFS), which did not differ between the usual care and intervention groups (3.2

months versus 3.1 months, HR 0.91, 95% CI 0.76 to 1.1). Patients in the intervention group

experienced significantly less all grade toxicity (85.6% v 93.4%, respectively P = .015),

however this included 23% of patients in the intervention (GA) arm who were allocated to

receive no chemotherapy. (96)

31

Table 1. Systematic reviews on the use and value of the geriatric assessment in older adults in oncology

Author Outcomes of interest Characteristics of studies Studies

(n)

Findings

Van

Abbema et

al 2019

(91)

i. Grade 3 to 5

chemotherapy toxicity;

ii. Unplanned

hospitalization

iii. Chemotherapy

discontinuation, and

dose reduction;

iv. Functional decline

v. Mortality

Retrospective and prospective

studies reporting

on factors associated with

chemotherapy intolerance in

patients ≥65y; patient-related

factors: age, gender, social,

frailty, hearing, depression,

cognition, falls, mobility,

nutrition, ADLs, IADLs, self-

rated health, PS, polypharmacy,

diastolic BP, comorbidity; tumor-

related factors: tumor type,

regimen; search Jan ’96 to July

’16.

27 - Heterogeneity of study design, study populations, and chemotherapy

outcomes assessed; predictors of outcomes inconsistent across studies.

- Associated with chemotherapy toxicity: older age (3 of 9 studies), cognition (2

of 6 studies), mobility (2 of 4 studies), comorbidity (3 of 9 studies), tumour type

and regimen (5 of 13 studies), polychemotherapy (3 of 3 studies), hearing

impairment (1 of 1 studies), diastolic BP (1 of 1 studies)

- Associated with hospitalisation: male (1 of 3 studies); polychemotherapy (1 of

3 studies); depressive symptoms, cognition (1 of 1 studies)

- Associated with functional decline: depression, IADL (1 of 1 studies).

- Associated with dose reduction: none identified in 6 studies

- Associated with discontinuation: frailty (1 of 2 studies), falls (1 of 2 studies),

mobility (1 of 2 studies), ADLs (2 of 4 studies), PS (4 of 6 studies), tumour type

and regimen (6 of 9 studies), comorbidity (5 of 11 studies)

- Associated with chemotherapy mortality: PS (1 of 2 studies), comorbidity (1 of

2 studies), metastatic disease (2 of 2 studies), frailty (1 of 1 studies), mobility

and nutrition (1 of 1 studies).

Bruijnen et

al

2019 (94)

i. Mortality;

ii. Post-operative

complications;

iii. Systemic treatment-

related outcomes of

toxicity, completion of

planned treatment, and

dose modifications

Longitudinal, observational,

interventional, or retrospective

studies; English or Dutch;

addressing association between

any GA domain and any of the

outcomes of interest in pts ≥65y

with any cancer of any stage;

search Sep ‘06-July ‘17

46 - 8 GA domains identified across studies: functional status, nutritional status,

cognition, mood, physical function, fatigue, social supports, falls.

- Measures to assess each GA domain varied.

- Associations between GA domains and outcomes varied considerably.

- Each GA domain was in at least 1 study associated with an outcome of interest.

- Associated with mortality: physical function (5 of 8 studies), nutritional status

(13 of 23 studies).

- Associated with systemic treatment-related outcomes: physical function (4 of 4

studies), nutritional status (8 of 14 studies).

- Associated with post-op complications: physical function (3 of 4 studies).

Hamaker

et al

2018 (87)

i. Alteration of

oncologic treatment

plan;

Cohort studies on the effect of a

GE in pts with any cancer on

outcomes of interest; no limits on

35 - Overall quality of studies was good

- Oncologic treatment choice before and after GE (11 studies): treatment

changed after GE in median of 28% of pts (8–54%), most to less intense

32

ii. Implementation

of non-oncologic

interventions;

iii. Effect of GE on

treatment-related

outcomes

age or language; GE was either

geriatric consultation or a GA

evaluating at least 3 of: cognition,

mood, nutrition, ADLs, IADLs,

comorbidity, polypharmacy,

mobility/falls, or frailty; search to

Dec ‘17

- Non-oncologic interventions (19 studies): one or more interventions in a

median of 72% of pts (26–100%), most social, nutrition, polypharmacy.

- Effect of GE on outcomes (13 studies): 5 of 9 studies found a positive effect of

GE on treatment-related toxicity or complications; 3 of 4 studies found higher

treatment completion rates after GE; 2 of 7 studies found lower mortality in

those undergoing GE.

Hamaker

et al

2014 (86)

i. Alteration of

oncologic treatment

plan;

ii. Implementation

of non-oncologic

interventions

Cohort studies on the effect of GE

in pts with any cancer on

outcomes of interest; no limits on

age or language; GE was either

geriatric consultation or a GA

evaluating at least 3 of: cognition,

mood, nutrition, ADLs, IADLs,

comorbidity, polypharmacy,

mobility/falls, or frailty; search

Jan ‘13

10 - Oncologic treatment choice before and after GE (6 studies): treatment changed

after GE in a median of 39% of pts after GA; 2/3 to less intense.

- Non-oncologic interventions (6 studies): one or more interventions in a median

of 70% of pts

Handforth

et al

2014 (93)

i. Prevalence of

frailty;

ii. Treatment-related

side-effects;

iii. Unplanned

hospitalization;

iv. Mortality

Observational studies or trials

reporting prevalence and/

or outcomes of frailty (using

established frailty model or by

GA with at least 3 domains) in

older pts (mean age ≥70y) with

any stage or type of cancer;

search Jan ‘96-June ‘13.

20 - Median number of GA domains assessed was 7 (range 3–9)

- GA domains included functional status, cognition, mobility, nutrition, mood,

fatigue, polypharmacy, social support, comorbidity.

- Thresholds used to define frailty varied across studies.

- Endpoints for treatment related complications varied across studies.

- Prevalence of frailty: median of 42% of pts frail (range 6%–86%) and 32% of

pts fit (range 11-78%).

- Consistency of associations of frailty with outcomes lacking across studies.

- A small number of studies (3 or less for each) indicated that frailty and pre-

frailty may be associated with all-cause mortality, postoperative mortality,

chemotherapy intolerance, and postoperative complications.

Puts et al

2014 (82)

An update

to Puts et

i. Feasibility of GA;

ii. Patient assessment;

iii. Impact on treatment

decisions;

iv. Mortality;

Cross sectional, longitudinal,

interventional, observational

studies in pts ≥65y, any cancer,

any stage, addressing feasibility

of GA, or effectiveness of GA for

34 - Quality of included studies moderate to good.

- Impact of GA on treatment decisions: meta-analysis showed treatment

decisions modified by GA in 23.2% of cases.

33

al 2012

review

v. Complications and

toxicity

predicting outcomes of interest, or

changing treatment decision; in

English/French/Dutch/ German;

Nov ‘10 to Aug ‘12

- Meta-analysis of data on value of GA in predicting treatment complications,

toxicity, and mortality not possible due to heterogeneity of GA instruments and

outcome measures used across studies.

- ADLs, IADLs, performance status, depression, frailty all found in at least one

study to be associated with complications of treatment or mortality

Versteeg et

al

2014 (92)

i. Treatment toxicity;

ii. Mortality;

iii. Impact on treatment

decision-making

RCTs, observational studies in pts

≥65y with any solid cancer

receiving chemotherapy where

GA was performed to evaluate

predictors of toxicity, mortality,

impact on treatment decisions;

search Jan ‘07 to Feb ‘13

13 - Quality of studies not reported.

- GA revealed geriatric problems in >50% of patients across studies.

- Predictors of toxicity (6 studies): none consistent across studies.

- Associated with mortality: nutritional status (5 of 5 studies).

- Impact on treatment decision (5 studies): treatment decisions modified by GA

for 21 to 53% of patients, mostly to a less intensive treatment.

Ramjaun

et al

2013 (90)

i. Mortality;

ii. Chemotherapy

toxicity;

iii. Postoperative

complications

Any publication in English or

French where GA assessed for

association with outcomes of

interest in pts ≥65y initiating

cancer treatment for non-

metastatic cancer; GA had to

include nutrition, cognition,

function, polypharmacy,

comorbidities, syndromes; search

May ‘97 to May ‘12

9 - 7 studies reported on mortality; 3 studies reported on chemotherapy toxicity; 1

study reported on post-operative complications

- Associated with mortality: all 6 GA domains found in at least one study to be

associated with mortality; strongest associations observed for nutritional status

(HR 1.84–2.54), depression (HR 1.51–1.81), functional status (HR 1.04–1.33).

- Associated with chemotherapy-related toxicity: functional status (OR 1.71–

2.47), impaired hearing (OR 1.67)

- Associated with post-operative complications: comorbidity (OR 5.62)

Hamaker

et al

2012 (88)

i. Mortality;

ii. Chemotherapy

toxicity;

iii. Chemotherapy

completion;

iv Perioperative

complications;

v. Radiotherapy

tolerance

Cohort studies investigating the

association between baseline GA

and outcomes of interest in pts

with cancer (any type or stage);

GA had to assess ≥2 of: cognition,

function, mood, nutrition, ADLs,

IADLs, comorbidity,

polypharmacy, mobility/falls,

frailty; search to Feb ‘12

37 - Quality of included studies heterogeneous, mostly poor

- Assessment and outcomes measures inconsistent between studies

- Associated with all-cause mortality: frailty (9 of 10 studies), nutrition (4 of 4

studies), comorbidity (by CIRS-G) (4 of 5 studies)

- Associated with chemotherapy toxicity: frailty (2 of 3 studies)

- Associated with chemotherapy completion: cognitive impairment (2 of 3

studies), ADLs (2 of 3 studies)

- Associated with perioperative complications: IADLs (3 of 4 studies)

Puts et al

2012 (64)

i. Feasibility of GA;

ii. Patient assessment;

Cross sectional, longitudinal,

interventional, or observational

73 - Quality of included studies poor to moderate.

34

iii. Impact on treatment

decisions;

iv. Mortality;

v. Complications and

toxicity of treatment

studies in pts ≥65y, any cancer, in

English/ French/Dutch/German,

addressing feasibility of GA, or

effectiveness of GA in predicting

outcomes, or impact on treatment

decisions; Jan ‘96 to Nov ‘10

- Domains covered by GA: ADL, IADL, comorbidity, cognition, depression,

nutrition, performance status, falls.

- Feasibility: GA took 10 to 45 minutes to complete.

- Most studies used the GA to describe patients’ health and function.

- At least one component of the GA was associated with treatment toxicity in 6

of 9 studies; with mortality in 8 of 16 studies; impacted treatment decision in 2

of 4 studies.

- IADLs, comorbidity, and depression most often associated with treatment

toxicity and mortality, though results conflicting across studies.

Abbreviations: GA- geriatric assessment; GE- geriatric evaluation; CIRS-G- Cumulative Illness Rating Scale in Geriatrics; BP- blood pressure; PS- performance status; ADL- activities of

daily living; IADL- instrumental activities of daily living; RCT- randomised controlled trial

35

2.4.4 Studies in this thesis

The studies of this thesis that have used a geriatric assessment have addressed some of the

identified areas for further research. Chapter 7 evaluates the impact of the GA on oncologists’

decision-making about chemotherapy for their older patients, and its perceived value to

oncologists in the evaluation of their older patients. Predictors from a GA for severe

chemotherapy-related toxicity and for observed survival time are explored in Chapter 6 and

Chapter 8 respectively. The survey presented in Chapter 5 provides new information on the use

of the geriatric assessment by Australian oncologists.

The following section provides a background to the approach of older adults and their

oncologists to decision-making about chemotherapy.

36

2.5 The approach of older adults and their oncologists to

decision-making about chemotherapy

2.5.1 Older adults’ approach to decision-making about chemotherapy

Preferred role in decision-making

Older adults are often assumed to prefer a more passive role in decision-making in the absence

of supporting data. Only two small studies performed a decade apart in the United States have

evaluated older adults’ preferred roles in decision-making about chemotherapy. (99, 100) Elkin

et al (99) determined the preferred role in decision-making of 73 patients with metastatic

colorectal cancer aged ≥70 years. Preferred roles were active in 25%, collaborative in 23%,

and passive in 52%. More recently, a mixed methods study by Puts et al (100) in 32 adults aged

≥70 years with varied advanced cancers reported preferred roles in decision-making to be

active in 41%, collaborative in 36%, and passive in 7%. Whether older adults play their

preferred role has not been evaluated.

Factors influencing decisions about chemotherapy

Reasons older adults accept or decline cancer treatment were described in a systematic review

of 38 studies by Puts et al. (101) The most consistent reason for older adults to accept (or

decline) chemotherapy was their doctor’s recommendation. Other commonly identified reasons

for treatment acceptance included convenience, expectations about side effects, treatment

success rates, wanting to treat the cancer, and the experience of significant others. Commonly

identified reasons for treatment refusal included fear of side effects, unclear benefits of

treatment, negative experiences of significant others, concern about comorbidity, and

prioritising quality of life. (102) Of note, reasons for acceptance of treatment were often the

same as the reasons for decline of treatment. For example, negative experiences of significant

37

others who had been through similar treatments can sway patients away from treatment,

whereas positive experiences of significant others can sway patients towards treatment. Also,

the benefits of treatment may be cited as a reason for acceptance of treatment, implying that

for the patient they outweigh possible harms, or be cited as a reason for decline of treatment,

implying that the benefits were not large enough to make treatment worthwhile.

Older adults may be less willing to accept harms of treatment. (103) Yellen et al used a series

of hypothetical scenarios to determine age differences in the treatment preferences of 244

patients with cancer. While older adults (≥65 years) were just as likely as younger adults (<65

years) to accept chemotherapy, younger adults were willing to accept treatment of greater

toxicity for a smaller survival advantage in both early and advanced cancer scenarios than were

older adults. (103) Blinman et al determined the preferences for adjuvant chemotherapy of 123

patients of all ages (median age 65 years) with colon cancer, where older age was associated

with needing a larger survival benefit to make adjuvant chemotherapy worthwhile. (104) Fu et

al determined the preferences for palliative chemotherapy of 107 patients of all ages (median

age 57 years) with colorectal cancer, where older age was associated with a reduced willingness

to tolerate adverse events affecting quality of life, such as depression, fatigue, and pain. (105)

Information needs

Patients with cancer vary in their desire for information about their diagnosis, treatment, and

prognosis. Where desired, patients do not always receive this information from their oncologist,

(29, 99) and when discussed, this information is not always recalled by patients. (106) For

example, most patients with advanced cancer desire information about their expected

prognosis, (21, 23) however oncologists do not always discuss this. Daugherty et al surveyed

729 medical oncologists in the United States about communication of prognosis to their

38

patients with advanced, incurable cancer. They found that whilst 98% said their usual practice

is to inform such patients that they will die from their cancer, only 43% said they always or

usually provide an estimated survival time. (29) Weeks et al evaluated prognostic

understanding of 1193 adults with advanced, incurable cancers. Only 31% of patients with

metastatic lung cancer and 19% of patients with metastatic colorectal cancer understood that

palliative chemotherapy was “not at all likely” to cure their cancer. (107) In their study on

decision-making role preferences, Elkin et al also evaluated the information needs of 73 older

adults making a decision about palliative chemotherapy for advanced colorectal cancer. Whilst

44% of patients wanted information about their expected survival time, only 25% reported

receiving such information. (99) Jansen et al evaluated age-related differences in recall of

information provided during initial consultations with oncologists by 260 patients with varied

cancers. (106) Controlling for the amount of information presented, recall of information about

diagnosis, prognosis, and treatment declined with increasing age. Older adults were observed

to ask fewer questions, and have shorter consultations.

2.5.2 Oncologists’ approach to decision-making about chemotherapy for their

older patient

Assessment of the older patient

For all patients with cancer, regardless of age, oncologists rely on their clinical assessment in

order to make an initial recommendation about treatment with chemotherapy based on

available evidence applicable to the patient in front of them. This ‘usual’ clinical assessment

involves gathering of information about tumour type and stage, taking a medical history, and

performing a clinical examination. It is unclear whether oncologists routinely seek to find out

more from their older patients, particularly whether they make an evaluation of each of the

domains of health outlined in a geriatric assessment.

39

Few studies have sought to evaluate the utilisation of the GA and its component measures in

Australia. (84, 108) To et al (84) surveyed 69 Australian oncologists about their use of the

geriatric assessment, 19% reported routinely using geriatric assessment tools, and 56% had

referred an older patient for a geriatrician-led geriatric assessment, with access and timeliness

identified as key barriers to their use. Lakhanpal et al (108) performed a retrospective chart

review of 304 patients aged ≥70 years attending an outpatient oncology clinic or presented to

a multidisciplinary tumour meeting across six Australian cancer centres. Whilst comorbidities

were assessed in 92% of initial consultations, assessment of at least one activity of daily living

occurred in only 50% of patients, and only one patient had all basic and instrumental activities

of daily living assessed and documented.

Factors influencing chemotherapy recommendations

Factors influencing oncologists’ chemotherapy recommendations for their older patients have

been evaluated using three main approaches:

a. Oncologist surveys, with or without hypothetical patient scenarios

b. Observational studies

c. Retrospective chart reviews identifying predictors of treatment

Surveys of oncologists using hypothetical patient scenarios, mainly in the setting of early stage

breast cancer, have been performed to understand patient factors that influence oncologists’

recommendations about chemotherapy. (109-117) One or more patient factors are varied

between scenarios in order to evaluate for alterations in treatment recommendation, usually

across a spectrum from more intensive to less intensive or no treatment. Using these methods,

patient age, (110-117) comorbidity, (111, 113-116) and patient preference (117, 118) have been

found to influence treatment recommendations, though studies have not been inclusive of all

40

patient and treatment factors. Survey studies have also asked oncologists to nominate or rate

the factors that most often influence their treatment recommendations for older adults in a

variety of settings. (69, 84, 110, 119, 120) Using this approach, patient performance status or

physical function has been most often identified or highly rated.

Observational studies in older adults receiving treatment for breast cancer (121, 122) have

incorporated questionnaires for treating oncologists addressing reasons for treatment choice.

For example, Ring (122) observed treatment patterns for 803 women aged ≥70 years with early

breast cancer. In those with high-risk disease (n=309), 30% were recommended chemotherapy,

and 17% received it. The most commonly cited reasons for this were use of endocrine therapy

alone as being more appropriate, small absolute benefits of chemotherapy, and patient

comorbidity and frailty. Freyer et al (121) observed treatment patterns for 1009 women aged

≥65 years with metastatic breast cancer in France. There was a significant difference in receipt

of chemotherapy between age groups (31% in those aged ≥75 years v 68% in those aged 65 to

74 years, p<0.001), with the most common reason cited by treating physicians for this observed

pattern being the “subjective evaluation of general health status”.

Retrospective chart reviews have consistently identified patient age and comorbidity as

predictors of receipt of chemotherapy. (123-127) Such studies have mainly been done in the

setting of early stage colon cancer. (123-125) For example, Jorgensen et al (125) evaluated

chemotherapy recommendations for 580 patients with node positive colon cancer and 498

patients with high risk rectal cancer across New South Wales, Australia. Increasing patient age

was associated with reduced likelihood of receiving chemotherapy in both groups (p<0.001 for

colon cancer, p=0.003 for rectal cancer) even after adjusting for documented comorbidity and

41

performance status. The main limitation of this approach is the scope of data captured in the

medical record.

2.5.3 Studies in this thesis

Chapter 5 is a survey of Australian oncologists evaluating how they assess older adults prior to

making a recommendation about chemotherapy and what factors they consider most important

in making a recommendation. Hypothetical patient scenarios are also used to evaluate the

impact of age and risk of treatment toxicity on the likelihood of recommending chemotherapy

in both the adjuvant and palliative settings.

The following section provides a background to predicting chemotherapy toxicity in older

adults.

42

2.6 Predicting chemotherapy toxicity in older adults

2.6.1 The rationale for predicting chemotherapy toxicity

Chemotherapy toxicities are the major harm to be considered by older adults and oncologists

when making a decision about treatment. Other harms include the inconvenience of treatment,

travel, time away from home, and associated procedures such as venepuncture, cannulation,

regular scans, and clinic visits.

The narrow therapeutic index and highly variable pharmacokinetics of most cytotoxic

chemotherapies lead to frequent toxicity. Chemotherapy toxicities can be broadly categorised

as haematological or non-haematological, considered as dose-dependent or dose-independent,

and are graded according to their severity (Table 2). Haematological toxicities are those that

primarily affect blood counts and are often dose-dependent. Non-haematological toxicities

include chemotherapy-related adverse events affecting any organ system. Some toxicities are

common across classes of cytotoxic drugs, such as gastrointestinal toxicities (nausea, vomiting,

diarrhoea) and fatigue, whereas others may be class specific, for example taxane-related

peripheral neuropathy, or drug specific, for example capecitabine-related palmar plantar

erythrodysaesthesia.

When chemotherapy toxicity occurs, delays to subsequent cycles of treatment or dose

reductions according to local guidelines are considered on an individual patient basis and

according to treatment intent, and are usually made in the setting of severe toxicity (≥ grade 3).

Having a means by which to better predict the occurrence of chemotherapy toxicity,

particularly severe toxicity, may allow for anticipatory intervention to modify that risk or

support patients through toxicity; avoid undertreatment resulting from empirical dose

reductions (as is often employed in older adults) for patients who were likely to tolerate

43

treatment; and facilitates informed decision-making for patients through a realistic appreciation

of the likelihood of harms.

Table 2. Grading of chemotherapy-related toxicities using the Common

Terminology Criteria for Adverse Events (CTCAE) (128)

Grade Description

Grade 1 Mild; asymptomatic or mild symptoms; clinical or diagnostic

observations only; intervention not indicated.

Grade 2 Moderate; minimal, local or noninvasive intervention indicated; limiting age-appropriate

instrumental activities of daily living.

Grade 3 Severe or medically significant but not immediately life-threatening; hospitalization or

prolongation of hospitalization indicated; disabling; limiting self care activities of daily living.

Grade 4 Life-threatening consequences; urgent intervention indicated.

Grade 5 Death related to adverse event.

2.6.2 Predicting chemotherapy toxicity in older adults

Oncologists predict the likelihood of chemotherapy toxicity for an individual patient by a

combination of clinical judgement and data about the rates of specific toxicities from clinical

trials. Prospective studies evaluating predictors of chemotherapy toxicity in older adults with

solid organ cancers are summarised in Table 3. Studies included 24 to 660 patients, mostly

receiving a range of chemotherapy regimens for varied cancer types. Most studies have used

grade ≥3 chemotherapy-related toxicity as the primary endpoint. Notably, only one study used

patient reported outcomes as a measure of treatment tolerance. The number of health domains

included in study geriatric assessments varied, ranging from one to eight. Consistent predictors

for chemotherapy toxicity across these studies are notably lacking, though varied measures of

function or performance status were most frequently identified as associated with toxicity.

44

Table 3. Prospective studies evaluating predictors of chemotherapy toxicity in older adults with solid organ cancer

Study N Design Population Median

age

(range)

Outcome GA domains

evaluated

Predictors for primary outcome

Aaldricks et

al, 2016

(129)

494 PO ≥70 years

Chemotherapy

Cancer

Any type or stage

75

(70-92)

Completion of <4

cycles of

chemotherapy

Nutrition

Cognition

Frailty

3 component items of the MNA:

-psychological distress or recent acute illness

p=0.002, HR 2.10 (95%CI 1.31-3.38)

->3 prescription meds

p=0.002, HR 1.96 (95%CI 1.27-3.03)

-neuropsychological problems

p=0.004, HR 3.44 (95%CI 1.50-7.90)

Alibhai et al,

2016 (130)

46 PO ≥65 years

Docetaxel

Metastatic

castrate resistent

prostate cancer

75 mean

(SD 5.4)

Grade ≥3 toxicity Frailty Nil identified

Note: rates of toxicity by CARG Score risk group (low risk 0%,

intermediate risk 17%, high risk 27%) and VES-13 vulnerability

score (vulnerable 22%, not vulnerable 17%) not significantly

different (p=0.65 and p=0.71 respectively)

Hsu et al,

2015 (131)

24 PO ≥70 years

Chemotherapy

Lung or colorectal

cancer

Any stage

74.5

(70-84)

Grade ≥3 toxicity PS

Mobility/falls

Grip strength

Nil identified

Von

Minckwitz

et al,

2015 (132)

391 P RCT ≥65 years

AC or CMF

versus nab-

paclitaxel +

capecitabine

Early breast

cancer

72

(65-84)

Treatment

discontinuation,

Grade ≥3 toxicity

PS

Function

Comorbidity

Nutrition

Frailty

Medications

For treatment discontinuation:

Treatment arm (AC/CMF v nPX)

-p<0.001, OR 9.56 (95%CI 4.45-20.5)

For treatment toxicity:

Treatment arm (AC/CMF v nPX)

-p<0.001, OR 0.154 (95%CI 0.078-0.305)

Hamaker et

al,

2014 (133)

78 P RCT ≥65 years

Pegyated

liposomal

doxorubicin v

capecitabine

Metastatic breast

cancer

75.5

(65.8-

86.8)

Grade 3/4 toxicity PS

Function

Comorbidity

Nutrition

Cognition

Mood

Frailty

Polypharmacy

-p=0.001, OR 6.38 (95%CI 1.99-23.47)

45

Laurent et

al, 2014

(134)

385 PO ≥70 years

Chemotherapy 1st

line

Solid organ

cancer

Any type or stage

78.9

(SD 5.4)

Completion of

planned cycles

PS

Function

Comorbidity

Nutrition

Cognition

Mood

Social

Mobility/falls

-Good performance status (ECOG-PS <2)

p<0.0001, OR 4.0 (95%CI 1.87-8.7)

-Independence in ADLs (ADLs >5)

p=0.01, OR 3.01 (95%CI 1.28-7.09)

-No difficulty mobilising

p=0.01, OR 2.7 (95%CI 1.25-5.85)

-Absence of falls risk

p=0.007, OR 2.85 (95%CI 1.33-6.08)

-Creatinine clearance

p=0.04, OR 1.3 (95%CI 1.01-1.70)

Wildes et al,

2013 (135)

65 PO ≥65 years

Chemotherapy

Colorectal, lung,

breast or

lymphoma

Any stage

73

(65-89)

Grade 3/4 toxicity,

completion of

planned cycles

PS

Function

Comorbidity

Nutrition

Cognition

Mood

Social

Mobility/falls

For completion of planned cycles:

-Curative intent treatment

p=0.03, OR 4.97 (95%CI 1.21-18.81)

-ECOG-PS 2 or 3

p=0.008, OR 0.089 (95%CI 0.015-0.53)

-Renal function

p=0.04, OR 1.03 (95%CI 1.00-1.06)

For grade 3 or 4 non-haematological toxicity:

-Moderate to severe comorbidity

p=0.007, OR 6.13 (95%CI 1.65-22.74)

Aparicio et

al,

2013 (136)

123 P RCT ≥75 years,

enrolled on trial

comparing 5FU

with 5FU plus

irinotecan 1st line

for mCRC

(substudy)

80

(75-91)

Grade 3 or 4

toxicity, unplanned

hospitalisation

PS

Function

Comorbidity

Nutrition

Cognition

For grade 3/4 toxicity:

-Irinotecan

p=0.006, OR 5.03 (95%CI 1.61-15.77)

-MMSE ≤27/30

p=0.019, OR 3.84 (95%CI 1.24-11.84)

-Impairment in IADLs

p=0.011, OR 4.67 (95%CI 1.42-15.32)

For unplanned hospitalisation

-MMSE ≤27/30

p=NR, OR 4.57 (95%CI NR)

-GDS ≤2

p=NR, OR 5.52 (95%CI NR)

Falandry et

al,

2013 (137)

60 P RCT >70 years

Pegylated

liposomal

doxorubicin for

77

(71-89)

Grade ≥3 toxicity Function

Comorbidity

Nutrition

Cognition

Mood

For grade 3/4 non-haematological toxicity:

-Age ≥80 years

p=0.022, OR 3.32 (95%CI 0.99-11.20)

46

metastatic breast

cancer

Social

Frailty

Medications

Hoppe et al,

2013 (138)

364 PO ≥70 years

Varied cancers

Starting 1st line

chemotherapy

77.4 (70-

93)

Functional decline

after cycle 1 (≥0.5

point reduction on

ADL scale)

Function

Nutrition

Cognition

Mood

Mobility

-high GDS (OR 2.16, 95%CI 1.09-4.3, p=0.03)

-low IADL (OR 2.87, 95%CI 1.06-7.79, p=0.04)

Extermann

et al, 2012

(68)

518 PO ≥70 years

Chemotherapy

Solid organ

cancer

Any type or stage

75.5

(70-92)

Grade ≥4

haematological

toxicity; grade ≥3

non-haematological

toxicity

PS

Function

Comorbidity

Nutrition

Cognition

Mood

Included in predictive model for haematologic toxicity:

-diastolic BP, IADL, LDH, chemo-tox score

Included in predictive model for non-haem toxicity:

-ECOG-PS, MMS, MNA, chemo-tox score

Soubeyran et

al,

2012 (139)

348 PO >70 years

Chemotherapy

Cancer

Any type or stage

77.5

(70-99.4)

Death within 6

months of

chemotherapy

PS

Function

Comorbidity

Nutrition

Cognition

Mood

-Male sex

p=0.013, OR 2.40 (95%CI 1.20-4.82)

-Advanced stage

p=0.003, OR 3.9 (95%CI 1.59-9.73)

-Poor nutrition (by MNA)

p=0.013, OR 2.77 (95%CI 1.24-6.18)

-Get Up and Go >20 seconds

p=0.006, OR 2.55 (95%CI 1.32-4.94)

Shin et al,

2012 (140)

64 PO ≥65 years

Chemotherapy

Solid organ

cancer

Any type or stage

71

(65-80)

Febrile (>38.5 °C)

neutropenia, grade

4 neutropenia,

discontinuation due

to grade ≥3 toxicity,

or hospitalisation

>7days for grade ≥3

toxicity

PS

Function

Comorbidity

Nutrition

Cognition

Mood

Frailty

-ECOG PS ≥2

p=0.037, OR 38.52 (95%CI 1.25-1191.97)

Hurria et al,

2011 (49)

500 PO ≥65 years

Chemotherapy

Solid organ

cancer

Any type or stage

73

(65-91)

Grade ≥3 toxicity PS

Function

Comorbidity

Nutrition

Cognition

Mood

Social

Mobility/falls

Included in predictive model for grade 3-5 toxicity (associated

with toxicity):

-age ≥72y, GI or GU cancer, standard dose regimen,

polychemotherapy, Hb <11g/dL (male) or <10g/dL (female),

creatinine clearance <34ml/min, fair or poor hearing, falls,

assistance for medications, limited in walking one block,

reduced social activity due to health

47

Puts et al,

2011 (141)

112 PO ≥65 years

Chemotherapy

Any cancer

Any type or stage

74

(65-92)

Grade ≥3 toxicity

occurrence at 3

months and at 6

months

Function

Nutrition

Cognition

Mood

Grip strength

-Grip strength impaired

p=0.018, OR 3.5 (95%CI 1.2-9.9)

Clough-Gorr

et al, 2010

(142)

660 PO ≥65 years

Stage I to IIIA

breast cancer

≥65

(median

NR)

Treatment tolerance

by self-report

Mood

Social

-Lower 5-item Mental Health Index scores (65.73 v 81.48) in

poorly tolerated group

p=0.0002

-Lower Medical Outcomes Study Social Support Survey scores

(64.02 v 76.64) in poorly tolerated group

p=0.0004

Marinello et

al,

2009 (143)

110 PO ≥70 years

Chemotherapy

Lung/colon/breast

cancer

Any type or stage

75.1

(70-87)

Combined

endpoint: death

(any cause),

treatment

interruption, or

Grade ≥3 toxicity

PS

Function

Cognition

-Advanced stage of cancer

p=NR, OR 2.44 (95%CI 0.99-5.99)

-Chemotherapy regimen

p=NR, OR 1.82 (95%CI 1.06-3.14)

-Comorbidity (CIRS score >6)

p=NR, OR 3.68 (95%CI 1.47-9.20)

-Karnofsky performance status

p=NR, OR 0.47 (95%CI 0.24-0.94)

Freyer et al,

2005 (144)

83 PO >70 years

Carboplatin/

Cyclophosphamid

e

Advanced ovarian

cancer

76

(70-90)

Severe toxicity

defined as: grade 4

neutropenia, febrile

neutropenia,

discontinuation due

to grade ¾ toxicity

or rehospitalisation

>7days for grade ¾

toxicity

PS

Function

Comorbidity

Nutrition

Cognition

Mood

Medications

-Symptoms of depression

p=0.006, OR not reported

-Dependence

p=0.048, OR not reported

-ECOG-PS ≥2

p=0.026, OR not reported

Abbreviations: PO- prospective observational; P RCT- prospective randomised controlled trial; OR- odds ratios; HR- hazard ratio; MNA- Mini Nutritional Assessment;

CARG- Cancer and Aging Research Group; PS- performance status; AC- adriamycin and cyclophosphamide; CMF- carboplatin, methotrexate, fluorouracil; nPX-

nabpaclitaxel; ECOG-PS- Eastern Cooperative Oncology Group Performance Status; ADL- activities of daily living; 5-FU- 5-fluorouracil; MMSE- mini mental status

examination; IADL- instrumental activities of daily living; GDS- geriatric depression scale; NR- not reported; BP- blood pressure; LDH- lactate dehydrogenase; MMS-

mini mental state; GI- gastrointestinal; GU- genitourinary; CIRS- cumulative illness rating scale

48

2.6.3 Predictive models for chemotherapy toxicity in older adults

The two developed models for predicting chemotherapy toxicity in older adults with varied

cancers are the Cancer and Aging Research Group’s (CARG) Toxicity Score, (49) and the

Chemotherapy Risk Assessment Scale for High-Age Patients (CRASH). (68) Both were

developed by identifying predictors of chemotherapy toxicity in older adults from baseline

patient, tumour, and treatment details, which included a GA. In doing so, the CRASH (68) and

CARG Scores (49) provided proof of concept that information gained from a GA could be

incorporated into a predictive model for treatment outcomes.

The Chemotherapy Risk Assessment Scale for High-Age Patients (CRASH) (68)

The Chemotherapy Risk Assessment Scale for High-Age Patients (CRASH) (68) uses

treatment and patient-related factors to estimate the likelihood of severe chemotherapy-related

toxicity. It was developed and internally validated in a cohort of 518 adults aged 70 years and

older who were commencing chemotherapy for a solid organ cancer of any type or stage. In

contrast to the CARG Score, two separate predictive models were developed, one for severe

(Grade 4) haematological and one for severe (Grade ≥3) non-haematological treatment toxicity.

The rationale for the split model was the asymptomatic nature of most haematological

toxicities, with the exception of febrile neutropenia. (Table 4) Rates of severe chemotherapy

toxicity increased across risk score groups (Table 5), although rates of combined

haematological and non-haematological toxicity were high (50%) even in the low risk group.

The implementation of the CRASH predictive model in day to day clinical practice is

challenging as it requires a complete Mini-Mental Status examination, (76) and Mini

Nutritional Assessment, (145) and a serum lactate dehydrogenase which is not always tested

routinely in patients with non-haematological solid organ cancers. The model also incorporates

49

a Chemotox score for each chemotherapy regimen that is not widely available and was

developed mainly as a research tool. (146)

Table 4. Chemotherapy Risk Assessment for High-Age Patients (CRASH) Score

(68)

Points

Haematologic Score 0 1 2

Diastolic blood pressure ≤72mmHg >72mmHg

IADLs 26-29 10-25

LDH 0-459 >459

Chemotox 0-0.44 0.45-0.57 >0.57

Non-haematologic Score

ECOG-PS 0 1 or 2 3 or 4

MMS 30 <30

MNA 28-30 <28

Chemotox 0-0.44 0.45-0.57 >0.57

Abbreviations: BP, blood pressure; Chemotox, toxicity score of the chemotherapy regimen using previously

described calculator (146); ECOG PS, Eastern Cooperative Oncology Group performance status; IADLs,

Instrumental Activities of Daily Living; LDH, lactate dehydrogenase; MMS, Mini Mental Health Status;

MNA, Mini Nutritional Assessment; ULN, upper limit of normal.

NOTE: For the combined CRASH score, add the points from the hematologic and nonhematologic score,

counting Chemotox only once

Table 5. Rates of severe chemotherapy toxicity according to CRASH Score (68)

Haem subscore Nonhaem subscore Combined score

Derivation

cohort

(n=347)

Points % with toxicity Points % with toxicity Points % with toxicity

0-1 7% 0-2 33% 0-3 50%

2-3 23% 3-4 46% 4-6 58%

4-5 54% 5-6 67% 7-9 77%

>5 100% >6 93% >9 79%

The Cancer and Aging Research Group’s (CARG) Toxicity Score (49)

The Cancer and Aging Research Group’s (CARG) Toxicity Score was developed by Hurria et

al (49) in a prospective study of 500 patients aged 65 years or older who were starting

chemotherapy for a solid organ malignancy of any type or stage at 7 institutions in the United

50

States. Prior to chemotherapy, all patients underwent a geriatric assessment that had been

developed previously and shown to be feasible in the oncology setting. (147) Variables from

this geriatric assessment, along with patient, tumour, and treatment characteristics, were tested

as predictors for the primary outcome, the occurrence of any severe (grade ≥3) chemotherapy-

related toxicity over the course of planned treatment. Variables that were most predictive of

this outcome, or otherwise clinically relevant, were included in the predictive model and

assigned a weighted score. The final model includes 11-items that are summed to give the

CARG Score (Table 6).

Table 6. Predictive model for calculation of the CARG Toxicity Score in 500 older

adults commencing chemotherapy (49)

Prevalence G3-5 Toxicity

Risk Factor n (%) n (%) OR (95%CI) Score*

Age ≥72 years 270 (54) 163 (60) 1.85 (1.22 – 2.82) 2

Cancer type GI or GU 185 (37) 120 (65) 2.13 (1.39 – 3.24) 2

Standard dose chemotherapy 380 (76) 204 (54) 2.13 (1.29 – 3.52) 2

More than one drug 351 (70) 192 (55) 1.69 (1.08 – 2.65) 2

Haemoglobin <11g/dL (male),

<10g/dL (female) 62 (12) 46 (74) 2.31 (1.15 – 4.64) 3

Creatinine clearance (Jellife, ideal

weight) <34 mL/min 44 (9) 34 (77) 2.46 (1.11 – 5.44) 3

Hearing fair or poor 123 (25) 76 (62) 1.67 (1.04 – 2.69) 2

1 or more falls in last 6 months 91 (18) 61 (67) 2.47 (1.43 – 4.27) 3

IADL: Medications taken with at

least some assistance 39 (8) 28 (72) 1.50 (0.66 – 3.38) 1

MOS: Walking one block at least

somewhat limited 109 (22) 69 (63) 1.71 (1.02 – 2.86) 2

MOS: Social activity limited at

least sometimes due to health 218 (44) 126 (58) 1.36 (0.90 – 2.06) 1

* Scores are summed to give the CARG Toxicity Score (range 0 to 23)

Abbrevations: GU, genitourinary; IADL, instrumental activities of daily living; MOS, Medical Outcomes

Study; OR, odds ratio.

Using the CARG Score, patients can be classified as low (score 0 to 5), intermediate (score 6

to 9), or high risk (score ≥10) of severe chemotherapy-related toxicity (Table 7), with rates of

severe toxicity in each risk-score group in this derivation study being 30%, 52%, and 83%

51

respectively. The area under the Receiver Operating Characteristic curve for the score in this

derivation study was 0.72 (95%CI 0.68-0.77), equating to moderate discrimination for the

occurrence of severe chemotherapy toxicity.

Table 7. Predictive ability of the CARG Toxicity Score (49)

No toxicity Toxicity

Risk Group* N (%) N (%) Total P ROC†

Score 0 to 5 89 (70) 39 (30) 128 <0.001 0.72

Score 6 to 9 110 (48) 117 (52) 227

Score 10 to 19 19 (17) 90 (83) 109

* Risk groups based on distribution of scores in the derivation cohort of N=500, where the range of scores was

0 to 19, and the cohort was divided into categories based on the risk of grade 3 to 5 toxicity.

† For calculation of the area under the ROC curve, the CARG Toxicity Score was treated as continuous.

The evidence for the CARG Score has evolved since the inception of this thesis and design and

completion of the studies presented in Chapter 6 and Chapter 7. At the time of thesis inception,

the CARG Score had not been evaluated in a similar external cohort of older adults with mixed

cancer types. It had only been tested by Nie et al (148) in a cohort of 120 older adults receiving

chemotherapy for lung cancer. Rates of toxicity varied significantly according to CARG Score

risk groups, with rates of toxicity in low, intermediate, and high risk groups of 9%, 40%, and

60% respectfully (p-value <0.001). A lack of external validation made the applicability of the

CARG Score to older adults in the Australian setting unclear, and to establish its place in

clinical practice a comparison of the CARG score to the usual judgement of oncologists was

included. A lack of prospective capture of lower grade toxicities in the CARG study was a

limitation recognised by the authors, (49) with lower grade toxicities often having significant

impact and being a reason for treatment discontinuation in older adults. (70)

Following the design and commencement of the projects comprising Chapter 6 and Chapter 7,

further evidence for the CARG Score emerged. The developing authors performed an external

52

validation study in a cohort of 250 patients with cancers of varied type and stage (matching the

inclusion criteria of the original study). (48) The validation study was performed at 8 centres

in the United States, 6 of which had participated in the development study. Discrimination of

the score for the occurrence of severe chemotherapy toxicity was less than in the original study

(AU-ROC curve of 0.65) though not significantly different (AU-ROC curve of 0.72, 95% CI

0.68 to 0.77, p-value for difference in AU-ROC between studies = 0.09). Differences in rates

of toxicity between intermediate and high-risk groups was also less marked (rates of severe

toxicity in low, intermediate, and high-risk groups of 37%, 62%, and 70% respectively, p-value

<0.001).

Alibhai et al (130) tested the CARG Score in a cohort of 46 older men receiving docetaxel for

metastatic castration resistant prostate cancer. Rates of severe toxicity seen in this study were

low and the study was limited by sample size, though an increase in rates across CARG Score

risk groups were observed (rates of severe toxicity in low, intermediate, and high-risk groups

of 0%, 17%, and 27% respectively) but this was not statistically significant (p=0.65). This study

also evaluated the predictive value of oncologists’ estimates of the risk of severe toxicity (on a

10-point scale, 1=low to 10=high) finding it to be low (OR 1.04, 95%CI 0.71-1.52, p=0.83),

however a limitation of this approach was the calibration of the ordinal scale between

oncologists: what numerical probability of toxicity equates to “low” for one oncologist might

be different for the next.

2.6.4 The impact of predictive models for toxicity on decision-making about

chemotherapy

Determining the impact of a predictive model on clinical decision-making usually follows its

external validation. Because external validation of predictive models for chemotherapy toxicity

53

in older adults was lacking at the time of thesis design, there were no studies available

evaluating the impact of these predictive models on decision-making about treatment. With the

growth in interest of the CARG Score over the last few years, Nishijima et al (149) recently

took the approach of determining the value of the CARG Score in decision-making by reporting

the agreement between treatment decision based on clinical impression and based on the CARG

Score, with the assumption that patients with a CARG Score ≥10 (high-risk) should be

recommended reduced intensity chemotherapy. Patients who were high-risk by CARG Score

but received standard intensity chemotherapy had significantly higher rates of severe toxicity

(88% v 40%, p=0.006) meaning there was potential for the CARG Score to modify

chemotherapy prescribing decisions, at least for high-risk patients.

2.6.5 Studies in this thesis

Evaluation of the clinical value of the CARG Score compared with oncologists’ estimates of

the risk of toxicity in an external population of older adults with mixed cancers in the local

setting is the focus of Chapter 6 of this thesis. A geriatric assessment is also used here to

identify other predictors of chemotherapy toxicity. Exploration of oncologists’ opinions on the

usefulness and impact of the CARG Score (and geriatric assessment) on chemotherapy

prescribing is the focus of Chapter 7 of this thesis. The impact of anticipated rates of severe

chemotherapy toxicity on the likelihood to recommend chemotherapy is evaluated in the survey

of oncologists presented in Chapter 4.

54

2.7 Summary

This chapter provided the relevant background on decision-making about chemotherapy for

older adults with cancer. Key concepts in decision-making in oncology were outlined,

including the need to consider anticipated benefits and harms, requirements for shared

decision-making, patient preferences for involvement in decision-making, and the importance

of consideration and communication of expected survival time. Challenges unique to decision-

making about chemotherapy in older adults were summarised, and the current evidence for use

of the geriatric assessment to inform treatment decisions in oncology was evaluated. The

approach of older adults and their oncologists to decision-making about chemotherapy was

described, with an emphasis on factors influencing decision-making for both parties. A

rationale for predicting chemotherapy toxicity in older adults was provided, with an emphasis

on how this might lead to changes in treatment decisions and outcomes for patients.

The next chapter complements this background chapter, as a published narrative review on

systemic treatment considerations for older adults with colorectal cancer.

55

3. Methodological considerations

3.1 Overview

The specific methodology of the research projects of this thesis is reported in each chapter

(Chapters 5, 6, 7, 8, and 9). This chapter serves to address some key considerations that apply

to these chapters in more detail.

56

3.2 Design and use of the Geriatric Assessment in this thesis

The abbreviated geriatric assessment (GA) referred to in Chapters 6, 7, and 8 was designed to

cover all major geriatric health domains using instruments that were easily performed together

with the patient by a non-geriatrician within the time constraints of an outpatient clinic. The

included instruments had previously been used in an oncology setting (64) and the health

domains covered were consistent with international oncology guidelines for the assessment of

older adults with cancer. (47) The GA was completed together with patients by a trained study

researcher or clinician-researcher on a single occasion, and prior to the commencement of

planned chemotherapy.

The instruments of the abbreviated GA are outlined in Table 1 and described below.

Table 1. Abbreviated Geriatric Assessment

Health Domain Assessment Tool(s)

Performance status Eastern Cooperative Oncology Group (ECOG) Performance Status

Karnofsky Performance Status (KPS)

Functional status Katz Index of Activities of Daily Living (ADLs)

Older Americans Resource and Services (OARS) Multidimensional Functional

Assessment Questionnaire

Instrumental Activities of Daily Living (IADLs)

Medical Outcomes Study (MOS) Physical Functioning

History of falls in the preceding 6 months

Timed Up and Go (TUG)

Comorbidities Cumulative Illness Rating Scale in Geriatrics (CIRS-G)

Polypharmacy Medication count (prescription and non-prescription)

Nutrition Mini Nutritional Assessment Short Form (MNA-SF)

Social supports Medical Outcomes Study Social Support Survey, Tangible (SSS)

Mood 5-item Geriatric Depression Scale (GDS-5SF)

Cognition Short Blessed Orientation Memory Concentration Test (OMC)

Summary scores g-8 Vulnerability Score (g8)

Canadian Study on Health and Aging (CSHA) Clinical Frailty Scale

Geriatric Assessment Summary Score

Performance Status

The ECOG-Performance Status (150) and Karnofsky Performance Status (151) are widely used

measures of performance status in oncology. They are global measures of functional ability,

57

widely used as inclusion criterion in oncology clinical trials, and are associated with mortality

(152, 153) and ability to tolerate treatment. (153)

Timed Up and Go (TUG)

The Timed Up and Go (154) is a test that quantifies functional mobility, and is a modified,

timed version of the original Get Up and Go Test. (155) To complete the test, a trained observer

times how many seconds it takes for an individual to stand from an armchair, walk 3 metres,

turn, and walk back to the chair and sit down again. In older adults receiving chemotherapy, it

has been associated with increased risk of falls (>14 seconds), (156) early mortality (>20

seconds), (139) and functional decline (>20 seconds). (138)

Katz Index of Activities of Daily Living (ADLs)

The Index of Independence in Activities of Daily Living (77, 157) was developed as a measure

of physical functioning of older adults and people with chronic illness. It assesses independence

in six activities: bathing, dressing, toileting, transfers, continence and feeding. The number of

activities in which the subject is independent are counted, giving a score from 0 (dependent in

all activities) to 6 (independent in all activities).

The OARS Multidimensional Functional Assessment Questionnaire - IADLs

The Older Americans Resource and Services (OARS) Multidimensional Functional

Assessment Questionnaire was designed to assess overall personal functional status and service

use of adults. (158) Part A of the questionnaire is the Multidimensional Functional Assessment

Questionnaire (MFAQ) and has 5 parts. The subscale on instrumental activities of daily living

(IADLs) uses 7 questions related to telephone use, travel, shopping, meal preparation,

housework, medications, and finances, rated on a 3-point Likert scale. For each of the 7 items,

58

a score of either 0 (unable), 1 (some help), or 2 (independent) is given, which are then summed

to give a total score (range 0 to 14), with higher scores indicating less need for assistance.

The Medical Outcomes Study (MOS) Physical Functioning Measure

The MOS Physical Functioning Measure (159) assesses an extended ADL scale and was

chosen for inclusion in the GA because it is sensitive to changes at relatively high levels of

functioning, and hence useful in the setting of outpatients referred for chemotherapy. It includes

ten items on physical functioning, one on satisfaction with physical functioning, and three on

mobility. The ten items on physical functioning were included in the GA and ask patients to

what degree they are limited (1 = a lot, 2 = a little, or 3 = not at all) when performing a described

physical activity. Scores for the ten items are summed (range 10 to 30) with higher scores

indicating higher levels of functioning.

Cumulative Illness Rating Scale in Geriatrics (CIRS-G)

The Cumulative Illness Rating Scale (160) provides a comprehensive review of medical

problems and quantification of their severity, yielding a cumulative score and index. The scale

was revised as the Cumulative Illness Rating Scale for Geriatrics (CIRS-G) to reflect common

problems of the elderly. (161) To complete the scale, a score of 0 to 4 (no problem to extreme

problem) is given for each organ/body system category based on a history of the patient’s

comorbidities. A Severity Index (total score/number of categories) is then calculated, ranging

from 0 to 4. The CIRS-G was used as a comorbidity scale, and so a patient’s primary diagnosis

(for the purpose of the studies in this thesis, their diagnosis of cancer), was not tallied within

the scale. Direct patient history and a review of the electronic medical record were used to

capture comorbid conditions and complete the CIRS-G for studies reported in this thesis.

59

Short Blessed (Orientation Memory Concentration) Test (SBT)

The Short Blessed Test (162), a modification of the Blessed Information-Memory-

Concentration Test, (163) is a screening test for cognitive impairment completed in person by

a trained researcher/clinician. It uses simple tests of orientation, remote and recent memory,

and concentration to identify and estimate the likelihood of dementia. Points are allocated for

incorrect responses. Based on findings from the Memory and Aging Project, (164) a score of 0

to 4 indicates normal cognition, a score of 5 to 9 indicates possible cognitive impairment

(further diagnostic evaluation recommended), and a score of 10 or more is likely consistent

with dementia.

Geriatric Depression Scale 5-Item Short Form (GDS-5SF)

The Geriatric Depression Scale 5-Item Short Form (165) is an abbreviated version of the

Geriatric Depression Scale, (166) a screening test for depression in older adults. Patients are

asked to answer ‘yes’ or ‘no’ to 5 questions about their mood and activity. A score of ≥2 (range

0 to 5) indicates possible depression (97% sensitivity and 85% specificity for a diagnosis of

depression using clinical evaluation). (165)

Modified Medical Outcomes Study (MOS) Social Support Survey

The Medical Outcomes Study Social Support Survey is a self-administered tool to assess the

availability of four categories of social supports, developed for use in people with chronic

illness living in the community. (167) The four categories of social supports measured using

the survey are: emotional/informational, tangible, affectionate, and positive social interactions.

The Tangible and Emotional/Informational subscales only were selected for use in this thesis,

as has been done previously in studies of older adults receiving chemotherapy. (49, 147)

Participants are asked how often, on a 5-point Likert scale, they have someone available to help

60

them in a variety of scenarios. The sum of responses is calculated (range 4 to 20), with higher

scores indicating more tangible social support.

Mini Nutritional Assessment Short Form (MNA-SF)

The Mini-Nutritional Assessment (MNA) is a screening and assessment tool designed to

identify older adults at risk of malnutrition. (145, 168) The MNA-short-form (MNA-SF) was

developed and validated to allow a 2-step screening process. (169) The MNA-SF uses only 6

items, compared to the 18-item MNA, addressing weight loss, body-mass-index, and dietary

intake, and takes less than 5 minutes to perform. Scores range from 0 to 14. Scores ≥12 indicate

satisfactory nutritional status; scores ≤11 suggest risk for malnutrition; and scores ≤7 are

consistent with malnutrition. (169)

G8 Questionnaire (G8)

The G8 screening tool is an eight-item questionnaire developed for use in older cancer patients,

(170, 171) and takes less than 5 minutes to perform. Evidence for its use is mostly in identifying

vulnerable older adults who are likely to have abnormalities detected on subsequent GA (172)

though may be prognostic for survival (173, 174) and predictive for chemotherapy toxicity.

(175) A score of ≤14 is considered abnormal.

Canadian Study on Health and Aging (CSHA) Clinical Frailty Scale

The Canadian Study on Health and Aging (CSHA) Clinical Frailty Scale (176) is a single item

subjective assessment of frailty. The scale asks clinicians to classify patients into one of seven

categories ranging from ‘very fit’ through to ‘severely frail’ using clinical judgement and

guided by a description of general appearance, presence of comorbidity, and comparison to

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peers. This measure of frailty was chosen as it is a brief, subjective assessment that can be

performed by treating oncologists (and other healthcare providers) at the point of care.

Geriatric Assessment Summary Score

To allow for comparison between patients in terms of performance on the GA as a whole, and

to reflect the burden of deficits across all health domains, a summary score of the GA was used

as an additional descriptor in the included studies. The summary score of the GA (range 0 to

7) was reported together with the results of individual instruments of the GA. A point was

scored for a deficit in each geriatric health domain, using frequent or pragmatic cut-off values

as follows:

Performance status: ECOG-PS ≥ 2

Functional status: TUG >/= 14s, or any dependency in ADLs

Nutrition: MNA-SF ‘at risk’ or ‘malnourished’

Cognition: ‘at risk’ or ‘likely consistent with dementia’ (score ≥5)

Social supports: practical supports in lowest quartile (score <18)

Psychological state: GDS-5SF ≥ 2

Comorbidity: total score in highest quartile (CIRS G total score >6)

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3.3 Evaluating predictive tools to inform treatment decisions

Chapter 6 evaluates the predictive value of both the CARG Toxicity Score (49) and

oncologists’ estimates for the occurrence of severe chemotherapy-related toxicity. A

background to the statistical approach for evaluating their predictive value, conceptually by

considering both measures as akin to ‘diagnostic tests’ for the occurrence of toxicity (the

outcome of interest), is presented here. The steps required to move from derivation of a

predictive model to its clinical implementation are also described.

3.3.1 The outcomes of a ‘diagnostic’ test

The purpose of a diagnostic test is to predict with some acceptable degree of accuracy, the

likelihood of an outcome of interest (often a disease) being present in any one individual. When

used to predict an outcome occurring in the future, such a test can be considered a predictive

tool. The outcomes of a simple diagnostic test can be considered as:

Disease or condition

Test Result Present n Absent n Total

Positive True positive a False positive c a+c

Negative False negative b True negative d b+d

Total a+b c+d

Sensitivity is the probability of a test being positive when the outcome of interest is present

and is defined as 𝑎

𝑎+𝑏. Specificity is the probability of a test being negative when the outcome

of interest is not present and is defined as 𝑑

𝑐+𝑑. The positive predictive value of a test is the

probability of the outcome of interest being present when the test is positive. It is represented

as 𝑎

𝑎+𝑐. The negative predictive value of a test is the probability of the outcome of interest being

not present when the test is negative. It is represented as 𝑑

𝑏+𝑑.

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3.3.2 Cut-off values for diagnostic tests

The sensitivity and specificity of any diagnostic test is dependent on the chosen test cut-off

value. When the distributions of scores on a test are compared for those who have the outcome

of interest (for example, chemotherapy toxicity) and those who do not, the distribution of scores

often overlaps. Where there is such overlap, the chosen cut-off value for the test to discriminate

between the two populations will classify some subjects incorrectly, either as false positives or

false negatives. (Figure 1)

Figure 1. Distribution of test scores in two populations, those who have the outcome

of interest and those who do not. The chosen test cut-off value effects the proportion of

subjects in each population classified incorrectly by the test (false positive and false negative

fractions). TN, true negative fraction; FN false negative fraction, TP, true positive fraction; FP,

false positive fraction.

With the selection of a higher cut-off value, the true negative fraction will increase with

increased specificity, but the true positive fraction and sensitivity will decrease. With the

selection of a lower threshold value, the true positive fraction and sensitivity will increase, but

the true negative fraction and specificity will decrease.

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3.3.3 The receiver operating characteristic (ROC) curve

A receiver operating characteristic (ROC) curve plots the true positive rate (sensitivity) against

the false positive rate (1-specificity) for different test cut-off values (Figure 2). ROC curves

are one way of measuring ‘discrimination’, the ability of a test to discriminate between those

who have the outcome of interest (for example, chemotherapy toxicity) and those who do not.

Each point on the ROC curve represents a sensitivity / specificity pair corresponding to a single

decision threshold.

Figure 2. The receiver operating characteristic (ROC) Curve. A. A curve falling on a

line with a gradient of 1 represents a test that is no better at discriminating for an outcome than

chance (tossing a coin). B. Moderate discrimination. C. Good discrimination.

The area under the ROC curve, on a scale of 0 to 1, gives a measure of the discrimination of a

test. A test with perfect discrimination has an area under the ROC curve of 1.0 (Figure 3A). A

test with no better discrimination than chance has an area under the ROC curve of 0.5 (Figure

3B).

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A. B.

Figure 3. Area under the Receiver Operating Characteristic (ROC) Curve

A. An example of a test with perfect discrimination. B. An example of a non-discriminatory

test

3.3.4 Clinical application and the cost of error in predictive tools

The chosen cut-off value for any diagnostic test or predictive tool must consider the

consequences of error, that is, a false-positive or false-negative result, and so are tailored to

clinical context. The choice of cut-off value is a trade-off between sensitivity and specificity,

in other words, between the consequences of false positive and false negative results. Some

tests may have a chosen cut-off value that aims for a low rate of false negatives at the cost of

higher rates of false positives, because the clinical consequences of a missed diagnosis would

pose significant harms or miss an opportunity for an intervention that changes outcomes for

the better. Other tests may have a chosen cut-off value that aims for a low rate of false positives

at the cost of higher rates of false negatives, because the clinical consequences of a false

positive result pose more harm to the individual or cost to the population than missed

diagnoses.

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Chapter 6 evaluates the value of the CARG Toxicity Score for predicting the outcome of severe

chemotherapy toxicity in older adults over a course of planned treatment. Using the score,

which ranges from 0 to 23, patients are classified as low, intermediate, or high-risk of

experiencing severe chemotherapy toxicity. The potential consequences of falsely over-

estimating (false positive) and falsely under-estimating (false negative) the risk of severe

chemotherapy toxicity are considered in Table 2.

Table 2. Consequences of a prediction tool for chemotherapy toxicity in practice

Test result Interpretation Action Consequence

True positive Predicts severe

chemotherapy

toxicity and severe

chemotherapy

toxicity occurs

Measures to reduce

severe

chemotherapy

toxicity

Potential to avoid severe

chemotherapy toxicity

Potential reduced efficacy of reduced

intensity chemotherapy

Avoid hospitalisation and/or early

cessation of treatment

False positive Predicts severe

chemotherapy

toxicity and severe

chemotherapy

toxicity does not

occur

Measures to reduce

severe

chemotherapy

toxicity

Potential reduced efficacy of reduced

intensity chemotherapy

Does not receive chemotherapy when

otherwise may have

True negative Does not predict

severe chemotherapy

toxicity and severe

chemotherapy

toxicity does not

occur

No change to initial

chemotherapy

recommendation

Maintained efficacy of standard dose

chemotherapy

False negative Does not predict

severe chemotherapy

toxicity and severe

chemotherapy

toxicity occurs

No change to

initial

chemotherapy

recommendation

Severe chemotherapy toxicity

Hospitalisation and / or early

cessation of treatment

Adding to the complexity of implementation of a predictive tool in clinical practice is that

clinicians may respond to the results of the tool in different ways. The actions and consequences

prompted by a risk score for chemotherapy toxicity may not be as well demarcated as in Table

2. In response to a ‘high-risk’ score, a decision may be made to no longer recommend

chemotherapy (risk of harm now outweighs benefits); to alter the recommendation to a lower

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intensity and/or reduced-dose regimen; or to proceed with their initial chemotherapy

recommendation (risk of harm is recognised but does not outweigh benefits). More frequent

monitoring for toxicity might also be considered. In response to a ‘low-risk’ score, a

recommendation for chemotherapy may be made where it otherwise would not have been

recommended; the intensity of the chemotherapy regimen may be increased; or chemotherapy

may proceed without change to the initial recommendation. For a risk score to change

chemotherapy recommendations, the risk of severe chemotherapy toxicity predicted by the

score has to be significantly different to oncologists’ estimate of the risk when making their

initial chemotherapy recommendation. Confidence that the changes made in response to the

risk score will alter outcomes for the better of the patient is also needed.

3.3.5 Pathway to implementation of a clinical prediction tool

Following the derivation of a clinical prediction tool, validation of the tool to show

reproducible accuracy is required. Internal validation of the tool within the population in which

it was derived can be achieved using statistical methods like bootstrapping. (177) External

validation establishes generalisability and can be performed in an external population and

clinical setting very similar to that in which it was derived (narrow external validation) or in a

range of different clinical settings (broad external validation). After adequate external

validation, the impact of the clinical prediction tool on clinical decision-making and outcomes

may then be evaluated. (178) (Figure 4)

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Figure 4. Pathway to day-to-day use of a clinical prediction tool. The stages of

development of a clinical prediction tool involve derivation, validation, and evaluation of

impact on decision-making and outcomes prior to implementation into clinical practice.

Widespread implementation of a clinical prediction tool into clinical practice is generally only

achieved if the tool alters clinical decision-making, improves outcomes for patients, or has

impact on resource utilisation. (178) Widely used examples include the “Ottawa ankle rules”

(179) that impact on resource utilisation (reduced ordering of plain x-rays for suspected ankle

fracture without adverse patient outcomes), and the Framingham risk score, (180) valuable in

primary prevention strategies for cardiovascular disease.

Chapter 6 evaluates the predictive value of the CARG Toxicity Score in a population external

to the population in which the score was derived, and as such contributes to the evidence for

use of the score in clinical practice. Following logistic regression to determine associations

between toxicity and covariates, the area under the ROC curve was used to summarise the

predictive value of the CARG Toxicity Score, oncologists’ estimates of the risk of toxicity, and

a combined measure of the two.

Derivation Validation Evaluation of impact Implementation

in clinical practice

low level of evidence high

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3.4 Estimating survival time to inform treatment decisions

Chapter 8 evaluates the accuracy of oncologists’ estimates of survival time for older adults

having chemotherapy for advanced cancer. This section describes the methods used in this

chapter in more detail.

3.4.1 Survival data

Survival data is a type of “time to event” data, and in oncology is usually expressed as the

duration of survival from a time point of interest (for example, date of diagnosis or

commencement of treatment), or the proportion of people who have died at a particular point

in time (for example survival rate at 2 years). The most common means of presenting survival

data for a given population is with a Kaplan-Meier survival curve, which plots the probability

of survival on the y-axis against time on the x-axis. The curve extends horizontally for every

period of time where no death is observed, and falls vertically where there are observed deaths.

Kaplan-Meier curves are commonly described by the median survival time or by the survival

rate at a given time point. These point estimates are useful and convenient starting points for

discussions about prognosis.

3.4.2 Scenarios for survival

Whilst convenient for oncologists, communicating estimates of expected survival time to

patients in the form of a point (single number) estimate, for example a median expected survival

time or survival rate at a given time point, implies certainty, and may be misinterpreted by

patients. For example, patients may interpret a median estimated survival time as an expected

maximum survival time or limit, rather than appreciate that half of patients will live shorter

than this time and half will live longer. An alternative approach is to estimate and communicate

expected survival time to patients in the form of scenarios for survival, often presented in the

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form of best-case, typical, and worst-case scenarios. (36-41) This approach more adequately

conveys the range of survival times that make up the survival curve.

Figure 5. Scenarios for survival derived from the Kaplan-Meier survival curve.

Stockler et al described a method for describing best-case, typical, and worst-case scenarios

for survival using data available from the Kaplan Meier survival curve in a study of 106 patients

with advanced cancer. (41) In this study, the authors observed that the shape of the Kaplan-

Meier survival curve was approximately exponential and the percentiles of the survival curve

could be used to describe the expected best-case, typical, and worst-case scenarios of survival

time for an individual (Figure 5). Further, it was noted that with exponential curves, there is a

relationship between the curve’s percentiles and its median: the 90th percentile is approximately

one-sixth of the median survival time, the 75% percentile is approximately one-half of the

median survival time, the 25th percentile is approximately twice the median survival time, and

the 10th percentile is approximately three times the median survival time. The authors proposed

that expected survival could be described by the following scenarios:

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a. Best-case scenario: the 10% of patients surviving the longest would live approximately 3

or more times the population’s median survival time

b. Typical scenario: 50% of patients would live between half and double the population’s

median survival time

c. Worst-case scenario: the 10% of patients living the shortest would die within one-sixth of

the population’s median survival time

In the same work, treating oncologists were asked to estimate expected survival time (median

survival time in a group of identical patients) for each of their patients. The proportion of

patients with observed survival times bounded by simple multiples of their estimated survival

time was similar to that of the exponential distribution: that is, approximately 5 to 10% of

patients lived 3 or more times their expected survival time (best-case scenario), about 50% of

patients lived between half and double their expected survival time (typical scenario), and

about 5 to 10% of patients died within ≤ one-sixth of their expected survival time (worst-case

scenario). (41) Interestingly, and relevant to older adults who may have comorbidity or frailty,

this method of eliciting oncologists’ estimates requires the patient as a whole to be considered,

not the cancer diagnosis (and prognosis) in isolation. Kiely et al recommends the median

survival from clinical trial populations be used as a starting point for oncologists to make their

estimate, with adjustment down for individual patient characteristics, such as poor performance

status and comorbidity. (40) Using oncologists’ estimates of expected survival time for an

individual has since consistently been shown to be a reasonable basis for estimating these best-

case, typical, and worst-case scenarios. (37, 181, 182) Further to this, systematic reviews of

first-line chemotherapy trials for patients with advanced breast cancer, (39, 42) advanced non-

small cell lung cancer, (36) and castration-resistant prostate cancer (43) have shown that the

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median survival time of patients in a clinical trial can be used to accurately estimate best-case,

typical, and worst-case scenarios for survival for patients in those trials.

Chapter 8 evaluates the applicability of using this method of “simple multiples of the median”

to estimate best-case, typical, and worst-case scenarios for survival in older adults with

advanced cancer.

3.4.3 Accuracy and error when estimating survival

Accuracy broadly describes the ‘closeness’ of an estimate to the true value. Terms related to

accuracy include:

a. Calibration: the extent of the divergence of repeated measurements (or estimates) from the

true value. Loss of calibration reflects presence of systematic error, or bias.

b. Precision: the reproducibility of a measurement, or the proximity of repeated

measurements (or estimates) to each other, the degree of scatter. Loss of precision reflects

the presence of random error.

c. Discrimination: the ability to observe differences accurately.

The relationship between precision and calibration is illustrated in Figure 5.

Figure 6. Precision, and calibration of estimates (or measurements)

In the setting of oncologists’ estimates of survival time, these terms can be considered as:

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a. Calibration: oncologists’ estimates are well calibrated if equal proportions over-estimate

survival as under-estimate survival. Consistent over-estimation or under-estimation reflects

poor calibration, and the presence of bias.

b. Precision: the proximity of an oncologist’s estimate of survival time for an individual to

the observed survival time of that individual. An estimate of survival time may be deemed

precise if it falls within a defined range either side of the observed survival time.

c. Discrimination: the ability of an oncologist to separate patients based on their survival time.

For example, to anticipate from a random pair of patients, the patient with the longest (or

shortest) survival time.

Boundaries for precision

Chapter 9 determines the precision of oncologists’ estimates of expected survival time of

older adults having chemotherapy for advanced cancer. The boundaries of precision used

were those consistent with most previous studies on the accuracy of estimates of expected

survival time. (37, 41, 183-185) Estimates of expected survival time for an individual patient

were deemed precise if they fell within 0.67 to 1.33 times the observed survival time. Being

proportional to the observed survival time allowed for a wider range of accuracy (in absolute

terms) for longer observed survival times. For example, whilst a fixed boundary of precision

of +/- 1 month would be reasonable for estimates where the observed survival time were <6

months, for observed survival times of ≥ 12 months a similar boundary of +/- 1 month is too

narrow, and a wider range of accuracy more reasonable. Choosing a boundary for precision

that was proportional to the observed survival time allowed for this.

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3.5 Evaluating patient and physician aspects of decision-

making

Perspectives on decision-making about treatment with chemotherapy are evaluated in Chapters

5 and 7 (perspectives of the oncologist) and Chapter 9 (perspectives of the older adult).

Methods used within these chapters are discussed and presented in more detail here.

3.5.1 Design of surveys

Surveys in Chapters 5, 7, and 9 use a combination of existing validated tools as well as study-

specific items designed to answer study objectives. Validated tools used are outlined with the

presentation of each chapter. Further detail on the Control Preferences Scale, factors

influencing decision-making about treatment, and hypothetical scenarios provided below. For

study-specific questions, item generation and categorisation of responses was by expert

consensus of the research team. Surveys used in Chapters 5 and 7 were piloted on, and items

refined by two clinicians outside of the research team. The survey used in Chapter 9 was piloted

on, and items refined by a small group of older adults receiving chemotherapy who were not

involved in the study. Formal statistical tests for internal consistency and reliability were not

performed for the few newly generated survey items, as information obtained by new items

was largely demographic, or assessed aspects of decision-making that were not unidimensional.

3.5.2 Preferred roles in decision-making

Chapter 9 determines the role that older adults with advanced, incurable cancer prefer to play

in decision-making about palliative chemotherapy. The most widely used method of eliciting

these role preferences in medical decision-making is using Degner and Sloan’s Control

Preferences Scale. (8) The scale asks patients to select one of five statements, ranging from

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most active to most passive, that best describes the role the prefer to play when making a

decision about treatment. A modified CPS has been used previously in studies comparing

preferred and perceived (actual) roles in decision-making. (186) (Table 3)

Table 3. The Control Preferences Scale

Response Control Preferences Scale (CPS) Modified CPS* Role

A I prefer to make the final selection

about which treatment I will receive

I made the final selection about

which treatment I would receive

Active

B I prefer to make the final selection of

my treatment after seriously

considering my doctor’s opinion

I made the final selection of my

treatment after seriously considering

my doctor’s opinion

Active

C I prefer that my doctor and I share

responsibility for deciding which

treatment is best for me

My doctor and I shared responsibility

for deciding which treatment was

best for me

Collaborative

D I prefer that my doctor make the

final decision about which treatment,

but seriously consider my opinion

My doctor made the final decision

about which treatment would be used

but seriously considered my opinion

Passive

E I prefer to leave all decisions

regarding treatment to my doctor

My doctor made all the decisions

regarding my treatment

Passive

*The modified control preferences scale uses past tense in order to evaluate actual decision-making roles (the

role that was played)

Use of non-parametric tests

Analysis of the collected data on role preferences in Chapter 9 required the use of non-

parametric tests, as the data were not normally distributed. The first step was to assign each of

the five statements on the CPS (preferred role) and modified CPS (perceived role) an ordinal

score (1=most active role, through to 5=most passive role). The Wilcoxon signed-rank test was

used to compare preferred with perceived roles in decision-making. Having repeated

measurements on the same sample, that is a participant’s preferred role and then their perceived

role, gave paired data. The null hypothesis of the Wilcoxon signed-rank test is that the mean

ranks of paired data sets do not differ. (187)

The Wilcoxon rank sum test (Mann-Whitney U test) was used to determine associations with

role preference. The Wilcoxon rank sum test is applied to data from independent samples, the

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null hypothesis being that the distributions of data from both samples are equal. (187, 188) For

example, that the distribution of role preferences within the group of patients who agreed to

have chemotherapy is the equal to the distribution of role preferences within the group of

patients who did not agree to have chemotherapy. The effect size of the difference in role

preferences between groups categorised by baseline characteristics were then summarised by

odds ratios (preference for an active role over collaborative/passive).

3.5.3 Factors influencing decision-making

Chapter 5 evaluates factors influencing decision-making about treatment with chemotherapy

for oncologists, and Chapter 9 evaluates factors influencing decision-making about treatment

with chemotherapy for older adults with advanced cancer. In both chapters, rating and ranking

style questions are used to evaluate the importance of prespecified patient and clinical factors.

In addition, Chapter 5 uses hypothetical patient scenarios to evaluate the influence of patient

age and expected rates of chemotherapy toxicity on oncologists’ likelihood to recommend

chemotherapy.

Rating and ranking

In chapters 5 and 9, respondents were provided with rating questions with a set of prespecified

factors that might influence their decision-making about treatment. Respondents were asked to

rate the importance of these factors using a Likert scale, and a mean importance rating

calculated for each factor by assigning an ordinal value to each response option. Ranking

questions were paired with these rating questions, such that respondents were required to

compare factors against the others, recognising that an individual may rate all factors as equally

important.

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Hypothetical scenarios

Hypothetical patient scenarios are used in Chapter 5 to determine the likelihood of oncologists

recommending palliative and adjuvant chemotherapy (on a 5-point Likert scale from ‘very

likely’ to ‘extremely unlikely’) according to age and the risk of severe (grade 3-5)

chemotherapy toxicity. Other patient factors did not vary between the 16 hypothetical scenarios

in order to isolate the effect of increasing patient age and increasing risk of severe

chemotherapy toxicity on the likelihood of recommending chemotherapy. Logistic regression

was then used to determine associations between patient age and toxicity risk on the likelihood

of oncologists to recommend chemotherapy, and to test physician factors as predictors for

chemotherapy recommendation. Logistic regression models were fitted using generalised

estimating equations in this instance to account for correlations among responses from an

individual respondent.

The development of the hypothetical scenarios was informed by literature review of similar

studies and by consensus opinion of the involved clinician researchers. Scenarios were

designed to reflect common clinical scenarios requiring consideration of chemotherapy. For

example, the hypothetical scenario requiring consideration of adjuvant chemotherapy

following surgical resection quoted a risk of recurrence and survival rate at 5 years consistent

with stage III colon cancer. The improvement in risk of recurrence and in survival conferred

by the proposed chemotherapy quoted was similar to benefits seen with adjuvant fluorouracil

and oxaliplatin (FOLFOX) chemotherapy. The remaining detail provided within the scenarios

were considered by consensus of the clinician researchers to be key to decision-making about

chemotherapy, and included information on performance status, functional status, comorbidity,

social support, patient’s preferences for treatment, and for the palliative scenario, detail on

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presence of cancer-related symptoms. This was done such that respondents focussed on age

and risk of severe chemotherapy toxicity as the variables of interest within the scenarios.

3.5.4 Other survey instruments

The patient survey in Chapter 9 uses the following additional validated tools:

Significant others

The importance of significant others in decision-making about cancer treatment was described

by Stigglebout et al. (189) A 5-point scale with responses ranging from “I do not care at all

about their opinion” to “I take their opinion very seriously” elicits the importance of the opinion

of various significant others to the treatment decision at hand.

Satisfaction with Decision Scale

The Satisfaction with Decision Scale (190) is a 6-item questionnaire that measures patient

satisfaction with a decision at the time the decision has been made. It was originally developed

and validated in the context of decisions regarding hormone replacement therapy. The scale

asks respondents to consider the healthcare decision that they have just made, and indicate on

a 5-point Likert scale (strongly agree to strongly disagree) the extent to which six statements

about the decision are true.

Vulnerable Elders Survey (VES-13)

The Vulnerable Elders Survey (80) is a short, validated tool that identifies older adults who are

at risk of functional decline and mortality. It is primarily based on reported functional status,

with items of the survey including patient age, patient-reported comparision of their health to

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others their own age, degree of difficulty performing six different physical activities and five

different instrumental activities of daily living.

Patient-rated Performance Status

The Patient-rated performance status (Pt-PS) is derived from the patient generated subjective

global assessment (PG-SGA) scale, a validated self-administered scale used to assess

nutritional and functional status. (191) Part of the PG-SGA rates activity and function, and is

very similar to the ECOG-Performance Status scale used by clinicians. Patients are asked to

select the item from the following that best describes them:

0 Normal and with no limitations

1 Not my normal self, but able to be up and about with fairly normal activities

2 Not feeling up to most things, but in bed or chair less than half the day

3 Able to do little activity and spend most of the day in bed or chair

4 Pretty much bedridden, rarely out of bed

3.6 Summary

This chapter has provided additional detail to the methods described in individual chapters of

the thesis. The instruments used to assess health domains as part of the GA were described.

Consideration was given to the interpretation of the parameters of predictive tools, with a focus

on the ROC curve, and to the consequences of error in predicting chemotherapy toxicity at the

patient level. The methods behind using estimates of expected survival time to calculate

individualised best-case, typical, and worst-case scenarios for survival time were described, as

were the methods used to elicit factors influencing patient and oncologist decision-making

about chemotherapy.

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4. Principles of geriatric oncology:

Systemic treatment considerations for older adults

with colon cancer

4.1 Overview

This chapter is a published work that is aimed at a clinical audience. Decision-making about

systemic treatment for older adults with colon cancer is reviewed. Particular attention is given

to the evidence base for adjuvant and palliative chemotherapy in older adults, the clinical

assessment of older adults prior to making a treatment recommendation, and balancing benefits

with harms. The published manuscript is quoted verbatim. Formatting is as required by the

journal.

Publication details

Moth EB, Vardy J, Blinman P. Decision-making in geriatric oncology: systemic treatment

considerations for older adults with colon cancer. Expert Rev Gastroenterol Hepatol, 2016.

10(12): p1321-1340.

Contribution of authors

I, Dr Erin Moth, contributed to concept development, and was responsible for reviewing the

existing literature, and drafting and revising of the manuscript.

Prof Janette Vardy contributed to concept development and revision of the manuscript.

Dr Prunella Blinman contributed to concept development, and drafting and revision of the

manuscript.

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4.2 Abstract

Introduction

Colon cancer is common, and can be considered a disease of older adults with more than half

of cases diagnosed in patients aged over 70 years. Decision-making about treatment with

chemotherapy for older adults may be complicated by age-related physiological changes,

impaired functional status, limited social supports, concerns regarding the occurrence of and

ability to tolerate treatment toxicity, and the presence of comorbidities. This is compounded by

a lack of high quality evidence guiding cancer treatment decisions for older adults.

Areas covered

This narrative review evaluates the evidence for adjuvant and palliative systemic therapy in

older adults with colon cancer. The value of an adequate assessment prior to making a treatment

decision is addressed, with emphasis on the geriatric assessment. Guidance in making a

treatment decision is provided.

Expert commentary

Treatment decisions should consider goals of care, a patient’s treatment preferences, and weigh

up relative benefits and harms.

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4.3 Introduction

Colon cancer is the second most common cancer in Australia, with an estimated incidence rate

of 62 new cases per 100,000 persons in 2016. (192) Colon cancer is a disease of older adults

with a median age at diagnosis of 70 years, and an increasing incidence with increasing age,

highest in patients >80 years (430 per 100,000, compared to 97 per 100,000 in those aged 55-

59 years). (192) As the population ages there will be increasing numbers of older adults with

colon cancer who will require treatment. As such, specialists involved in their care need to be

adept at the management of older adults with colon cancer. For medical oncologists, this means

the clinical assessment of older adults, decision-making about treatment, and management of

older adults with regards to systemic therapy.

Systemic therapy for colon cancer includes adjuvant chemotherapy, palliative chemotherapy

and palliative targeted therapy. Adjuvant chemotherapy is given following resection of a high-

risk stage II or stage III colon cancer to improve disease-free survival (DFS) and overall

survival (OS), with the goal of treatment being cure. Palliative chemotherapy and targeted

therapy is given for metastatic, incurable colon cancer to prolong progression-free survival

(PFS) and OS, and to relieve cancer-related symptoms and improve health-related quality of

life, but is generally not curative.

Older adults with colon cancer, compared with younger adults, receive less chemotherapy in

both the adjuvant (193-195) and palliative settings. (196) Published rates of adjuvant

chemotherapy use for stage III colon cancer in adults ≥70 years range from 35% in the

Netherlands (197) to 42-52% in the United States. (193) Reasons for this are multifactorial,

and include the presence of comorbidities, (198) age-related physiological changes, (199) and

geriatric problems such as impaired functional status, frailty, limited social supports, cognitive

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impairment, mood disturbance, polypharmacy, and malnutrition. (200) These factors affect

patients’ suitability for systemic therapy, the ability to tolerate and deliver the treatment, and

highlight the complexity of decision-making about systemic therapy for older adults with

cancer.

Decision-making about systemic therapy in patients of all ages requires a trade-off between the

benefits and harms of the treatment. In older adults, this trade-off is more tightly balanced and

skewed towards smaller survival benefits for a greater risk of treatment toxicity. This decision-

making complexity is compounded by the limited evidence guiding treatment

recommendations in this population due to the underrepresentation of older adults in cancer

clinical trials (201) and the paucity of randomised, prospective data for the use of systemic

therapy in this population. (202)

The aim of this narrative review is to explore key issues surrounding decision-making for

systemic therapy for older adults with colon cancer. Areas covered include the use of

chemotherapy in older adults in both adjuvant and palliative settings, balancing benefits with

harms, the appropriate assessment of older adults to better inform treatment decision-making,

and consideration of patient preferences. Given the differences in the approach to the curative

treatment of colon and rectal cancers, here we review the evidence for the adjuvant treatment

of colon cancer only. As the palliative systemic treatment of these cancers is similar, the review

of palliative treatment applies to both colon and rectal (‘colorectal’) cancers.

4.4 Adjuvant chemotherapy considerations

Adjuvant chemotherapy is considered for patients with resected stage III (node-positive) or

high-risk stage II colon cancer. (203, 204) Single agent chemotherapy with 5-fluororuracil (5-

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FU) plus a co-factor of leucovorin (LV) or levamisole reduces the relative risk of recurrence

of colon cancer by 30% at 5 years (absolute improvement in DFS of 12% from 55% to 67%,

HR 0.70, 95%CI 0.63-0.78, p<0.0001) and the relative risk of death by 26% at 5 years (absolute

improvement in OS of 7%, from 64% to 71%, HR 0.74, 95%CI 0.66-0.83, p<0.0001). (205)

Capecitabine, an oral fluoropyrimidine, is at least as effective as bolus 5-FU/LV, (206, 207)

but differs in its toxicity profile as later discussed.

Combination chemotherapy with FOLFOX (oxaliplatin and infusional 5-FU/LV), compared

with infusional 5-FU/LV alone, had superior DFS and OS in the landmark MOSAIC trial,

reducing the relative risk of recurrence by 20% (absolute improvement in DFS of 5.9% at 5

years from 67.4% to 73.3%, HR 0.80, 95%CI 0.68-0.93, p=0.003) and the relative risk of death

by 16% (absolute improvement in OS of 2.5% at 6 years from 76% to 78.5%, HR 0.84, 95%CI

0.71-1.00, p=0.046). (50) The DFS and OS benefits were limited to patients with stage III

disease, whereas patients with high-risk stage II disease had an insignificant absolute

improvement in 5-year DFS (82.3% versus 74.6%, HR 0.72, 95%CI 0.50-1.02, p=not

reported). (50) The NSABP-07 trial of FLOX (oxaliplatin and bolus 5-FU/LV) (208) and the

XELOXA (NO16968) trial of XELOX (oxaliplatin and capecitabine) (209) also showed

significant improvements in DFS and added further support for oxaliplatin-containing

combination regimens. Six months of FOLFOX or XELOX chemotherapy is now the

recommended standard of care for fit patients with resected stage III colon cancer. (204)

4.4.1 The evidence for adjuvant chemotherapy in older adults

The inclusion of older adults in trials of adjuvant chemotherapy for colon cancer has been

limited. The median age of patients participating in phase III adjuvant chemotherapy trials is

61 years (IQR 53 to 68 years) (210) with patients aged ≥70 years comprising only 18% of

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patients. (211) Many trials excluded patients based on age alone with an upper limit of 75 years.

(50) As such, evidence for the use of adjuvant chemotherapy in older adults comes largely from

pooled subgroup analyses, (205, 211-213) subgroup analyses from individual randomised

trials, (206, 208, 209, 214, 215) and large population-based studies (193-195, 216) as

summarised in Table 1.

Adjuvant chemotherapy with 5-fluorouracil

Two pooled analyses of seven phase III randomised trials compared adjuvant 5FU/LV to

surgery alone in patients with stage III and selected stage II colon cancer. (205, 213) Sargent

et al found similar efficacy of 5FU/LV across 10-year age groups, but increasing age was

associated with higher rates of grade 3 leucopenia. (213) Gill et al evaluated for predictive and

prognostic factors, including age, and found that increasing age was a negative prognostic

factor for OS (likely due to competing causes of mortality), but treatment benefits were

consistent across age groups. (205)

Results of large population-based studies also support the use of adjuvant 5-FU in older adults

with colon cancer, and reduce some of the selection bias of the pooled analyses of only

including fitter patients suitable for clinical trials. Three large population-based studies, two

accessing patient data from the Medicare/SEER database (194, 195) and one from the National

Cancer Database, (216) of patients with stage III colon cancer who had adjuvant 5-FU showed

older adults were less likely to receive adjuvant chemotherapy, (216) but obtained an OS

benefit from the treatment, (194, 195, 216) the magnitude of which may diminish with

increasing age. (195)

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Capecitabine as an alternative to 5-fluorouracil

Oral capecitabine is a convenient alternative to intravenous schedules of 5FU/LV and is

considered an equivalent alternative to 5FU/LV as adjuvant and palliative chemotherapy for

colon cancer. Data supporting its use in older adults comes from the X-ACT trial that compared

adjuvant capecitabine with bolus 5FU/LV in patients with stage III colon cancer, and included

a subgroup analysis by age with 396 patients aged ≥70 years. (206) Capecitabine, compared

with bolus 5FU/LV, had similar DFS and OS, which was maintained across all age groups (HR

for DFS 0.88, 95%CI 0.77-1.01, p-value for non-inferiority <0.0001; HR for OS 0.86, 95%CI

0.74-1.01, p-value for non-inferiority <0.001). Of note, dose reductions were required more

frequently in older patients (51% in those ≥70 years, 39% in those <70 years), without an

impact on efficacy. Capecitabine caused significantly less febrile neutropenia, neutropenia,

stomatitis, alopecia, diarrhoea and nausea, but more hand-foot syndrome, and this toxicity

profile did not differ according to age group (<65 years and ≥65 years). (207, 217)

Combination oxaliplatin and 5-fluorouracil/ leucovorin or capecitabine adjuvant

chemotherapy

The benefit of the addition of oxaliplatin to adjuvant 5-FU/LV or capecitabine for older adults

with colon cancer is unclear. Subgroup analyses of randomised trials have produced conflicting

results (208, 209, 215) and two of these trials excluded patients over the age of 75 years. (50,

215) Tournigand et al analysed outcomes of the 315 patients aged 70-75 years in the MOSAIC

trial of FOLFOX chemotherapy and found no DFS or OS benefit in this subgroup (in those >70

years, HR for DFS 0.93, 95%CI 0.64-1.35, p=0.710; HR for OS 1.10, 95%CI 0.73-1.65,

p=0.661). (215) This exploratory analysis lacked power due to small numbers, however, and a

large proportion of the older patients had stage II disease where there was no proven benefit of

oxaliplatin in the trial. Tolerance of FOLFOX was similar to younger patients. Yothers et al

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(208) included an analysis by age in the final results of the NSABP-C07 trial of FLOX versus

bolus 5-FU/LV. It should be noted that the FLOX regimen is not commonly used due to high

rates of severe and fatal toxicities (grade 4 or 5 toxicity in all ages 11.8%, those ≥70 years

19.3%; rates of death in all ages 1.3%, those ≥70 years 3.6%). The addition of oxaliplatin

significantly improved DFS but not OS, although an analysis by age demonstrated a significant

OS benefit in younger patients not seen in older patients (OS in those <70 years: HR 0.80,

95%CI 0.68-0.95, p=0.013; ≥70 years HR 1.18, 95%CI 0.86-1.62, p=0.3). The exploratory age

subgroup analysis of the XELOXA (NO16968) trial of XELOX versus bolus 5FU/LV, (209)

showed improved DFS and OS in patients ≥70 years, albeit with a smaller effect size than in

younger patients (in those ≥70 years HR for DFS 0.86, 95%CI 0.64-1.16, HR for OS 0.91,

95%CI 0.66-1.20, p-values not reported; in those <70 years HR for DFS 0.80, 95%CI 0.67-

0.94, and HR for OS 0.82, 95%CI 0.67-1.01, p-values not reported). The comparatively better

point estimates of benefit for oxaliplatin in the XELOXA trial was likely due to the inclusion

of patients with stage III disease only, whereas the MOSAIQ and NSABP-C07 trials included

patients with stage II disease where there was no benefit of oxaliplatin.

A pooled analysis of the three aforementioned oxaliplatin trials (MOSAIC, XELOXA,

NSABP-C07) (211) showed the OS benefit of oxaliplatin was in patients aged <70 years (HR

for death 0.83, 95%CI 0.74-0.92), but not in those ≥70 years (HR for death 1.04, 95%CI 0.85-

1.27, p value for age-treatment interaction 0.05). A pooled analysis by Haller et al included

more contemporary trials of FOLFOX or XELOX. (212) Data from four randomised trials

(XELOXA, AVANT, X-ACT, and NSABP C08) included more than 400 patients ≥70 years in

each treatment arm. The addition of oxaliplatin improved DFS and OS in patients aged <70

and ≥70 years and across levels of comorbidity. The effect size was smaller in older patients;

for example, in patients aged <70 years, the HR for death was 0.62 (95%CI 0.54-0.72,

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p<0.0001) but for patients aged ≥70 years, the HR for death was 0.78 (95%CI 0.61-0.99,

p=0.045). Rates of serious adverse events were higher in those ≥70 years across all treatment

cohorts. (212)

‘Real-world data’ from a large population-based cohort study further complicates the evidence

for the addition of oxaliplatin in older adults. Sanoff et al (193) evaluated four databases

(SEER-Medicare, National Comprehensive Cancer Network, New York State Cancer Registry,

and Cancer Care Outcomes Research and Surveillance Consortium) to determine the effect of

adjuvant chemotherapy on OS in 5489 patients aged ≥75 years with stage III colon cancer.

Adjuvant chemotherapy significantly improved OS over no treatment (for example, in the

largest SEER-Medicare database, HR for death 0.60, 95%CI 0.53-0.68), but out of the three

databases evaluable for the added benefit of oxaliplatin, only the two largest databases

demonstrated a trend towards improved OS (SEER-Medicare data HR for death 0.85, 95%CI

0.69-1.04; NYSCR HR for death 0.82, 95%CI 0.51-1.33; in contrast to NCCN HR for death

1.84, 95%CI 0.48-7.05). (193)

The presently recruiting ADAGE trial (218) will add to the evidence for adjuvant

chemotherapy in older adults with colon cancer. It will be the first elderly-specific randomised

phase III trial evaluating the efficacy of adjuvant chemotherapy in those ≥70 years with stage

III disease. Using a factorial design, this trial aims to determine whether there is a benefit to

adjuvant chemotherapy over observation, and whether the addition of oxaliplatin confers more

benefit over 5-FU or capecitabine alone. Results are not expected until 2025.

In summary, the available evidence best supports the use of 5-FU/LV or capecitabine as

adjuvant chemotherapy options in older adults with resected stage III colon cancer. The

additional benefit of oxaliplatin in this population is unclear, and should only be considered for

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fitter older patients after a full discussion about the incremental benefits and likely excess

toxicity with combination FOLFOX or XELOX.

4.5 Palliative chemotherapy considerations

Palliative chemotherapy and targeted therapy for advanced, incurable colon cancer improves

DFS and OS over best supportive care alone. Contemporary trials of combination first-line

chemotherapy and targeted therapy report a median OS of around 30 months. (219, 220) A

variety of chemotherapy and targeted therapy agents have activity in advanced colon cancer,

and may be used in combination or as single agents. The choice of regimen is tailored to the

individual patient and depends on the goals and timing of treatment, toxicity of the individual

regimens, and the molecular profiles of the cancer such as RAS status.

Commonly used first line options for treatment include single agent fluoropyrimidines (221)

of infusional 5FU/LV rather than bolus schedules (222, 223) or oral capecitabine as an

equivalent alternative; (224) combination treatment with FOLFOX, (222, 225) FOLFIRI

(5FU/LV + irinotecan), (226) CapeOx (capecitabine + oxaliplatin), (227) or FOLFOXIRI

(5FU/LV + oxaliplatin + irinotecan). (228, 229) Targeted agents can be used alone or in

combination with chemotherapy and include bevacizumab, (230) cetuximab and panitumumab,

(231) aflibercept, (232) ramucirumab, (233) and regorafenib. (234) Detailed discussion of the

evidence supporting these regimens and their various combinations and sequences is beyond

the scope of this review.

4.5.1 The evidence for palliative chemotherapy in older adults

Older adults have been traditionally under-represented in trials of palliative chemotherapy in

colon cancer (235) with key trials excluding participants based on age alone. (225, 226, 236)

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More recently, however, there have been an increasing number of elderly-specific prospective

trials to better guide treatment decisions for older adults with advanced, incurable disease as

per Table 2. Newer trials are using broader inclusion criteria to allow for the enrolment of older

adults who would not otherwise be considered for standard treatment, (237-242) using

modified dosing schedules to improve toxicity in older adults, (237, 241, 243) and

incorporating baseline geriatric assessments to better identify older adults who are more (or

less) likely to tolerate and benefit from treatment. (243) The choice of chemotherapy regimen

in the palliative setting should pay particular attention to the goals of care, and consider the

balance between treatment-related toxicities and quality of life.

Single agent chemotherapy with 5-fluorouracil or capecitabine

Single agent first-line chemotherapy with 5-FU/LV or oral capecitabine is a reasonable

approach for older adults who are not fit for combination therapy, or who wish to avoid

additional toxicities that impair quality of life. The equivalent efficacy of 5-FU in older patients

(≥70 years), compared with younger patients, was demonstrated in a large retrospective pooled

analysis of data from 22 trials of 5-FU (infusional or bolus), where 629 of 3825 (16.4%)

patients were aged ≥70 years. (223) Response rate (RR), PFS, and OS were equivalent in older

and younger patients, suggesting that patients who fulfil traditional clinical trial criteria,

regardless of age, have the same chance of benefiting from treatment. Toxicity data were not

reported in this study. Older age has been reported as a risk factor for 5FU toxicity. (244) with

older adults experiencing higher rates of leukopenia, diarrhoea and stomatitis, (213, 245)

although this is, in part, mitigated by the preferential use of infusional over bolus 5-FU

regimens. (222, 246)

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Oral capecitabine is at least as efficacious as intravenous 5-FU in the palliative setting (224)

with higher RR and equivalent time to progression (TTP) and OS. (247-250) It is generally

well tolerated in the elderly (238, 251) with its most common severe (grade 3 or 4) side effects

being hand-foot syndrome (up to 21%) and diarrhoea (up to 18%). (252) The main advantage

over intravenous 5-FU is its oral administration and convenience of treatment, although no

differences were seen in a study comparing QOL outcomes between administration schedules.

(242) The oral administration poses some risk if follow up is not as frequent, as the treatment

requires self-monitoring and identification of toxicities needing treatment interruption between

scheduled visits, which may be difficult for older adults with, for example, limited social

supports or impaired cognition.

Combination chemotherapy with 5-fluorouracil or capecitabine and oxaliplatin or irinotecan

Subgroup and pooled analyses of key phase III trials suggest that fit older adults who meet

traditional clinical trial inclusion criteria are likely to experience similar benefits of

combination oxaliplatin chemotherapy to younger patients in the first-line setting. (253-256)

The modest RR and PFS benefits from the addition of oxaliplatin (236) must be balanced

against toxicities attributable to oxaliplatin, including higher rates of neutropenia, nausea, and

neuropathy. (257) Older adults also experience higher rates of gastrointestinal toxicity from

oxaliplatin combination chemotherapy (253, 256) with the rate of severe (grade 3/4) diarrhoea

about 25% in those ≥70 years. (256, 258)

The benefit of oxaliplatin combination chemotherapy is questionable in less fit older adults, as

suggested by the FOCUS2 trial. (242) This trial was designed for elderly or frail patients

considered unsuitable for standard dose combination chemotherapy in the first-line setting, and

addressed the questions of efficacy and safety of first-line combination versus single agent

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chemotherapy, and oral versus intravenous 5-FU-based chemotherapy. The median age of the

459 randomised patients was 74 years, and 29% had a performance status of ECOG 2. The

addition of oxaliplatin resulted in a non-significant improvement in PFS (HR 0.84, 95%CI

0.69-1.01, p=0.07), and there was no difference in global QOL between the capecitabine and

FU/LV arms or OS across all four arms. The highest rates of severe (≥grade 3) toxicity occurred

in the oxaliplatin/capecitabine arm (43%) and lowest in the 5-FU arm (27%). Capecitabine

increased the rate of ≥grade 3 events, and was associated with higher rates of nausea, vomiting,

diarrhoea, anorexia and hand-foot syndrome. Oxaliplatin regimens were associated with higher

rates of diarrhoea, neurosensory toxicity, nausea, vomiting and neutropenia.

Combination irinotecan and 5FU/LV chemotherapy was evaluated in a recent elderly-specific

randomised phase III trial by Aparicio et al. (243) Elderly patients (≥75 years) were randomised

to one of two variations of 5FU/LV administration (classic or simplified), with or without

irinotecan, in the first-line setting using a 2x2 factorial design. In total, 282 patients were

randomised, with a median age of 80 years. Despite an improvement in RR with the addition

of irinotecan (41.7% v 21.1%, p=0.0003), there was no significant difference in the primary

outcome of PFS (HR 0.84, 95%CI 0.66-1.07, p=0.15). Rates of any grade ≥3 toxicity were

increased with combination therapy (76.3% v 52.2%), notably neutropenia (38.5% v 5.2%) and

diarrhoea (22.2% v 5.2%). A sub-study of geriatric assessment showed severe chemotherapy

toxicity was predicted by impairments in cognition (by Mini Mental State Examination) and

instrumental activities of daily living. (136)

Results of the Aparicio trial (243) are in contrast with those from a pooled analysis of four

phase III randomised trials of 5-FU +/- irinotecan, which showed that patients ≥70 years

obtained similar PFS benefits to younger patients (HR for PFS in those ≥70 years 0.75, 95%CI

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0.61-0.90, P=0.0026; for those <70 years HR for PFS 0.77, 95%CI 0.70-0.85, P<0.0001). (235)

This discrepancy is likely due to differences in patient selection; patients aged >80 years, the

median age in the Aparicio trial, made up <1% of those in the pooled analysis and patients

included in the pooled analysis were of better performance status (91% ECOG 0-1).

Bevacizumab in older adults

Bevacizumab is a humanised monoclonal antibody that inhibits vascular endothelial growth

factor (VEGF). Bevacizumab improves PFS and OS when added to first and second-line

chemotherapy for metastatic colorectal cancer, (259-261) and when continued beyond disease

progression. (262) The toxicities of bevacizumab include arterial and venous thromboembolic

events, haemorrhage, hypertension, proteinuria, wound healing complications, and bowel

perforation. (263) Bevacizumab is recommended in addition to first-line combination

chemotherapy for fit patients, and in addition to 5-FU or capecitabine alone in those not fit for

combination treatment. (204)

Evidence for the efficacy of bevacizumab in older adults first came from pooled analyses of

randomised trials (264-266) and large observational cohort studies. (267-269) The largest

pooled analysis was by Hurwitz et al (265) and included data from 3763 patients (1492 ≥65

years) enrolled in seven randomised trials of chemotherapy +/- bevacizumab in the first or

second-line setting with a subgroup analysis by age (<65 years, ≥65 years, and ≥75 years). The

addition of bevacizumab improved both PFS and OS across all age subgroups with a similar

effect size. Toxicity was not analysed by age, though the bevacizumab group had higher rates

of severe proteinuria (1.7% v 0.2%), hypertension (7.7% v 1.6%), bleeding (4.0% v 1.9%),

arterial thromboembolic events (3.3% v 1.6%) and any grade gastrointestinal perforation (2.2%

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v 0.7%). An earlier pooled analysis of four of the seven included trials (264) showed patients

≥65 years had significantly higher rates of thromboembolic (particularly arterial) events.

Three large observational cohort studies, one from the BriTE registry of 1953 patients (896

≥65 years) receiving first-line bevacizumab. (268) another of 1914 patients (628 ≥65 years)

treated on an expanded access program of first-line bevacizumab added to physician’s choice

chemotherapy, (269) and a German study of 1777 patients (206 ≥75 years) receiving first-line

bevacizumab-based therapy (267) provide further support for the benefit of bevacizumab in

older adults. In the BriTE registry, (268) PFS was similar across age groups (<65 years, 65-74

years, 75-79 years, ≥80 years) however median OS declined with age (median OS 26mths for

those <65 years, 16.2mths for those ≥80 years), with age being a significant predictor of

survival even after adjustment for baseline covariates. Age was also a predictor for arterial

thromboembolic events (adjusted incidence rate ratio of 2.01 for patients 75-79 years and 1.67

for patients ≥80 years compared with patients <65 years). In the expanded access program,

(269) PFS and OS in patients treated with bevacizumab did not significantly differ across age

groups (<65 years, 65-74 years, ≥75 years). In contrast, the German observational study (267)

found both PFS and OS to be significantly shorter in patients ≥70 years treated with

bevacizumab plus chemotherapy compared to those <70 years, possibly explained by less

intensive chemotherapy backbones received by older patients in this study, and competing

causes of mortality.

Recognising that not all elderly patients were suitable to receive standard combination

chemotherapy as a backbone to bevacizumab, Cunningham et al (237) designed the innovative

AVEX trial, an elderly-specific randomised phase III trial evaluating the efficacy of

bevacizumab when added to first-line capecitabine for patients ≥70 years with ECOG-PS ≤2.

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This trial specifically included patients deemed by investigators to be unsuitable for first-line

oxaliplatin or irinotecan-based combination chemotherapy. The median age of the 280

randomised patients was 76 years. The addition of bevacizumab to capecitabine was found to

improve PFS (9.1mths versus 5.1mths; HR 0.53; 95%CI 0.41-0.69; P<0.0001), with a non-

significant trend towards improved OS. The rate of grade 3 to 5 treatment-related adverse

events was 40% in the combination arm compared to 22% with capecitabine alone, with rates

of grade ≥3 venous thromboembolism of 8% versus 4%, hand-foot syndrome 16% versus 7%,

and any grade haemorrhage 25% versus 7%. The results of this trial were consistent with prior

single arm phase II studies that demonstrated acceptable safety and efficacy of bevacizumab in

addition to capecitabine in older patients. (239, 270) This has been adopted by clinicians as an

acceptable first-line regimen for older adults.

Antibodies against the epidermal growth factor receptor (EGFR)

Cetuximab and panitumumab are monoclonal antibodies directed against the epidermal growth

factor receptor. Their benefit in the first- and second-line settings in combination with

chemotherapy, and in the third-line setting as monotherapy for patients with RAS wild-type

advanced colorectal cancer was confirmed in a meta-analysis of fourteen randomised trials (HR

for PFS in 1st and 2nd line combined =0.83, 95% CI 0.76-0.90, p<0.0001). (231) The main

toxicity concerns with these agents are fatigue, skin rash and electrolyte abnormalities.

Retrospective (271, 272) and small phase II studies (240, 241, 273) of anti-EGFR therapy in

older adults show they derive similar benefits to younger adults, with no indication of greater

toxicity. A large German observational study of 657 patients receiving cetuximab in

combination with irinotecan for pre-treated metastatic colorectal cancer found no difference in

RR or PFS between those <65 years and those ≥65 years. The rate of grade 3/4 toxicities did

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not differ significantly between groups, despite older patients in the study having more

comorbidities. (272) Acceptable toxicity profiles in older adults receiving cetuximab or

panitumumab have also been demonstrated in a number of single arm phase II studies, (240,

241, 273) even in study populations deemed ‘unsuitable’ for chemotherapy. (240, 273)

Regorafenib

Regorafenib is an oral multi-kinase inhibitor with superior efficacy to placebo in patients with

chemotherapy refractory metastatic disease. In the CORRECT trial, (234) regorafenib

improved median OS by a modest 1.4 months (HR 0·77, 95% CI 0·64-0·94, p=0·0052),

however 67% of patients in the regorafenib arm required dose modification due to toxicity.

Toxicities included any-grade fatigue in 47%, any-grade diarrhoea in 34%, and grade ≥3 hand-

foot skin reactions in 17%. Two separate subgroup analyses by age, one from the CORRECT

trial, (274) and another from the CONSIGN phase IIIb continued access trial, (275) have shown

that the efficacy and tolerability of regorafenib does not differ significantly by age.

4.6 The assessment of older adults with cancer for

chemotherapy

The clinical assessment of older adults for chemotherapy should detect patient factors that may

impact treatment tolerability and outcomes, and include assessment of geriatric domains such

as functional status, social supports, comorbidities, nutrition, mood, medications, and

cognition. Treating physicians should also make an assessment of a patient’s preference for the

treatment being considered and how much they wish to be involved in the treatment decision

to foster shared decision-making, where desired, and patient-centred care. (5)

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4.6.1 Broadening the concept of ‘fitness for chemotherapy’

Patients included in clinical trials are generally younger and fitter than patients seen in routine

clinical practice, which makes translation of clinical trial results to patients who would not

meet traditional trial inclusion criteria difficult. A strong emphasis is often placed on

chronological age and performance status when selecting older adults who are ‘fit for

chemotherapy’.

Population studies addressing receipt of chemotherapy for colon cancer in both the adjuvant

(193-195, 216, 276, 277) and palliative settings (196) have revealed that increasing age is

associated with a decline in receipt of chemotherapy, even when adjusted for comorbidity.

(125, 277) Additionally, studies exploring chemotherapy decision-making by oncologists in

the adjuvant setting have revealed that age is an important driver of treatment choice. (112,

113, 115, 123, 124) Chronological age does not correlate well with physiological age (139) and

should not be used in isolation to assess a patient’s fitness for chemotherapy. Problems with

the use of performance status, such as the Eastern Cooperative Oncology Group (ECOG) score,

(150) are that it is only a crude measure of a patient’s functional status, and does not capture

more subtle changes in physical function, comorbidities, nutrition, social supports, cognition,

or the presence of geriatric syndromes, all of which are relevant to treatment decision-making

for the older adult. (86) It is widely accepted that patients with a poor performance status

(ECOG 3 or 4) have a worse prognosis, (152) may tolerate treatment poorly, (278) and

generally should not have chemotherapy.

Methods to improve the assessment and selection of older adults for chemotherapy include the

use of geriatric assessments, geriatric screening tools, and risk predicting tools. A

Comprehensive Geriatric Assessment (CGA) is a formal assessment of key health domains in

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an older adult. It is commonly used in geriatric medicine and usually performed by a

multidisciplinary team. Given it is resource intensive, abbreviated forms more simply called

geriatric assessments (GA), are feasible alternatives for use in oncologic practice. (147, 279)

Alternatives to a GA are brief screening tools that identify vulnerable older adults who may

benefit from a full CGA, such as the G8-Questionnaire or Vulnerable Elders Survey (VES-13).

(172)

The most consistently identified benefit of a GA in oncology is in identifying the presence of

geriatric problems that would not otherwise have been detected, and which may benefit from

supportive interventions or further evaluation. (78, 92, 280) For example, in a large prospective

study of 1967 patients aged ≥70 years for whom treatment with chemotherapy was being

considered, 71% were considered to be ‘at risk’ by use of a screening tool (G8-questionnaire)

of whom 51% had geriatric problems found on CGA. (63) This was despite 70% of patients

being rated as good performance status (ECOG 0/1). Other potential benefits of a GA are its

ability to predict for treatment outcomes such as all-cause mortality and toxicity, although the

available data are inconsistent amongst studies. (88, 92) Impaired instrumental activities of

daily living (IADLs), poor performance status, and number of GA deficits have been found to

be the most consistent predictors from a GA for mortality. (82) There are no consistent

individual predictors from a GA for toxicity. (82, 92)

Despite the uncertainty as to how best to use the GA to inform treatment decision-making, it

can positively impact on clinical care. For example, a systematic review by Hamaker et al (86)

included six studies assessing the impact of a GA on treatment decisions, and found that

interventions (oncological and non-oncological) were recommended for the majority of

patients who had a GA. The initial chemotherapy treatment plan was modified in a median of

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39% of patients, often to a less intensive treatment. A prospective study of 375 patients with

cancer, found that impaired activities of daily living and malnutrition were factors

independently associated with a change in treatment. (281)

Due to the valuable information gained from a more holistic assessment of the older adult with

cancer, incorporation of a GA covering key health domains is now recommended as part of

routine oncological practice. (78) The next step is to evaluate the value of interventions

performed on the basis of a GA, with early studies suggesting that patients who receive GA

prompted interventions are more likely to complete chemotherapy as planned. (282) This is the

primary outcome being assessed in the presently recruiting GERICO trial, (283) a randomised

GA intervention trial specifically for frail older adults receiving chemotherapy for colon

cancer. Further intervention trials in mixed tumour populations are also recruiting. (284)

4.6.2 Tools to aid in predicting chemotherapy toxicity

There has been a recent focus on the development of geriatric-specific prediction models to

estimate the risk of severe chemotherapy-related toxicity. The CRASH Score (68) uses

treatment and patient-related factors to estimate the likelihood of severe chemotherapy-related

toxicity. Two separate predictive models were developed, one for haematological and one for

non-haematological toxicity. The Cancer and Aging Research Group’s (CARG) Toxicity Score

(49) was developed in a cohort of 500 patients ≥65 years commencing chemotherapy for any

tumour type or stage. An 11-item predictive model uses cancer and treatment variables,

laboratory values, and geriatric assessment variables to classify patients as low-, intermediate-

, or high-risk of experiencing a grade 3 to 5 treatment-related toxicity over the course of

treatment. The CARG Score has recently been validated in an external cohort (48) and in a

non-small cell lung cancer population. (148) There are no data about the impact of the CRASH

100

or CARG scores on treatment decisions, but they may be useful in identifying older adults at

particular risk for treatment toxicity for whom supportive measures or dose modifications may

be considered.

4.6.3 Other tools to guide chemotherapy decision-making

There are an increasing number of other tools that may assist in treatment decision-making and

the assessment of older adults. Mortality risk calculators, such as ePrognosis, (285) have been

used in general geriatric medicine, and may be useful in decision-making about adjuvant

chemotherapy where consideration of competing causes of mortality is paramount.

4.7 Making a decision about treatment

Decisions about chemotherapy are a complex interplay between patient and clinician factors

[Figure 1]. Considerations include the goals of treatment, the baseline prognosis of the patient’s

cancer, the likelihood of benefits and harms, and how patients trade-off these benefits and

harms, otherwise known as their preferences for the treatment.

4.7.1 Balancing the benefits and harms of adjuvant chemotherapy

The goal of adjuvant chemotherapy is cure. The benefits of adjuvant chemotherapy are usually

a reduction in the risk of cancer recurrence and improved OS and, as such, are intangible and

only realised in time. The harms of adjuvant chemotherapy, however, are real, experienced by

most patients, and carry the risk of long-term side effects such as peripheral neuropathy that

impair quality of life in patients who are otherwise cured of cancer.

101

For older adults having adjuvant chemotherapy, competing causes of mortality may diminish

the survival benefits of treatment, and physiological and functional impairments may increase

the risk of harm. Hence, the balance between treatment benefits and harms are more finely

balanced than in younger patients who have more to gain from long term survival benefits and

generally tolerate treatment better. Clinicians should be mindful, however, not to underestimate

the life expectancy of an older adult based on chronological age alone, as an 80-year-old

woman in excellent health may be expected to live up to a further 13 years, compared to a 75-

year-old in relatively poor health, who may be expected to live another 7 years. (286)

With regards to adjuvant chemotherapy in older adults with colon cancer, the discussed

evidence supports the use of single agent 5-FU/LV or capecitabine, but less so oxaliplatin

combinations in patients ≥70 years. The benefits of adjuvant 5-FU/LV or capecitabine

chemotherapy for stage III colon cancer are a 30% relative reduction in the risk of recurrence

and a 26% relative reduction in the risk of death at 5 years. The additional benefit of oxaliplatin

is uncertain, possibly decreases with increasing age, and occurs at the cost of more frequent

and severe toxicity. The harms of adjuvant chemotherapy include the toxicities and

inconveniences of the treatment. Older adults who have adjuvant chemotherapy for colon

cancer outside of clinical trials have reduced dose intensities due to early cessation and dose

modifications for toxicity. (197, 276, 287, 288) Even highly selected older adults treated within

the trial setting experience higher rates of treatment toxicity and early treatment

discontinuation, (206, 208, 212) with advanced age an identified predictor of early mortality.

(210)

The risks and benefits of adjuvant chemotherapy also need to be weighed against the risk and

consequences of recurrent disease. Stage III colon cancer has a relatively poor baseline

102

prognosis with overall recurrence rates about 50% at 5 years, and 5-year OS rates of 40 to 80%

without adjuvant treatment. (289) Careful discussion of the possible outcomes of all treatment

options, including observation, should be part of the decision-making process.

4.7.2 Balancing the benefits and harms of palliative chemotherapy

The goals of palliative chemotherapy are to prolong survival (PFS and OS), improve disease-

related symptoms and health-related quality of life (HRQOL), but generally not to cure the

cancer. The proviso on the palliative intent of treatment is the small subset of patients with

metastatic colorectal cancer that have resectable disease for whom chemotherapy is given along

with surgery for cure.

The balance between the benefits and harms of palliative chemotherapy differs to the adjuvant

setting because patients with advanced, incurable cancer will have shorter survival without

chemotherapy and usually have cancer-related symptoms which may improve with treatment.

Chronic toxicities are also less of a concern. The balance for palliative chemotherapy is also

dynamic and changes over time, and is generally characterised by more intensive chemotherapy

with bigger survival benefits when a patient is first diagnosed, and moves towards less intensive

chemotherapy and the maintenance of HRQOL towards their end-of-life.

As discussed, there is reasonably good evidence supporting the use of palliative chemotherapy

and targeted therapy in older adults with colorectal cancer. There is much more heterogeneity

of the benefits and harms of palliative chemotherapy, compared with adjuvant chemotherapy,

due to the wide spectrum of metastatic colorectal cancer from asymptomatic, oligometastatic

disease, to widespread metastases with a high tumour burden and substantial cancer-related

103

symptoms. In older adults, the additional wide spectrum of pre-morbid physiological and

functional reserve necessitates individualised treatment decisions.

4.7.3 Patients’ preferences for chemotherapy

Patients’ evaluation of the relative benefits and harms of chemotherapy, compared with a given

alternative or alternatives, are known as their preferences for the treatment. (1) Consideration

of patients’ preferences is a key feature of shared decision-making, and clinicians should

endeavour to elicit patients’ preferences for chemotherapy in order to personalise treatment

decisions.

Studies of patients’ preferences can be used by clinicians as a guide and useful starting point

in discussions about treatment. Preferences studies quantitate the trade-off between the benefits

and harms of chemotherapy by determining the minimal survival benefit that makes the harms

and inconveniences worthwhile. In general, patients judge small survival benefits to make

adjuvant chemotherapy worthwhile, but preferences are inherently individual and vary widely.

Some patients require very small benefits and others very large benefits to make treatment

worthwhile, and others never want chemotherapy at all.

A study of patients’ preferences for adjuvant chemotherapy for colon cancer included 123

patients with a median age of 65 years (range 19-86) who had all previously had adjuvant

treatment. (104) Most patients judged small survival benefits sufficient to make adjuvant

chemotherapy worthwhile; for example, an extra 1 month survival time (beyond a baseline of

5 years) or an extra 1-2% survival rate (beyond a 5-year baseline of 65% or 85%). Older age

was associated with needing larger survival benefits to make adjuvant chemotherapy

worthwhile. (104) A recent study of patients’ preferences for palliative chemotherapy for colon

104

cancer included 107 patients with a mean age of 57 years. Patients varied in their willingness

to tolerate different treatment-related adverse events, and older age was associated with a

reduced willingness to tolerate any adverse events. (105) Overall, patients were less willing to

tolerate non-acute, adverse events affecting quality of life, such as depression, fatigue and pain.

To our knowledge, there have been no studies specifically evaluating the treatment preferences

of older adults with colon cancer. One study of cancer patients’ preferences for chemotherapy

in all cancer types suggested older adults were less willing to accept major toxicity for the same

survival benefit as younger patients. (103) These limited data suggest that older adults having

chemotherapy for colorectal cancer will likely need larger survival benefits to make the

treatment worthwhile.

4.8 Expert commentary

Evidence for the use of systemic therapy for older adults with colon cancer has been historically

limited by trial design, leading to the inclusion of only small numbers of fitter older adults who

are not representative of the general geriatric population. This has prompted age-based analyses

of existing trial and population datasets, and more recently, elderly-specific prospective clinical

trials. In the adjuvant setting, the evidence supports the use of single agent 5-FU/LV or

capecitabine for stage III colon cancer, but less so oxaliplatin-containing combinations in

patients aged ≥70 years. In the palliative setting, there is good evidence to suggest older adults

experience similar benefits to younger adults from single-agent infusional fluorouracil or

capecitabine, though their toxicity profiles differ. Fitter older adults are likely to experience

similar benefits to younger adults from combination chemotherapy regimens, but modest

benefits in RR and PFS must be balanced with additive toxicity where quality of life is a

priority. The use of combination chemotherapy regimens in less fit or frail older adults is not

105

supported by the available evidence. Geriatric assessment should be incorporated into the

routine assessment of the older adult with cancer, both to provide information that may affect

treatment decisions and to identify areas where supportive care interventions may be needed.

Treatment decisions should consider the goals of care, an older adult’s treatment preferences,

and weigh up the relative benefits and harms.

106

Table 1. Key elderly-specific colon cancer chemotherapy trials, subgroup analyses, and large population-based studies in the

adjuvant setting

Study

Design and Methods

Participants

Results and Comments

Schmoll et al,

2015 (209)

Exploratory subgroup analysis

by age of phase III RCT XELOX

vs bolus 5FU/LV in stage III CC

(NO16968 trial); to determine

treatment effect in those <70

years versus ≥70 years

N=1886 (total)

N=409 (≥70yrs)

N=1477 (<70yrs)

DFS: XELOX improved DFS compared with bolus 5FU/LV; HR 0.80, 95%CI 0.69-

0.93, p=0.004

≥70yrs, HR 0.86, 95%CI 0.64-1.16, p=NR; <70yrs, HR 0.80 95%CI 0.67-0.94,

p=NR

OS: XELOX improved OS compared with bolus 5FU/LV; HR 0.83, 95%CI 0.70-

0.99, p=0.04

≥70yrs, HR 0.91, 95%CI 0.66-1.26, p=NR; <70yrs, HR 0.82, 95%CI 0.67-1.01,

p=NR

Note: Improved DFS & OS with XELOX compared to 5FU/LV; smaller effect size in

older patients

Haller et al,

2015 (212)

Pooled analysis of four RCTs in

stage III CC

(XELOXA/AVANT/X-

ACT/NSABP-C08) of the

addition of oxaliplatin to 5FU or

capecitabine; to compare

efficacy (OS and DFS) and

safety (AE) of

XELOX/FOLFOX v 5FU/LV

according to age and

comorbidity

N=4819 (total)

XELOX/FOLFOX:

N=2418 (<70yrs)

N=480 (≥70yrs)

5FU/LV:

N=1497 (<70yrs)

N=424 (≥70yrs)

DFS: XELOX/FOLFOX improved DFS compared with 5FU/LV; HR 0.69, 95%CI

0.63-0.76, p<0.001

For those ≥70yrs, HR 0.77, 95%CI 0.62-0.95, p=0.014

For those <70yrs, HR 0.68, 95%CI 0.61-0.76, p<0.0001

OS: XELOX/FOLFOX improved OS compared with 5FU/LV; HR 0.65, 95%CI 0.57-

0.73, p<0.001

For those ≥70yrs, HR 0.78, 95%CI 0.61 to 0.99, p=0.045

For those <70yrs, HR 0.62, 95%CI 0.54 to 0.72, p<0.0001

AE: Fewer serious G3/4 AEs in <70yrs; oxaliplatin-related G3/4 AEs comparable

across ages

Note: Benefit in DFS and OS for the addition of oxaliplatin regardless of age and

medical comorbidity; smaller effect size in older patients

McCleary et al,

2013 (211)

Pooled subgroup analysis from 7

adjuvant chemotherapy trials in

stage II/III CC (ACCENT

database) comparing IV 5FU

with oral or combination

regimens; to assess impact of

N=14528 (total)

N=11953 (<70yrs)

N=2575 ≥70yrs

DFS: In those ≥70yrs; HR 0.94, 95%CI 0.78-1.13

In those <70yrs; HR 0.78, 95%CI 0.71-0.86; p value for age-treatment

interaction =0.09

TTR: In those ≥70yrs; HR 0.86, 95%CI 0.69-1.06

In those <70yrs; HR 0.77, 95%CI 0.69-0.85; p value for age-treatment

interaction =0.36

107

age on CC recurrence and

mortality

OS: In those ≥70yrs; HR 1.04, 95%CI 0.85-1.27

In those <70yrs; HR 0.83, 95%CI 0.74-0.92; p value for age-treatment

interaction =0.05

Note: Survival benefit of oxaliplatin reduced in patients ≥70 years. Oral

fluoropyrimidines non-inferior to IV 5FU/LV across all age groups.

Tournigand et

al, 2012 (215)

Subgroup analysis of MOSAIC

trial (phase III RCT of 5FU/LV

+/- oxaliplatin (FOLFOX4) in

stage II/III CC); to determine

treatment effect in patients aged

70 to 75yrs

N=2246 (total)

N=315 (70-75yrs)

DFS: FOLFOX4 improved DFS compared with 5FU/LV; HR 0.80, 95%CI 0.68-0.93,

p=0.003

For those 70-75yrs, HR 0.93, 95%CI 0.64-1.35, p=0.710

For those <70yrs, HR 0.78, 95%CI 0.66-0.92, p=0.003

OS: FOLFOX4 improved OS compared with 5FU/LV; HR 0.84, 95%CI 0.71-1.00,

p=0.046

For those 70-75yrs, HR 1.10, 95%CI 0.73-1.65, p=0.661

For those <70yrs, HR 0.80, 95%CI 0.66-0.97, p=0.020

Note: No significant benefit in DFS or OS with the addition of oxaliplatin to 5FU in

patients 70-75yrs

Chang et al,

2012 (290)

Prospective phase II feasibility

study of capecitabine dose

escalation in patients ≥ 70yrs;

2000mg/m2/day (D1-14),

escalated to 2500mg/m2/day at

cycle 2

N=82 Intens.: Dose escalation possible in 56 patients

41 (50%) completed treatment with RDI of ≥80%;

41 (50%) completed treatment with RDI <80%, or did not complete all cycles

Tox: G3 HFS 21 (25.6%)

QOL: No significant change in QOL over time

Twelves et al,

2012 (206)

Subgroup analysis of phase III

RCT (non-inferiority) of

capecitabine vs bolus 5FU/LV,

stage III CC (the X-ACT trial);

to compare efficacy across age

groups (primary endpoint DFS)

N=1987 (total)

N=396 ≥70yrs

N=1591 <70yrs

DFS: Capecitabine equivalent to 5FU, HR 0.88 95%CI 0.77-1.01, p<0.0001; no age-

treatment interaction (p=0.50)

For those ≥70yrs, HR 0.97, 95%CI 0.72-1.31; for those 40-69yrs, HR 0.87,

95%CI 0.75-1.01

OS: Capecitabine equivalent to 5FU, HR 0.86 95%CI 0.74-1.01, p=0.000116; no

age-treatment interaction (p=0.78)

For those ≥70yrs, HR 0.91, 95%CI 0.65-1.26; for those 40-69yrs, HR 0.87,

95%CI 0.73-1.04

Note: Capecitabine is an equivalent alternative to 5FU; effect maintained in older

patients.

Sanoff et al,

2012 (193)

Retrospective cohort study of

patients ≥75 years with stage III

CC from 4 databases:

SEER/Medicare, NYSCR,

N = 5489 (≥75yrs)

Receipt of any adjuvant chemotherapy:

SEER: HR for death 0.60, 95%CI 0.53-0.68, p=NR

NYSCR: HR for death 0.76, 95%CI 0.58-1.01, p=NR

CanCORS: HR for death 0.48, 95%CI 0.19-1.21, p=NR

108

CanCORS, NCCN, to determine

effect of adjuvant chemotherapy

on survival

NCCN: HR for death 0.42, 95%CI 0.17-1.03, p=NR

Addition of oxaliplatin:

SEER: HR for death 0.84, 95%CI 0.69-1.04, p=NR

NYSCR: HR for death 0.82, 95%CI 0.51-1.33, p=NR

NCCN: HR for death 1.84, 95%CI 0.48-7.05, p=NR

Note: Patients ≥75yrs have improved survival from adjuvant chemotherapy, though

the incremental benefit of the addition of oxaliplatin is small.

Yothers et al,

2011 (208)

Exploratory subgroup analysis of

phase III RCT of bolus 5FU/LV

+/- oxaliplatin (FLOX) in stage

II or III CC (NSABP-C07 trial);

to determine efficacy (DFS and

OS) in patients ≥70yrs

N=2409 (total)

N=396 (≥70yrs)

N=2013 (<70yrs)

DFS: FLOX improved DFS, HR 0.82, 95%CI 0.72-0.93, p=0.002

For those <70yrs, HR 0.76, 95%CI 0.66-0.88, p<0.001

For those ≥70yrs, HR 1.03, 95%CI 0.77-1.36, p=0.87

OS: FLOX did not significantly improve OS, HR 0.88, 95%CI 0.75-1.02, p=0.08

For those <70yrs, HR 0.80 95%CI 0.68-0.95, p=0.013

For those ≥70yrs, HR 1.18, 95%CI 0.86-1.62, p=0.3

The effect of oxaliplatin on OS varied significantly by age (p interaction

=0.039)

Tox: For those <70yrs, G4 or G5 toxicity rate 9% (5FU) and 10% (FLOX)

For those ≥70yrs, G4 or G5 toxicity rate 13% (5FU) and 20% (FLOX)

Note: The addition of oxaliplatin to bolus 5FU/LV improved OS in patients <70

years, but not ≥70yrs

Zuckerman et

al, 2009 (195)

Retrospective cohort study of

patients ≥66yrs with stage III CC

using SEER-Medicare database;

to examine the effect of age on

benefit from adjuvant

chemotherapy

N=7182 (≥66yrs) Treatment: 51.1% received chemotherapy after surgery; 66-69yrs: 19%;

70-74yrs: 29.8%; 75-79yrs: 29.7%; 80-84yrs: 16.5%; ≥85yrs: 5.1%

Cancer death: 66-69yrs: HR 0.47, 95%CI 0.33-0.65, p<0.001

70-74yrs: HR 0.32, 95%CI 0.25-0.40, p<0.001

75-79yrs: HR 0.41, 95%CI 0.34-0.50, p<0.001

80-84yrs: HR 0.59, 95%CI 0.49-0.72, p<0.001

≥85yrs: HR 0.54, 95%CI 0.41-0.71, p<0.001

Note: Age modified the survival benefit of chemotherapy; magnitude of

benefit declining with age

Lembersky et

al, 2006 (214)

Subgroup analysis in phase III

RCT UFT/LV vs bolus 5FU/LV,

stage II/III CC (NSABP C06),

non-inferiority study

N=1551 (total)

N=939 (≥60yrs)

N=612 (<60yrs)

OS: Oral UFT/LV equivalent to bolus 5FU/LV; HR 1.010, 95%CI 0.822-1.242,

p=0.92

For those ≥60yrs, HR 1.40, 95%CI 1.12-1.74, p=0.03 (age <60yrs referent)

DFS: Oral UFT/LV equivalent to bolus 5FU/LV; HR 1.005, 95%CI 0.848-1.192,

p=0.95

For those ≥60yrs, HR 1.41, 95%CI 1.18-1.69, p=0.002 (age <60yrs referent)

109

Jessup, et al

2005 (216)

Retrospective cohort study of

patients with stage III CC using

National Cancer Database (US);

to determine prevalence of

chemotherapy use and 5YS

N=85934 (total) Prevalence of adjuvant chemotherapy use:

<60 years: 82%, 60-69 years: 77.2%, 70-79 years: 69%, ≥80 years: 39.2%

5YS was similar across age groups

Elderly patients derived the same benefits from chemotherapy, but were less likely to

receive treatment

Gill et al, 2004

(205)

Pooled analysis of data from 7

RCTs of adjuvant chemotherapy

(5FU vs surgery alone), stage

II/III CC; to determine the

benefit (DFS and OS) of

adjuvant chemotherapy by age

N=3302 (total)

N=1438 (<60yrs)

N=1864 (≥60yrs)

DFS: 5FU improved DFS, HR 0.70, 95%CI 0.63 to 0.78

In those <60yrs, 5Y-DFS 69% v 56%, p<0.001; In those ≥60yrs, 5Y-DFS 63%

v 55%, p=0.001

OS: 5FU improved OS, HR 0.74, 95%CI 0.66 to 0.83

In those <60yrs, 5YS 74%v 67%, p=0.0002; In those ≥60yrs, 5YS 69 v 62%,

p=0.0005

Note: Benefit (5YR OS and DFS) for adjuvant chemotherapy across age groups; age

prognostic for OS

Sundararajan et

al, 2002 (194)

Retrospective cohort study of

patients ≥65yrs, stage III CC,

using SEER/Medicare database

N=4768 OS: 5FU improved OS, HR 0.66, 95%CI 0.6 to 0.73

Note: Adjuvant chemotherapy with 5FU improved OS in patients ≥65yrs

Sargent et al,

2001 (213)

Pooled analysis of data from 7

RCTs comparing 5FU to surgery

alone, stage II/III CC, with

analysis of efficacy and toxicity

by 10yr age groups

N=3351 (total)

N=564 (≤50y)

N=1012 (51-60y)

N=1269 (61-70y)

N=506 (>70y)

DFS: 5FU improved DFS, HR 0.68, 95%CI 0.60 to 0.76, p<0.001

OS: 5FU improved OS, HR 0.76, 95%CI 0.68 to 0.85, p<0.001

Efficacy (OS and DFS) did not differ across age groups; p value for interaction

for OS 0.61 and TTR 0.33

Tox: Age was associated with higher rates of ≥ G3 leucopenia

Abbreviations: RCT – Randomised Controlled Trial; XELOX – capecitabine + oxaliplatin; FU/LV – fluorouracil/leucovorin; CC – colon cancer; NR – not reported; FOLFOX – infusional

5FU/LV + oxaliplatin; OS – overall survival; DFS – disease-free survival; TTR – time to recurrence; RDI – relative dose intensity; QOL – quality of life; SEER – Surveillance, Epidemiology

and End Results Program; CanCORS – Cancer Care Outcomes Research & Surveillance Consortium; NCCN – National Comprehensive Cancer Network ; NYSCR – New York State Cancer

Registry; UFT/LV – oral tegafur-uracil/leucovorin; 5YS – 5-year survival

110

Table 2. Elderly-specific prospective colon cancer trials and large subgroup analyses in the palliative setting

Study

Design

Participants

Results and Comments

Stein et al, 2015

(251)

Non-interventional, observational study

of patients with mCRC receiving

capecitabine as part of first-line

chemotherapy, with analysis by age

group

N=1249 (total)

N= (>75yrs)

N= (≤75yrs)

ORR: 38% (≤75yrs) v 32% (>75yrs) (P=0.019)

PFS: 9.7mths (≤75yrs) v 8.2mths (>75yrs) (P=0.00021)

OS: 31.0mths (≤75yrs) v 22.6mths (>75yrs) (P<0.0001)

Tox: No significant difference in toxicity between age groups

Note: ORR, PFS and OS differed between age groups; older patients less

likely to receive combination tx

Aparicio et al,

2016 (243)

Phase III RCT (2x2 factorial design) in

patients ≥75yrs, classic LV5FU2 or

simplified LV5FU2, +/- irinotecan 1st

line (FFCD 2001-02)

Primary end point PFS

Secondary end points RR, OS

N=282 (total)

N=71 (LV5FU2)

N=71 (simplified

LV5FU2)

N=70 (LV5FU-

irinotecan)

N=70 (FOLFIRI)

RR: Improved RR with irinotecan

21.1% v 41.7%, P=0.0003

PFS: No difference in PFS with addition of irinotecan

PFS 5.2mths v 7.3mths (HR 0.84, 95%CI 0.66-1.07, P=0.15)

OS: No difference in OS with addition of irinotecan

14.2mths v 13.3mths (HR 0.96, 95%CI 0.75-1.24, P=0.77)

Tox: Increased G3/4 toxicity with irinotecan (76.3% v 52.2%)

Note: The addition of irinotecan to 5FU did not improve PFS or OS in

patients ≥75yrs

Sastre et al,

2015 (273)

Phase II single arm study, panitumumab

1st-line for those ≥70yrs with KRAS WT

mCRC, frail or unsuitable for

chemotherapy

N=33 (≥70yrs) PFS: 4.3mths, 95%CI 2.8mths-6.4mths

OS: 7.1mths, 95%CI 5mths-12.3mths

Tox: G3 rash 15.2%

Pietrantonio et

al, 2015 (240)

Phase II single arm study, panitumumab

in patients ≥75yrs with KRAS WT

mCRC, 1st- or 2nd-line, those not

suitable for chemotherapy

N=40 ORR: 32.5%

PFS: 6.4mths, 95%CI 4.9-8.0mths

OS: 14.3mths, 95%CI 10.9-17.7mths

Tox: G3 rash 20%

Feliu et al, 2014

(270)

Single arm phase II study of

bevacizumab + CAPOX in patients

≥70yrs with mCRC

N=68 ORR: 46%

TTP: 11.1mths, 95%CI 8.1-14.1mths

OS: 20.4mths, 95%CI 13.2-27.6mths

Tox: G3/4 diarrhoea 18%, G3/4 DVT 6%, G3/4 PE 4%

111

Hofheinz et al,

2014 (267)

Observational cohort study of patients

receiving bevacizumab + chemotherapy

1st line for mCRC, analysis by age

N=1777 (total)

N=480 (≥70yrs)

N=213 (≥75yrs)

ORR: <70yrs v ≥70yrs: 62% v 55%, p=0.0046

PFS: <70yrs v ≥70yrs: 10.5mths v 9.5mths, p=0.074

<75yrs v ≥75yrs: 10.5mths v 8.9mths, p=0.00019

Doublet v single-agent in ≥70yrs: 9.7mths v 9.2mths, p=0.52

OS: <70yrs v ≥70yrs: 25.8mths v 22.7mths, p<0.0008

<75yrs v ≥75yrs: 25.8mths v 20.8mths, p<0.0001

Note: PFS and OS were shorter in older compared with younger patients

receiving bevacizumab in combination with chemotherapy

Cunningham et

al, 2013 (237)

Phase III RCT in patients ≥70yrs,

capecitabine +/- bevacizumab 1st-line in

mCRC in those not fit for doublet

chemotherapy, primary endpoint PFS

N=280 (median age

76yrs)

PFS: 9.1mths v 5.1mths, HR 0.53, 95%CI 0.41-0.69, p<0.0001

Tox: G3-5 treatment-related AE rate: capecitabine + bevacizumab 40%,

capecitabine alone 22%; G3-5 events: hand-foot syndrome (16% v

7%), diarrhoea (7% v 7%), VTE (8% v 4%)

Note: Bevacizumab improves PFS when added to cetuximab 1st-line

treatment in patients ≥70yrs

Hurwitz et al,

2013 (265)

Pooled analysis from 7 RCTs of

chemotherapy +/- bevacizumab; to

determine the efficacy and safety of

bevacizumab, analysis by age

N=3763 (total)

N=2269 (<65yrs)

N=1492 (≥65yrs)

N=426 (≥75yrs)

PFS: Addition of bevacizumab improved PFS, HR 0.57, 95%CI 0.46-0.71,

p<0.0001

For those <65yrs, HR 0.68, 95%CI 0.62-0.75, P<0.0001

For those ≥65yrs, HR 0.66, 95%CI 0.59-0.75, P<0.0001

For those ≥75yrs, HR 0.55, 95%CI 0.44-0.70, P<0.0001

OS: Addition of bevacizumab improved OS, HR 0.80, 95%CI 0.71-0.90,

p=0.0003

For those <65yrs, HR 0.80, 95%CI 0.73-0.88, P<0.0001

For those ≥65yrs, HR 0.87, 95%CI 0.77-0.97, P=0.0156

For those ≥75yrs, HR 0.76, 95%CI 0.62-0.94, P=0.0118

Note: The addition of bevacizumab to chemotherapy improved OS and PFS

across all age groups

Abdelwahab et

al, 2012 (291)

Phase II study of cetuximab + irinotecan

≥2nd line in patients ≥65yrs

N=46 PFS: 4mths, 95%CI 3-5.6mths

OS: 7mths, 95%CI 5.9-8mths

Tox: G3/4 rash 20%, G3/4 diarrhoea 18%

Jehn et al, 2012

(272)

Observational study of cetuximab in

combination with chemotherapy in

patients with pretreated mCRC, reduced

performance status and age >65yrs

N=657 (total)

N=309 (≤65yrs)

N=305 (>65yrs)

PFS: No difference in PFS between age groups, p=0.12

For those ≤65yrs v >65yrs, 6.6mths v 7mths

Tox: No difference in rate of G3/G4 toxicities between age groups, though

median duration of toxicities longer for those >65yrs

112

Note: The safety and efficacy profile of cetuximab was similar across age

groups (≤65yrs & >65yrs)

Sastre et al,

2012 (241)

Phase II single arm study, 1st-line

cetuximab + capecitabine in patients

≥70yrs

N=66 (≥70yrs) ORR: 31.8%, 95%CI 20.9 to 44.4%

PFS: 7.1mths, 95%CI 5.3 to 8.4mths

OS: 16.1mths, 95%CI 12.0 to 18.8mths

Tox: high rates of severe paronychia led to protocol dose reduction of

capecitabine from 1250mg/m2 BD to 1000mg/m2 BD; G3/4 rash

28%, G3/4 HFS 20%, G3/4 diarrhoea 12%

Seymour et al,

2011 (242)

Elderly/frail specific, phase III RCT, 1st-

line, 2x2 factorial design; FU/LV or

capecitabine +/- reduced dose oxaliplatin

(25%DR), in those not fit for full-dose

chemotherapy (cited as due to frailty in

71%, age in 68%)

Primary endpoint: PFS for addition of

oxaliplatin; change in global QOL for

substitution of capecitabine for FU/LV

N=459

PFS: Addition of oxaliplatin- HR 0.84, 95%CI 0.69-1.01, p=0.07

Capecitabine v FU- HR 0.99, 95%CI 0.82-1.20, p=0.93

OS: Addition of oxaliplatin- HR 0.99, 95%CI 0.81-1.18, p=0.91

Capecitabine v FU- HR 0.96, 95%CI 0.79-1.17, p=0.71

QOL: No difference in global QOL between FU and capecitabine arms

Tox: Highest rates of ≥G3 toxicity with OxCap (43%), lowest with FU

(27%); no increase in toxicity with addition of oxaliplatin; increased

risk of ≥G3 toxicity with capecitabine vs FU

Note: The addition of oxaliplatin to 5FU or capecitabine did not improve

PFS in elderly frail patients; substitution of 5FU with capecitabine did

not improve QOL.

Price et al, 2012

(292)

Subgroup analysis of patients ≥75yrs in

RCT of capecitabine v

bevacizumab/capecitabine v

bevacizumab/capecitabine/mitomycin C

(AGITG MAX study)

N=99 (≥75yrs) For addition of bevacizumab (CB/CBM v C)

PFS: In those <75yrs, HR 0.65, 95%CI 0.52-0.82, p<0.001

In those ≥75yrs, HR 0.45, 95%CI 0.29-0.69, p<0.001; p-interaction

=0.19

OS: In those <75yrs, HR 0.99, 95%CI 0.77-1.27, p=0.94

In those ≥75yrs, HR 0.79, 95%CI 0.50-1.24, p=0.31; p-interaction

=0.29

Note: Addition of bevacizumab to chemotherapy improved PFS in younger

and older age groups

Cassidy et al,

2010 (264)

Retrospective pooled analysis of older

patients enrolled in four phase II/III

RCTs of bevacizumab plus

chemotherapy, to determine efficacy and

toxicity of bevacizumab added to

chemotherapy in older patients

N=3007 (total)

N=1864 (<65yrs)

N=1142 (≥65yrs)

N=712 (≥70yrs)

PFS: <65yrs, PFS 9.5mths v 6.7mths, HR 0.59 (95%CI 0.52-0.66, p<0.0001)

≥65yrs, PFS 9.3mths v 6.9mths, HR 0.58 (95%CI 0.49-0.68, p<0.0001)

≥70yrs, PFS 9.2mths v 6.4mths, HR 0.54 (95%CI 0.44-0.66, p<0.0001)

OS: <65yrs, OS 19.9mths v 16.5mths, HR 0.77 (95%CI 0.69-0.86, p<0.0001)

≥65yrs, OS 17.9mths v 15.0mths, HR 0.85 (95%CI 0.74-0.97, p=0.015)

≥70yrs, OS 17.4mths v 14.1mths, HR 0.79 (95%CI 0.66-0.93, p=0.005)

113

Tox: Increased thromboembolic events with bevacizumab (arterial) in

patients ≥65yrs & ≥70yrs

Note: The benefit of bevacizumab to chemotherapy in OS and PFS was seen

across all age groups, with increased arterial thromboembolic events in

older patients.

Kozloff et al,

2010 (268)

Observational study subgroup analysis of

older patients treated with bevacizumab

based treatment first-line (BRiTE study),

to determine safety, PFS, OS, SBP, as

assessed by age group

N=1953 (total)

N=896 (≥65yrs)

PFS: Age <65yrs 9.8mths, 95%CI 9.2-10.3mths

Age 65 to 74yrs 9.6mths, 95%CI 9.0-10.2mths

Age 75 to 79yrs 10mths, 95%CI 8.5-10.5mths

Age ≥80yrs 8.6mths, 95%CI 7.5-9.9mths

OS: Age <65yrs 26mths, 95%CI 24.5-27.6mths

Age 65 to 74yrs 21.1mths, 95%CI 18.6-23.9mths

Age 75 to 79yrs 20.3mths, 95%CI 16.8-22.5mths

Age ≥80yrs 16.2mths, 95%CI 13.4-20.4mths

Safety: Increased arterial thromboembolic events with age

Feliu et al, 2010

(239)

Single arm phase II trial of capecitabine

+ bevacizumab in patients ≥70yrs

considered unsuitable for oxaliplatin or

irinotecan doublet

N=59 (≥70yrs) RR: 34% (95%CI 22.4-47.5mths)

PFS: 10.8mths (95%CI 7.6-14.1mths)

OS: 18mths (95%CI 9.6-26.3mths)

Tox: G3/4 toxicity in 54%; G3/4 PPE 19%; G3/4 diarrhoea 9%

Van Cutsem et

al, 2009 (269)

Expanded access trial of bevacizumab

added to chemotherapy 1st-line for

mCRC (The BEAT Study)

N=1914 (total)

N=1286 (<65yrs)

N=499 (65-74yrs)

N=129 (≥75yrs)

Tox: Similar toxicity across age groups

PFS: <65yrs v 65-74yrs v ≥75yrs: 10.8 v 11.2 v 10.0mths, p=NR

OS: <65yrs v 65-74yrs v ≥75yrs: 23.5 v 22.8 v 16.6mths, p=NR

Sastre et al,

2009 (256)

Subgroup analysis by age of phase III

RCT of continuous infusional 5FU +

oxaliplatin (FUOX) v XELOX first-line

N=348 (total)

N=109 (≥70yrs)

N=233 (<70yrs)

RR: Did not differ between age groups (P=0.081)

≥70yrs v <70yrs, 34.9% v 44.7%

TTP: Did not differ between age groups (P=0.114)

≥70yrs v <70yrs, 8.3mths v 9.6mths

OS: Did not differ between age groups (P=0.74)

≥70yrs v <70yrs, 16.8mths v 20.5mths

Tox: G3/4 diarrhoea higher in ≥70yrs receiving XELOX (25.0% v 8.1%,

P=0.005); higher rates of treatment discontinuation due to toxicity in

older patients (37% v 21%)

114

Kabbinavar et

al, 2009 (266)

Pooled analysis from two RCTs of

patients ≥65yrs receiving first-line

chemotherapy +/- bevacizumab

N=439 (≥65yrs) ORR: 34.4% v 29% (P=NS)

PFS: 9.2mths v 6.2mths, HR 0.52, 95%CI 0.40-0.67, P<0.001

OS: 19.3mths v 14.3mths, HR 0.70, 95%CI 0.55-0.90, P=0.006

Note: Patients ≥65yrs benefit from the addition of bevacizumab to

chemotherapy 1st-line

Folprecht et al,

2008 (235)

Pooled analysis from four phase III

RCTs of FU/FA +/- irinotecan as 1st-line

treatment in mCRC

N=2092 (total)

N=599 (≥70yrs)

RR: For those ≥70yrs: 50.5% v 30.3% (P<0.0001)

For those <70yrs: 46.5% v 29% (P<0.0001)

PFS: For those ≥70yrs: HR 0.75, 95%CI 0.61-0.90, P=0.0026

For those <70yrs: HR 0.77, 95%CI 0.70-0.85, P<0.0001

OS: For those ≥70yrs: HR 0.87, 95%CI 0.72-1.05, P=0.15

For those <70yrs: HR 0.83, 95%CI 0.75-0.92, P=0.0003

Tox: Similar across age groups; significantly more neutropenia,

leucopenia, diarrhoea, nausea and vomiting with irinotecan in both

age groups

Francois et al,

2008 (293)

Single arm phase II trial of FOLFIRI as

1st-line treatment in those ≥70yrs

N=40 (≥70yrs) RR: 40% (95%CI 25-55%)

PFS: 8mths (95%CI 6 to unreached)

OS: 17.2mths (95%CI 11.6-22.2mths)

Tox: G3/4 diarrhoea 15%

Arkenau et al,

2008 (253)

Subgroup analysis of phase III RCT of

FUFOX v CAPOX first-line

N=476 (total)

N=140 (≥70yrs)

RR: Age ≥70yrs v age <70yrs: 49% v 52% (P=NS)

PFS: Age ≥70rs v age <70yrs: 7.7mths v 7.5mths (P=NS)

OS: Age ≥70yrs v age <70yrs: 18.8mths v 14.4mths (P=0.013)

Tox: Greater gastrointestinal side effects in those ≥70yrs

Figer et al, 2007

(254)

Subgroup analysis of patients 76-80yrs

enrolled in OPTIMOX1 trial of 1st-line

continuous FOLFOX v six cycles

followed by maintenance 5FU

N=620 (total)

N=37 (76 to 80yrs)

ORR: Age <76yrs v age 76 to 80yrs: 59% v 59.4% (P=NS)

PFS: Age <76yrs v age 76 to 80yrs: 9 v 9mths (P=0.63)

OS: Age <76yrs v age 76 to 80yrs: 20.2 v 20.7mths (P=0.57)

Tox: > G3/4 toxicity in older pts (65 v 48%); neutropenia (41 v 24%),

neurotoxicity (22 v 11%)

Goldberg et al,

2006 (255)

Pooled analysis from four RCTs of

FOLFOX in adjuvant, 1st and 2nd line

N=3742

N=614 (≥70yrs)

Safety: ≥G3 haematological toxicity more common in those ≥70yrs; no

difference in GI or neuro toxicity between age groups

OS: For those ≥70yrs (+ oxaliplatin), HR 0.82, 95%CI 0.63-1.06

For those <70yrs (+ oxaliplatin), HR 0.77, 95%CI 0.67-0.88, P

interaction =0.79

115

Feliu et al, 2006

(258)

Single arm phase II study of XELOX 1st

line in patients ≥70yrs with mCRC

N=50 (≥70yrs) RR: 36% (95%CI 28-49%)

TTP: 5.8mths (95%CI 3.9-7.8mths)

OS: 13.2mths (95%CI 7.6-16.9mths)

Tox: G3/4 diarrhoea in 22%

Feliu et al, 2005

(238)

Single arm phase II study of capecitabine

in patients ≥70yrs, mCRC, inappropriate

for combination chemotherapy

N=51

RR: 24% (95%CI 15 to 41%); clinical benefit rate of 40% (of 35 patients)

OS: Median 11mths (95%CI 8.6-13.3mths)

PFS: Median 7mths (95%CI 6.4-9.5mths)

Tox: Only 6 patients (12%) had a G3-4 toxicity

D’Andre et al,

2005 (245)

Pooled analysis from 4 North Central

Cancer Treatment Group trials of 5-FU

+/- leucovorin for advanced CRC

N=371 (≤55yrs)

N=450 (56-65yrs)

N=354 (66-70yrs)

N=483 (>70yrs)

RR: Did not differ by age (p=0.90)

TTP: Did not differ by age (p=0.25)

OS: Did not differ by age (p=0.42)

Tox: Severe toxicity 46% v 53% (>65yrs v ≤65yrs, p=0.01); diarrhoea 16 v

21%, stomatitis 13 v 17%, infection 2 v 4%

Comella et al,

2005 (294)

Single arm phase II study of XELOX

1st-line in patients ≥70yrs

N=76 (≥70yrs) RR: 41% (95%CI 30 to 53%)

PFS: 8.5mths (95%CI 6.7-10.3mths)

OS: 14.4mths (95%CI 11.9-16.9mths)

Tox: G3/4 PPE 13%

Souglakos et al,

2005 (295)

Single arm, phase II study of FOLFIRI

1st line in patients ≥70yrs

N=30 (≥70yrs) RR: 36.5% (95%CI 26.6-48.4%)

TTP: 7mths

OS: 14.5mths

Tox: G3/4 neutropenia 20%; G3/4 diarrhoea 17%

Sastre et al,

2005 (296)

Single arm phase II trial of irinotecan +

5FU 1st line in patients ≥72yrs, of good

performance status and without geriatric

syndromes

N=85 (≥72yrs) RR: 35% (95%CI 25%-46%)

TTP: 8mths (95%CI 6.0-10.0mths)

OS: 15.3mths (95%CI 13.8-16.9mths)

Tox: G3/4 neutropenia 21%; G3/4 diarrhoea 17%

Folprecht et al,

2004 (223)

Retrospective pooled analysis of data

from 22 palliative chemotherapy trials

using 5FU-based chemotherapy

N=3825

N=629 (≥70yrs)

OS: No difference between age groups (p=0.31); ≥70yrs v <70yrs, 10.8mths

v 11.3mths

PFS: Better PFS in older patients (p=0.01); ≥70yrs v <70yrs, 5.5mths v

5.3mths

RR: Equivalent across age groups (≥70yrs 23.9%, 21.1% <70yrs)

Abbreviations: mCRC – metastatic colorectal cancer; ORR – overall response rate; PFS – progression free survival; OS – overall survival; RR – response rate; HFS – hand-foot syndrome;

BRiTE – Bevacizumab Regimens: investigation of treatment effect and safety; SBP – survival beyond progression; TTP – time to progression; FOLFOX – 5FU/LV + oxaliplatin; XELOX –

capecitabine + oxaliplatin; FUFOX – 5FU/LV + oxaliplatin; CAPOX – capecitabine +oxaliplatin; FOLFIRI – 5FU/LV + irinotecan; NR – not reported

116

Figure 1. Key factors influencing systemic treatment decisions for older adults with

colon cancer

117

5. How do oncologists make decisions about

chemotherapy for their older patients with cancer?

A survey of Australian Oncologists

5.1 Overview

This chapter is a published work that is aimed at a general oncology clinical audience. Factors

influencing oncologists’ recommendations about treatment with chemotherapy for their older

patients and methods of clinical assessment used to guide treatment decisions are evaluated.

Using hypothetical scenarios, the effect of age and risk of severe treatment toxicity on the

likelihood to recommend chemotherapy in both the adjuvant and palliative settings are also

explored. The published manuscript is quoted verbatim.

Publication details

Moth EB, Kiely BE, Naganathan V, Martin A, Blinman P. How do oncologists make

decisions about chemotherapy for their older patients with cancer? A survey of Australian

oncologists. Support Care Cancer, 2018. 26(2): 451-460.

Contribution of authors

I, Dr Erin Moth, contributed to the study conception and study design, was responsible for data

acquisition and control, data analysis and interpretation, preparation of the draft manuscript,

manuscript editing and revision.

Dr Belinda Kiely contributed to the study conception and design, data analysis and

interpretation, manuscript editing and revision.

118

Prof Vasi Naganathan contributed to the study conception and design, data analysis and

interpretation, manuscript editing and revision.

A/Prof Andrew Martin contributed to the study conception and design, data control and

algorithms, data analysis and interpretation, manuscript editing and revision.

Dr Prunella Blinman contributed to the study conception and design, data analysis and

interpretation, manuscript editing and revision.

119

5.2 Abstract

Purpose

Oncologists are making treatment decisions on increasing numbers of older patients with

cancer. Due to comorbidities and frailty that increase with age, such decisions are often

complex. We determined factors influencing oncologists’ decisions to prescribe chemotherapy

for older adults.

Methods

Members of the Medical Oncology Group of Australia (MOGA) were invited to complete an

online survey in February to April 2016.

Results

93 oncologists completed the survey of which 69 (74%) were consultants and 24 (26%) were

trainees, with most (72, 77%) working predominantly in a public hospital associated practice.

The 3 highest ranked factors influencing decisions about (i) adjuvant chemotherapy were

performance status, survival benefit of treatment, and life expectancy in the absence of cancer,

and (ii) palliative chemotherapy were performance status, patient preference, and quality of

life. Most geriatric health domains are reportedly assessed routinely by the majority of

respondents, though few routinely use geriatric screening tools (14%) or geriatric assessments

(5%). In hypothetical patient scenarios, oncologists were less likely to prescribe palliative and

adjuvant chemotherapy as age and rates of severe toxicity increased.

Conclusion

Performance status was the most influential factor for oncologists when making a decision

about chemotherapy for their older patients, and the importance of other factors differed

120

according to treatment intent. Oncologists were less likely to recommend chemotherapy as

patient age and treatment toxicity increased. The low uptake of geriatric assessments or

screening tools provides scope for improved clinical assessment of older adults in treatment

decision-making.

5.3 Introduction

The most consistent determinant of an older adult’s decision to accept or decline chemotherapy

is their oncologist’s recommendation. (101) Little is known, however, about how oncologists

make decisions about chemotherapy for their older patients. Treatment decisions in this setting

are complex, with the need to consider comorbidities, frailty, benefits and toxicity of treatment,

and patients’ treatment preferences. There is also limited clinical trial evidence in this

population to guide treatment as many trials have excluded older patients. (201, 297)

Chemotherapy use declines with increasing age in a number of cancer types and treatment

settings (121, 123, 125, 126, 298-301) providing good rationale to explore how oncologists

make decisions about chemotherapy for their older patients.

Published studies, varying in methodology, have sought to determine factors considered by

oncologists when making decisions about chemotherapy for their older patients. Observational

studies in early (302) and late-stage (121) breast cancer have asked oncologists for reasons for

their recommendation either for or against chemotherapy, and for regimen choice. Factors

frequently considered were perceived limited benefits, comorbidity, frailty, quality-of-life,

(302) age and health status. (121) Studies that asked oncologists which factors most influence

their chemotherapy recommendation have found patient performance status the most

frequently influential factor. (69, 110, 119, 120) Retrospective database reviews, (123-127)

predominantly in the setting of early-stage colon cancer, (123-125) have evaluated predictors

121

of treatment choice and have found that patient age and comorbidity predict whether patients

received chemotherapy, though the scope of factors evaluable retrospectively is a major

limitation. Survey studies using hypothetical patient scenarios, (32, 109-115, 117)

predominantly in early breast cancer, (32, 109-111, 114) have identified advancing patient age,

(32, 110-115, 117) comorbidity, (32, 111, 113-115) and patient preference (113, 117) as

predictors of receipt of chemotherapy and regimen type. Only one of these studies addressed

palliative treatment decisions. (117) There is little data about the utilisation of comprehensive

geriatric assessments or geriatric assessment tools and the influence of chemotherapy toxicity

on oncologists’ treatment decisions, despite the recommendation for the use of geriatric

assessment in routine practice (78) and the development of predictive tools that estimate the

likelihood of chemotherapy toxicity for older adults. (48, 49, 68)

We aimed to determine factors influencing oncologists’ decisions about chemotherapy for their

older patients in both adjuvant and palliative settings. We sought to determine: (i) the

importance of patient and clinical factors in decision-making; (ii) methods used to assess older

patients’ suitability for chemotherapy; (iii) oncologists’ attitudes towards decision-making;

and, (iv) using hypothetical scenarios, the effect of age and expected rates of chemotherapy

toxicity on the likelihood of recommending treatment.

5.4. Methods

5.4.1 Survey distribution

Medical Oncology Group of Australia (MOGA) members (450 consultants, 174 trainees) were

invited to complete an online survey from February, 2016. A reminder email was sent 4 weeks

later, the survey remaining open for 3 months. Completion of the anonymous survey

constituted consent to participate. Surveys returned by oncologists whose clinical practice

122

comprised at least 10% of patients ≥65 years were included. The study was approved by Sydney

Local Health District’s Human Research Ethics Committee of Concord Repatriation General

Hospital (CH62/6/2015-226).

5.4.2 Survey

The survey was designed to collect information about oncologists’ approaches to decision-

making (Appendix A). The importance of fourteen pre-specified clinical factors in making a

decision about treating an older adult with (i) palliative chemotherapy for an advanced cancer,

and (ii) adjuvant chemotherapy for an early cancer, was assessed on a 5-point Likert scale.

Respondents then ranked the three most influential factors. Attitudes towards decision-making

were evaluated by asking respondents to what extent they agreed (on a 5-point Likert scale)

with six statements about the decision-making process. Respondents were asked how they

defined “an older adult with cancer”, and whether they thought there was an age above which

adjuvant or palliative chemotherapy should generally not be considered. Respondents were

asked which clinical assessments they routinely perform (>50% of the time), and how they

assess individual health domains: formally using validated tools, or informally. An informal

assessment was defined as an evaluation using clinical judgement based on history and

examination, without using an objective clinical tool.

Hypothetical scenarios were used to determine the likelihood of oncologists recommending

palliative and adjuvant chemotherapy according to age and risk of severe (grade 3-5)

chemotherapy toxicity. The advanced cancer scenario described a patient with a symptomatic

incurable cancer for which palliative chemotherapy had a response rate of 40% and a 3-month

absolute improvement in median overall survival (OS) (from 6 to 9 months). The early cancer

scenario described the patient as having a resected early-stage cancer for which the 5-year OS

123

rate was 70%, with adjuvant chemotherapy reducing the risk of recurrence by 25% (from 40%

to 30%), giving an absolute improvement in 5-year OS of 5% (from 70 to 75%). Patient factors

common to both scenarios were: Eastern Cooperative Oncology Group Performance Status of

1, independence in basic and instrumental activities of daily living, adequate social supports,

minor comorbidity, and willingness to be guided by their oncologist regarding treatment. For

each scenario, respondents were asked to rate on a 5-point Likert scale how likely they would

be to recommend chemotherapy if the patient were aged 70, 75, 80, and ≥85 years if the

probability of severe toxicity were (i) 10%, and (ii) 40% (16 decision-making scenarios in total,

8 in each treatment setting).

5.4.3 Statistical analysis

A mean importance score for each pre-specified clinical factor was derived based on Likert

scale values (0 being “not at all important”, 5 being “very important”). The frequency of each

factor being ranked first, second, and third was calculated. These frequencies were multiplied

by a weight (wi=4-i, where i=rank position 1, 2, or 3), then summed to form a rank score.

The proportion of oncologists ‘likely’ or ‘very likely’ to recommend chemotherapy across the

16 hypothetical scenarios were summarised by patient age groupings, risk of toxicity, and

setting. The frequency of selecting a given likelihood category was presented using diverging

stacked bar charts (each bar centred at the midpoint of the ‘neutral’ category). The association

between patient age and toxicity risk on the likelihood to recommend chemotherapy was

quantified for each setting using logistic regression fitted using generalised estimating

equations to account for correlations among responses from an individual respondent. This

approach was also used to test the following physician factors as predictors for chemotherapy

recommendation: physician age, consultant or trainee, sex, years of experience, proportion of

124

practice ≥65 years or ≥80 years, and how they defined an “older adult with cancer”. Odds ratios

for each factor represent the odds of a chemotherapy recommendation (‘likely’/‘very likely’

relative to ‘neutral’/‘unlikely’/‘very unlikely’).

We sought 125 completed surveys to provide reasonable precision of estimated proportions

(i.e, 95% confidence intervals extending no more than +/- <9% from point estimates).

5.5. Results

Ninety-three surveys were returned for a response rate of 15%, all meeting criterion for

inclusion. Table 1 outlines the characteristics of respondents. About half were female (55%),

and most were consultants (74%) rather than trainees (26%) working mainly in public hospital

practices (77%). Most (53%) were aged 20-39 years, with few (8%) ≥60 years. The study

sample was representative of the MOGA membership with respect to available data on age,

sex, and level of training.

Table 2 shows the importance ranking of chemotherapy decision-making factors. The 3 factors

with the greatest overall rank score in the adjuvant setting were (in order): performance status,

survival benefit of treatment, and life expectancy in the absence of cancer. In the palliative

setting these were: performance status, patient preference, and quality-of-life.

Figure A (supplementary) shows the mean importance rating of chemotherapy decision-

making factors. Other than for age in the palliative setting, the mean importance rating of all

factors was ≥3 (at least moderately important). Performance status had the highest mean

importance rating in both treatment settings (rated ‘very important’ by 94% of respondents in

the adjuvant setting and 97% in the palliative setting). Age had the lowest mean importance

125

rating in both settings (rated ‘important’ or ‘very important’ by 41% of respondents in the

adjuvant setting and 26% in the palliative setting).

An “older adult with cancer” was most frequently defined as ≥75 years (48%). Almost half of

oncologists (37, 45%) agreed with an upper age limit (an age above which chemotherapy

should generally not be considered) for adjuvant chemotherapy. Age limits proposed by these

37 oncologists were >85 (n=20), >80 (n=14) and >75 years (n=3). Fewer oncologists (18, 22%)

agreed with an upper age limit for palliative chemotherapy, with proposed age limits being >85

(n=12), >80 (n=5), and >70 years (n=1).

Methods of assessment of older patients are presented in Figure 1. Only a minority of

respondents routinely use geriatric screening tools (14%) or a geriatric assessment (5%). When

asked how they assess functional status, nutrition, cognition, and psychological state, most

reported assessing these domains ‘informally’ using clinical judgement (functional status 87%,

nutrition 70%, cognition 77%, psychological state 87%), rather than with specific assessment

tools (functional status 12%, nutrition 13%, cognition 19%, psychological state 7%).

Figure 2 presents respondents’ attitudes to chemotherapy decision-making for older adults.

Most (88%) agreed they could adequately assess which older adults were suitable for

chemotherapy, but only half (52%) were confident in predicting who was likely to experience

chemotherapy-related toxicity. Most (93%) agreed that a clinical tool predictive of

chemotherapy toxicity would be useful. A majority (71%) agreed there is a role for geriatricians

in the management of older adults with cancer, but less than half (46%) agreed geriatricians

should play a role in cancer-specific treatment decision-making.

126

Figure B (supplementary) presents the likelihood of oncologists recommending chemotherapy

according to patient age and treatment toxicity for each setting. The likelihood of a positive

chemotherapy recommendation (‘very likely’ or ‘likely’) as a function of patient age and

toxicity risk for each setting are presented in Figure 3. In each setting, patient age was a

significant predictor (p<0.001) of chemotherapy recommendation adjusting for toxicity risk.

In the palliative setting, relative to a 70-year-old patient, the adjusted odds of recommending

chemotherapy for a 75-year-old was 0.35 (95%CI 0.24-0.52), for an 80-year-old 0.08 (95%CI

0.04-0.14), and for a patient ≥85 years 0.02 (95%CI 0.01-0.04). In the adjuvant setting, relative

to a 70-year-old patient, the adjusted odds of recommending chemotherapy for a 75-year-old

was 0.28 (95%CI 0.19-0.40), for an 80-year-old 0.06 (95%CI 0.03-0.10), and for a patient ≥85

years 0.01 (95%CI 0.01-0.03). In each setting, toxicity risk was a significant predictor

(p<0.001) of chemotherapy recommendation adjusting for patient age. In the palliative setting,

relative to a chemotherapy regimen with a toxicity risk of 10%, the adjusted odds of

recommending chemotherapy with a toxicity risk of 40% was 0.06 (95%CI 0.03-0.10). In the

adjuvant setting this odds ratio was 0.14 (95%CI 0.09-0.22).

In the palliative setting, there were no respondent characteristics predictive of chemotherapy

recommendation (Table 3). In the adjuvant setting, oncologists with a higher proportion of their

practice aged ≥80 years (≥10% vs <10%) were more likely to recommend chemotherapy on

univariate analysis, and this remained predictive after adjusting for age and toxicity risk (OR

2.43, 95%CI 1.09-5.42, p=0.03).

5.6. Discussion

In our study, performance status was the most important consideration influencing oncologists’

decisions about chemotherapy, whereas chronological age was one of the least important

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factors. An “older adult with cancer” was most commonly defined as ≥75 years, with an upper

age limit agreed on more frequently for adjuvant rather than palliative chemotherapy.

Oncologists make an assessment of most geriatric health domains, but rarely use formal

geriatric assessments or screening tools. Oncologists were less likely to recommend

chemotherapy as patient age and risk of treatment toxicity increased.

The emphasis on performance status to guide the treatment decisions of oncologists in our

study is consistent with other studies of older adults. (110, 120) Reasons for this include

oncologists’ experience with performance status as a quick and easy assessment, a commonly

used clinical trial enrolment criterion, and its association with patient outcomes. (152) There

is, however, a trend away from relying solely on performance status in older adults with cancer.

It only provides a summative measure of patient function, failing to capture other factors

important in treatment decision-making, such as comorbidity, nutrition, social supports, and

more subtle deficits in physical functioning. (86)

Almost half of oncologists agreed with age limits for adjuvant chemotherapy, albeit fewer for

palliative chemotherapy, despite age rating as the least important factor influencing

chemotherapy decision-making. The reason for less oncologists agreeing with an age limit for

palliative chemotherapy is likely because palliative chemotherapy is predominantly given to

improve symptoms and quality-of-life. Its benefits are immediate and so it is often considered

at any age. In contrast, adjuvant chemotherapy reduces the risk of future recurrence in a patient

without cancer symptoms, at the cost of toxicity and reduced quality-of-life. In this setting,

patients need to have adequate estimated life expectancy to make short-term toxicities

worthwhile. Chronological age is a predictor of cancer treatment choice (32, 110-115, 123,

124, 303) and treatments received, (121, 125-127, 298, 299, 301) but does not always correlate

128

with physiological age, (139) which is more informative for treatment decision-making.

Oncologists agreeing with age limits for chemotherapy may be making associations between

chronological age and other factors such as comorbidities or reduced physiological reserves,

or limited life expectancy and unclear survival benefits from treatment. This may be

particularly the case for adjuvant chemotherapy, where life expectancy and survival benefits

from treatment were ranked as highly influential factors. Chronological age having the lowest

importance rating for chemotherapy decision-making in our study is reassuring, and suggests

that oncologists consider a range of factors other than age alone when assessing older adults

for chemotherapy.

Few oncologists reported routinely using geriatric assessments and screening tools, consistent

with other studies. (69, 304) Despite the potential benefits of a geriatric assessment (64, 78,

82) and its incorporation into international oncology guidelines, (47, 78) the best model for its

implementation into local practice is unknown. Additionally, its role in decision-making about

chemotherapy remains unclear, likely explaining the low uptake in our study. The strongest

evidence for the use of a geriatric assessment in oncology is in identifying geriatric problems

not otherwise recognised. (78, 92, 280) There is inconsistency across studies for the ability of

the geriatric assessment or its component parts to predict mortality and treatment toxicity. (88,

92) Impaired instrumental activities of daily living, poor performance status, and number of

deficits are the most consistent predictors of mortality from a geriatric assessment, (82)

however consistent individual predictors for toxicity are lacking. (82, 92) Further reasons for

the low use of geriatric assessments or tools in our study include a lack of awareness, doubts

about benefits to patient care, lack of time, and confidence in clinical acumen and care

otherwise provided to patients. Most respondents (88%) in our study were confident in

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assessing an older patient’s suitability for chemotherapy, and, as such, may not value additional

assessments.

Only 52% of respondents were confident in predicting chemotherapy-related toxicity and most

(93%) agreed that a clinical tool predictive for toxicity would be useful. Such tools have been

developed in the research setting, (48, 49, 68) but are not yet widely used in practice. The

Cancer and Aging Research Group’s (CARG) Toxicity Score, (48, 49) for example, is an 11-

item validated predictive model using clinical and geriatric assessment variables to classify a

patient as low, medium, or high-risk of experiencing severe (grade 3-5) chemotherapy toxicity.

Another example, the CRASH Score, (68) provides separate models for haematological and

non-haematological toxicity. The results of our study suggest that oncologists underestimate

the actual risk of severe chemotherapy toxicity in practice, and may benefit from the use of

predictive toxicity tools if these were validated locally. To illustrate, actual rates of severe

chemotherapy toxicity in prospective studies of older adults with solid organ cancers is about

50%. (49, 68) In our study, however, few respondents were likely to recommend chemotherapy

in a hypothetical situation when the rate of severe toxicity was lower than this (i.e., 40%). The

proportion of oncologists likely to recommend chemotherapy to a 75-year-old fell from 59%

to 19% in the adjuvant scenario and from 76% to 18% in the palliative scenario as the expected

rate of severe toxicity increased from 10% to 40%.

The likelihood of recommending chemotherapy in both the adjuvant and palliative scenarios

decreased with increasing patient age as in other studies. (32, 110-115, 117, 305) Naimh et al,

(111) for example, surveyed 151 oncologists regarding the treatment of older women with

early-stage breast cancer using hypothetical scenarios that varied by patient age (70, 75, 80,

and 85yrs) and health status (good, average, or poor). Both age and health status predicted

130

treatment recommendations (P<0.0001 for both). Keating et al (115) surveyed 1096

oncologists about chemotherapy for stage III colon cancer using hypothetical scenarios that

varied by patient age (55 v 80yrs) and comorbidity (none, moderate, or severe congestive

cardiac failure). Both age and comorbidity were strong predictors of treatment

recommendation. It is important to consider that patient age has been one of at most two factors

varied and tested in previous studies using hypothetical scenarios, as in our study, and so it is

difficult to conclude that respondents in these types of studies would not consider other patient

or clinical factors more important. For example, in our study, oncologists may have related

increasing chronological age with patient characteristics not described in the scenarios, such as

reduced physiological reserves or altered pharmacokinetics, or placed greater significance on

the described minor comorbidities as the hypothetical patient’s age increased. As such,

concluding that oncologists focus singularly on chronological age from this and other similar

studies may not be entirely reasonable.

The only respondent characteristic predictive of chemotherapy recommendation was a clinical

practice with a higher proportion of patients aged ≥80 years for the adjuvant scenario.

Chemotherapy has previously been shown to be more likely recommended by oncologists with

high volume practices, (111, 114) in teaching roles or large cancer centres. (115) It is likely,

however, that there are oncologist factors that influence treatment recommendations beyond

demographics and that are difficult to measure, such as comfort with risk, individual age-

related bias, temperament, and the oncologist’s own preference for chemotherapy.

Our study adds to existing knowledge on the complexities of chemotherapy decision-making

for older adults. Strengths include the novel data on how chemotherapy toxicity influences

oncologists’ recommendations. The use of hypothetical scenarios with unspecified tumour-

131

types allowed for responses not limited by subspecialty knowledge, and focussed the survey

on the general principles of chemotherapy treatment decision-making to make it broadly

applicable. It should be emphasised that this study evaluated treatment decision-making only

with regard to chemotherapy, and that oncologists’ attitudes towards recommending

immunotherapies and targetted agents may differ, particularly given their substantially

different toxicity profiles and potential to be costly. A lower response rate than expected was

a limitation of our study. Though comparable to other local surveys of oncologists (306) and

fairly typical of non-incentivised physician surveys, (307, 308) the low response rate reduced

our power to detect significant predictors of chemotherapy recommendation. As is

representative of the MOGA membership, most of our respondents were aged under 40. It is

possible older oncologists make decisions about older patients differently. Responder drop-out

was also evident, with 84 of the 93 respondents completing all questions. Generalisability of

the survey is likely limited by the majority of respondents practicing in the public system,

meaning our study population was not entirely representative of all Australian oncologists.

(309) We used pre-specified clinical factors for the rating and ranking questions and evaluated

only two factors in the hypothetical scenarios to maintain focus and reduce respondent burden.

In reality, decision-making is complex, dynamic, and individualised, and there are many more

factors affecting treatment recommendation. Responses to hypothetical scenarios may also not

be truly reflective of actual clinical practice.

Clinical implications of our study are that decisions about chemotherapy for older adults are a

complex interplay of various factors, including patient age and risk of treatment toxicity. This

raises the question about whether oncologists should consider using a geriatric assessment and

available validated assessment tools to improve their evaluation of older patients and optimise

their decision-making, though definitive proof that taking this approach improves patient

132

outcomes is awaited. Research implications include the exploration of older adults’ decision-

making priorities and preferences for chemotherapy, the evaluation of the impact of geriatric

assessment measures over and above routine clinical evaluation on treatment decision-making,

and strategies to implement geriatric assessment in routine clinical practice. Further work to

increase the representation of older adults in clinical trials is also warranted, since variations in

treatment for older adults will likely continue so long as there is minimal evidence to guide

optimal treatment.

5.7 Conclusion

Performance status was the predominant factor influencing chemotherapy decision-making for

older adults with cancer of any stage, and recommendations for adjuvant and palliative

chemotherapy decreased as age and toxicity increased. The limited uptake of formal

instruments or geriatric assessments provides scope for improvement in the routine clinical

assessment of older adults with cancer.

133

Table 1. Participant demographics and clinical practice

Characteristic

Survey

Respondents

n (%)

MOGA

Membership ¶

n (%)

Sex * Male

Female

41 (44)

51 (55)

343 (55)

281 (45)

Position Consultant

Trainee

69 (74)

24 (26)

450 (72)

174 (28)

Age 20 – 39 years

40 – 59 years

60+ years

49 (53)

37 (40)

7 (8)

302 (50)

236 (39)

61 (10)

Years of experience † 1 to 5 years

6 to 10 years

10 to 20 years

>20 years

31 (33)

26 (28)

14 (15)

20 (22)

Practice type Mostly public

Mostly private

Equal public and private

Other (eg locum work)

72 (77)

7 (8)

11 (12)

3 (3)

New patients seen each year (mean) 158

Cancer types treated ‡ Breast

Lung / thoracic

Colorectal

Genitourinary

Upper GIT

Neurological

Gynaecological

Head and neck

Melanoma

Sarcoma

Other

60 (65)

51 (55)

53 (57)

39 (42)

36 (39)

13 (14)

25 (27)

17 (18)

17 (18)

7 (8)

3 (3)

Proportion of practice ≥65yrs § <10%

10 to 25%

26 to 50%

51 to 75%

>75%

0 (0)

5 (5)

31 (33)

45 (48)

9 (10)

Proportion of practice ≥80yrs ‖ <10%

10 to 25%

26 to 50%

51 to 75%

>75%

33 (35)

49 (53)

6 (6)

1 (1)

0 (0)

Definition of “an older adult with

cancer” †

65 years and older

70 years and older

75 years and older

80 years and older

2 (2)

33 (35)

45 (48)

11 (12)

*1 missing response; † 2 missing responses; ‡ more than one cancer type could be

selected; § 3 missing responses; ‖ 4 missing responses; ¶ Demographic data accessed from

MOGA Membership database at 624 members, 2016; age missing for 25 members

134

Table 2. Ranking of factors important in decision-making about chemotherapy

Adjuvant setting Palliative setting

Rankings by oncologists* (number) Overall Rank Score** Rankings by oncologists* (number) Overall Rank Score**

Factor Ranked

1st

Ranked

2nd

Ranked

3rd

Ranked 1st Ranked

2nd

Ranked

3rd

Performance status 28 14 8 120 (1) 47 13 8 175 (1)

Survival benefit with

treatment 12 21 20 98 (2) 0 5 6 16

Life expectancy in the absence

of cancer 16 12 15 87 (3) 2 3 9 21

Patient preference 8 8 7 47 10 13 11 67 (2)

Quality of life 1 5 3 16 9 10 13 60 (3)

Functional status 4 4 5 25 12 8 7 59

Comorbidities 3 9 6 33 2 15 10 46

Cancer type 8 5 5 39 4 4 5 25

Cancer related symptoms - - - - 3 7 8 31

Treatment toxicity 3 5 9 28 3 5 8 27

Cognition 1 5 4 17 1 5 6 19

Age 2 0 4 10 0 0 2 2

Burden of disease - - - - 0 2 1 5

Social supports 0 0 0 0 0 0 2 2

* Represents the number of oncologists ranking the nominated factor as 1st, 2nd, or 3rd in degree of importance in treatment decision-making

** Each rank was assigned a weight (a rank of 1st was assigned 3 points, 2nd = 2 points, 3rd = 1 point), with frequencies multiplied by these weights and then summed to

form an overall rank score for each factor

135

Table 3. Predictors of chemotherapy recommendation in hypothetical scenarios

Palliative scenario Adjuvant scenario

Univariate analysis Multivariate analysis Univariate analysis Multivariate analysis

Potential predictor Odds ratio p-value Odds ratio p-value Odds ratio p-value Odds ratio p-value

Age of patient

70 years

75 years

80 years

≥85 years

1

0.51 (0.41 to 0.64)

0.18 (0.13 to 0.25)

0.05 (0.03 to 0.09)

<0.001

1

0.35 (0.24 to 0.52)

0.08 (0.04 to 0.14)

0.02 (0.01 to 0.04)

<0.001

1

0.36 (0.27 to 0.47)

0.09 (0.06 to 0.14)

0.02 (0.01 to 0.05)

<0.001

1

0.25 (0.17 to 0.37)

0.05 (0.02 to 0.09)

0.01 (0.005 to 0.03)

<0.001

Toxicity of regimen

10%

40%

1

0.13 (0.09 to 0.18)

<0.001

1

0.06 (0.03 to 0.10)

<0.001

1

0.26 (0.20 to 0.34)

<0.001

1

0.13 (0.08 to 0.20)

<0.001

Sex

Male

Female

1

1.20 (0.82 to 1.77)

0.4

-

1

0.96 (0.61 to 1.51)

0.9

-

Position

Consultant

Trainee

1

1.47 (0.87 to 2.49)

0.1

-

1

0.66 (0.40 to 1.09)

0.1

-

Age of physician

20 to 39 years

40+ years

1

0.82 (0.55 to 1.22)

0.3

-

1

1.08 (0.68 to 1.71)

0.7

-

Years of experience

1 to 10 years

>10 years

1

0.77 (0.52 to 1.14)

0.2

-

1

0.93 (0.58 to 1.47)

0.7

-

Practice ≥65yrs

<50%

≥50%

1

1.45 (0.93 to 2.24)

0.1

-

1

1.50 (0.93 to 2.41)

0.1

-

Practice ≥80yrs

<10%

≥10%

1

1.31 (0.84 to 2.05)

0.2

-

1

1.65 (1.02 to 2.67)

0.04

1

2.43 (1.09 to 5.42)

0.03

Definition of “older adult”

>65 or >70yrs

>75 or >80yrs

1

1.12 (0.73 to 1.71)

0.6

-

1

1.32 (0.80 to 2.18)

0.3

-

Odds ratio: the odds of being likely or very likely to recommend chemotherapy

Multivariate analysis: only predictors found to be significant on univariate analysis included in multivariate analysis

136

Figure 1. Assessments routinely performed by oncologists for older adults with

cancer. Oncologists were asked which of the above clinical assessments they perform

routinely (>50% of the time) in evaluating an older adult with cancer.

1

5

14

45

64

70

88

93

96

98

99

100

0 20 40 60 80 100

Consult with a geriatrician

Use a geriatric assessment

Use a geriatric screening tool

Enquire about falls

Assess nutrition

Assess psychological state

Assess cognition

Assess number of medications

Assess functional status

Assess social supports

Assess performance status

History and physical examination

% of oncologists routinely performing assessment

137

Figure 2. Attitudes towards chemotherapy decision-making. Oncologists were asked to rate on a 5-point Likert scale the extent to which

they agreed with the above statements concerning decision-making about chemotherapy for older adults. The stacked bar graph shows the

proportion of respondants answering in each response category.

11

1

1

29

8

17

1

18

4

33

20

36

6

29

8

25

57

36

60

45

71

2

14

11

33

7

17

0% 20% 40% 60% 80% 100%

My clinical practice has adequate access to a geriatric medicine service

There is a role for geriatricians in the management of older adults withcancer

There is a role for geriatricians in treatment decision-making for older adultswith cancer

A clinical tool that predicts the likelihood of significant chemotherapy-relatedtoxicity in older adults would be useful

I am able to predict which older patients are likely to experience toxicity fromchemotherapy

I am able to assess an older patient's suitability for chemotherapy

Proportion of surveyed oncologists (%)

Strongly disagree Disagree Neither agree nor disagree Agree Strongly agree

138

A. B.

C.

Figure 3. Relationship between chemotherapy recommendation, age, treatment

toxicity, and setting.

A. Chemotherapy recommendation according to age and toxicity in the palliative scenario

B. Chemotherapy recommendation according to age and toxicity in the adjuvant scenario

C. Chemotherapy recommendation for all ages by treatment setting and toxicity

0102030405060708090

100

70 years 75 years 80 years ≥85 years

% o

nco

logi

sts

reco

mm

end

ing

chem

oth

erap

y

Hypothetical patient age

10% rate of severe toxicity

40% rate of severe toxicity

0102030405060708090

100

70 years 75 years 80 years ≥85 years

% o

nco

logi

sts

reco

mm

end

ing

chem

oth

erap

y

Hypothetical patient age

10% rate of severe toxicity

40% rate of severe toxicity

0

10

20

30

40

50

60

10% expected rate ofsevere toxicity

40% expected rate ofsevere toxicity

Pro

po

rtio

n o

f o

nco

logi

sts

(%)

reco

mm

end

ing

chem

oth

erap

y

Adjuvant setting Palliative setting

139

Supplementary Figure A. Mean importance rating of factors influencing

chemotherapy prescribing for older adults in the curative and palliative settings.

Oncologists were asked to rate the importance of pre-specified patient and clinical factors on

their decision-making about chemotherapy on a 5-point Likert scale, for both the adjuvant or

palliative setting. The bar chart represents the mean importance rating for each factor.

0 1 2 3 4 5

Age

Social supports

Survival benefit with treatment

Cancer type

Cancer related symptoms*

Burden of disease*

Life expectancy in the absence of cancer

Cognition

Patient preference

Treatment toxicity

Comorbidities

Functional status

Quality of life

Performance status

Mean score on Likert scalewhere 0=not at all important, 5=very important

*Not included for the adjuvant scenario, given no active cancer.

Adjuvant Palliative

140

A. Likelihood of prescribing palliative chemotherapy B. Likelihood of prescribing palliative chemotherapy

where the expected rate of severe toxicity is 10% where the expected rate of severe toxicity is 40%

C. Likelihood of prescribing adjuvant chemotherapy D. Likelihood of prescribing adjuvant chemotherapy

where the expected rate of severe toxicity is 10% where the expected rate of severe toxicity is 40%

Supplementary Figure B. Likelihood of prescribing chemotherapy according to age in each treatment setting

Note: Horizontal bars are centred at the midpoint of the ‘neutral’ category.

24

6

1

32

21

4

1

30

27

19

7

11

39

50

46

4

6

26

45

≥85 years

80 years

75 years

70 years

Proportion of oncologists (%)Very unlikely Unlikely Neutral Likely Very likely

61

46

20

5

32

37

35

25

6

13

27

35

4

14

40

1

4

5

≥85 years

80 years

75 years

70 years

Proportion of oncologists (%)

Very unlikely Unlikely Neutral Likely Very likely

37

14

7

31

34

6

2

24

28

28

13

7

20

37

46

4

22

39

≥85 years

80 years

75 years

70 years

Proportion of oncologists (%)

Very unlikely Unlikely Neutral Likely Very likely

58

43

21

8

35

42

31

20

7

12

29

27

4

15

33

4

11

≥85 years

80 years

75 years

70 years

Proportion of oncologists (%)

Very unlikely Unlikely Neutral Likely Very likely

141

6. Predicting chemotherapy toxicity in older adults:

comparing the predictive value of the CARG

Toxicity Score with oncologists’ estimates of

toxicity based on clinical judgement

6.1 Overview

This chapter is a published work that is aimed at a general oncology clinical audience. The

value of an existing chemotherapy toxicity risk prediction tool (the Cancer and Aging Research

Group’s Toxicity Score) (49) is evaluated in a local population, and compared to the value of

oncologists’ estimates of the likelihood of chemotherapy toxicity based on clinical judgement.

The published manuscript is quoted verbatim.

Publication details

Moth EB, Kiely BE, Stefanic N, Naganathan V, Martin A, Grimison P, Stockler MR, Beale

P, Blinman P. Predicting chemotherapy toxicity in older adults: Comparing the predictive

value of the CARG Toxicity Score with oncologists' estimates of toxicity based on clinical

judgement. J Geriatr Oncol, 2018 10(2): 202-209.

Contribution of authors

I, Dr Erin Moth, contributed to study conception and design, was responsible for ethics

submissions, data acquisition, data analysis and interpretation of results, preparation of the draft

manuscript, manuscript editing and revision.

Dr Belinda Kiely contributed to study conception and design, interpretation of results,

manuscript editing and revision.

142

Dr Natalie Stefanic contributed to study design, data acquisition, interpretation of results,

manuscript editing and revision.

Prof Vasi Naganathan contributed to study conception and design, interpretation of results,

manuscript editing and revision.

A/Prof Andrew Martin contributed to study conception and design, data analysis and

interpretation of results, manuscript editing and revision.

A/Prof Peter Grimison contributed to study design, interpretation of results, manuscript editing

and revision.

Prof Martin Stockler contributed to study design, interpretation of results, manuscript editing

and revision.

A/Prof Philip Beale contributed to study design, interpretation of results, manuscript editing

and revision.

Dr Prunella Blinman contributed to study conception and design, interpretation of results,

manuscript editing and revision.

Acknowledgement of funding

This project was funded by a Sydney Local Health District Cancer Services Research Grant

(CIA Dr EM, other investigators: BK, PLB, PG, VN). Dr EM was supported in this work by

two PhD scholarships: a University of Sydney Australian Postgraduate Award (APA), and PhD

funding support from Sydney Catalyst: the Translational Cancer Research Centre of Central

Sydney and regional NSW, University of Sydney, NSW, Australia and Cancer Institute NSW.

143

6.2 Abstract

Aim

The Cancer and Ageing Research Group’s (CARG) Toxicity Score was designed to predict

grade ≥3 chemotherapy-related toxicity in adults ≥65yrs commencing chemotherapy for a solid

organ cancer. We aimed to evaluate the CARG Score and compare it to oncologists’ estimates

for predicting severe chemotherapy toxicity in older adults.

Methods

Patients ≥65yrs starting chemotherapy for a solid organ cancer had their CARG Score (range

0-23) calculated. Their treating oncologist, blinded to these results, independently estimated

each patient’s risk of severe chemotherapy toxicity (0-100%). Toxicities were captured

prospectively. The predictive value of the CARG Score and oncologists’ estimates was

estimated using logistic regression and in terms of Area Under the Receiver Operating

Characteristic curve (AU-ROC).

Results

126 patients from 10 oncologists at 2 sites participated. The median age was 72yrs (range 65-

84). The median CARG Score was 7 (range 0-17); the median oncologist estimate of risk was

30% (range 3-80%), and these measures were not correlated (r=-0.01). 64 patients (52%)

experienced grade ≥3 toxicity. Rates of severe toxicity in low-, intermediate-, and high-risk

groups by CARG Score were 58%, 47%, and 58% respectively, and 63%, 44%, and 67% by

oncologist estimate. Severe chemotherapy toxicity was not predicted by the CARG Score (OR

1.04, 95%CI 0.92-1.18, p=0.54, AU-ROC 0.52), or oncologists’ estimates (OR 1.00, 95%CI

0.98-1.02, p=0.82, AU-ROC 0.52).

144

Conclusion

Neither the CARG Score, nor oncologists’ estimates based on clinical judgement, predicted

severe chemotherapy-related toxicity in our population of older adults with cancer. Methods to

improve risk prediction are needed.

145

6.3 Introduction

Chemotherapy toxicity is an important consideration for older adults with cancer (101, 310)

and their oncologist (311) when making decisions about the treatment. About 50% of older

adults having chemotherapy for a solid organ cancer will experience a severe (grade 3 to 5)

chemotherapy-related toxicity over the course of treatment. (48, 49, 68, 92, 143, 312) Such

toxicities may impair quality of life, and lead to hospital admissions and early cessation of

treatment. Clinical prediction tools that help distinguish between older adults at low or high

risk of chemotherapy-related toxicity may help optimise treatment decisions and improve

patient care. Ideally such tools are easily implemented, maintain predictive value in varied

populations, and add to clinical judgement. (178, 313)

The Cancer and Ageing Research Group’s (CARG) Toxicity Score is a chemotherapy toxicity

risk prediction tool that provided proof of concept for combining geriatric assessment variables

with clinical characteristics to predict chemotherapy-related toxicity in older adults. (48, 49)

Eleven clinical, treatment, and geriatric assessment (GA) variables are used to classify patients

as low- (score 0-5), intermediate- (score 6-9), or high-risk (score >10) for severe

chemotherapy-related toxicity. It was developed in a cohort of 500 patients ≥65 years having

chemotherapy for a solid organ cancer of any type or stage. The score showed moderate

predictive performance [Area Under the Receiver Operating Characteristic (AU-ROC), 0.72],

with rates of severe toxicity increasing across risk groups (30% for low-, 52% for intermediate-

, and 83% for high-risk). The score was validated in a similar external cohort (AU-ROC 0.65),

(48) and has been tested in smaller populations with prostate (130) and lung (148) cancers with

mixed results. Whether the score is predictive of lower grade toxicities, often significant in

older adults, is unknown.

146

Prior to this study, the CARG Score had not been tested in a similar population outside of the

United States, and there was little data comparing the CARG Score with clinical judgement.

The aims of this study were therefore to (i) evaluate the CARG Score for predicting severe

chemotherapy-related toxicity in an external population of older adults, and (ii) compare it to

the predictive value of oncologists’ estimates of the risk of severe toxicity. Secondary aims

were to determine the ability of the CARG Score to predict for all-grade toxicities and to

identify predictors of severe toxicity using a brief GA.

6.4 Methods

6.4.1 Design and participants

A prospective observational study was conducted at two tertiary referral cancer centres in

Sydney, Australia. Eligibility criteria were as used by Hurria et al (49): age ≥65 years, a solid

organ cancer (any type or stage), and starting an initial or new line of outpatient chemotherapy.

Exclusion criteria included concurrent radiotherapy, treatment with immunotherapy, or

insufficient English to complete assessments.

Ethics approval was granted by the Sydney Local Health District Human Research Ethics

Committee of Concord Repatriation General Hospital (HREC/15/CRGH/102). Signed,

informed consent was obtained from all participants.

6.4.2 Procedures

Prior to starting chemotherapy, each participant’s CARG Score (range 0-23) was calculated by

a study researcher with whom they completed an abbreviated GA covering standard health

domains (components outlined with presentation of the results). The GA was to be completed

147

prior to the commencement of chemotherapy whilst minimising additional hospital visits.

Treating oncologists were blinded to results of the CARG Score and GA and asked to

independently estimate participants’ risk of severe chemotherapy-related toxicity over the

course of planned treatment (nomination of a single number from 0-100%). This was after they

had assessed the patient and decided to commence chemotherapy. Treating oncologists were

asked to document details of the proposed chemotherapy treatment (regimen, treatment intent,

and dose), and assess participants’ performance status (by Eastern Cooperative Oncology

Group Score (150) & Karnofsky Performance Score, (151)) and level of frailty using the

Canadian Study of Health and Aging (CSHA) Clinical Frailty Scale. (176)

Participants were followed prospectively until completion of planned chemotherapy, or

cessation due to disease progression, patient preference, death, or toxicity. Chemotherapy-

related toxicities (all-grade) were captured prospectively, having been recorded by the treating

oncologist at each clinical presentation (routine or otherwise, including hospitalisations) in the

presence of a trained study investigator (NS) or clinician (oncologist) researcher (EM) and

graded as per the NCI CTCAE version 4.0. (128) The electronic medical record for each patient

was also reviewed each cycle by the study’s clinician researcher (EM) such that all clinical

encounters and their toxicities were captured, including hospitalisations. Grade ≥3

haematological toxicities were recorded if present on the day of the next cycle and led to a

treatment modification or intervention, or if present upon presentation for toxicity between

cycles. No GA-driven interventions were made, and any supportive interventions were as per

usual practice for each oncologist.

148

6.4.3 Statistical analysis

The population was described using frequencies and proportions (%) for categorical variables

and mean and median for continuous variables. Correlation between the CARG Score and

oncologists’ estimates was tested using Spearman’s correlation coefficient. The proportion of

patients with severe toxicity in each risk group by (i) CARG Score and (ii) oncologists’

estimates were determined. CARG Score risk groups were per the derivation study (6): low-

risk (score 0-5), intermediate-risk (score 6-9), and high-risk (score ≥10). Risk groups by

oncologists’ estimates were determined by quartiles (middle two quartiles combined) and were

as follows: low-risk (<30%), intermediate-risk (30-50%), and high-risk (>50%). Associations

between risk groups and severe toxicity were tested using Chi-tests of association.

Univariate and multivariate logistic regression were used to explore potential associations

between toxicity and covariates, with the CARG Score and oncologists’ estimates here treated

as continuous variables. AU-ROC curves were used to summarise the predictive performance

of (i) the CARG Score, (ii) oncologists’ estimates, and (iii) a combined measure of the two.

The combined measure was the risk score estimated from a logistic regression model with the

CARG Score and oncologists’ estimates fitted as covariates. Each of the 11 component items

of the CARG Score were tested for association with toxicity, as were 8 pre-specified patient

measures: age, ECOG-PS, CSHA Clinical Frailty Scale, Orientation-Memory-Concentration

(OMC) test, Mini-Nutritional Assessment Short-Form (MNA-SF), Timed Up and Go (TUG),

g8 Vulnerability Score, and summary GA score, with variables dichotomised where

appropriate.

149

As we were testing the CARG Score in a new population, we compared the proportions of

participants scoring on each of the 11 component items of the CARG Score to those in the

development population (49) using chi-squared statistics.

The planned sample size was N=100 to provide 87% power at the two-sided 5% level of

statistical significance to identify, using logistic regression modelling, a two-fold increase in

the odds of a grade 3+ toxicity for every standard deviation increase in CARG Score, assuming

an underlying prevalence of grade 3+ toxicity of 50%.

6.5 Results

6.5.1 Participant characteristics

Between August 2015 and November 2016, 126 patients were enrolled and completed baseline

assessments. Demographics and clinical characteristics are shown in Table 1 and GA results in

Table 2. Most patients were of good performance status (ECOG-PS 0 or 1, 110, 87%) and

performed well on measures of functional status. Oncologists rated most patients as ‘fit or well’

(93, 74%), and a minority ‘vulnerable’ (28, 22%) or ‘frail’ (5, 4%). A small proportion scored

outside of normal range on screening for cognitive impairment (24, 20%) and depression (32,

25%). Over half (70, 55%) were ‘at-risk’ or ‘likely malnourished’ on nutritional assessment.

Participants were well supported socially, had moderate comorbidity, and one third (41, 32%)

rated their health as ‘excellent’ or ‘very good’. The median time from abbreviated GA to the

start of chemotherapy was 0 days (range 0 to 41 days).

Supplementary Table 1 compares the study population with Hurria’s derivation cohort (6) by

components of the CARG Score. Our cohort had significantly more participants with

gastrointestinal malignancies and less with impaired ability to walk one block.

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6.5.2 CARG Score and oncologists’ estimates

Distributions of the CARG Score and oncologists’ estimates are shown in Figure 1 (A and B).

The median CARG Score was 7 (range 0-17), with 25 (20%) participants classified as low-, 77

(61%) as intermediate, and 24 (19%) as high-risk. The median oncologist estimate was 30%

(interquartile range 30-50%), with 30 (24%) classified as low- (estimate <30%), 79 (63%) as

intermediate- (estimate 30-50%), and 15 (12%) as high-risk (estimate >50%). Oncologists’

estimates and the CARG Score were not correlated, r= -0.03 (Figure 2).

6.5.3 Chemotherapy toxicity

Of the 126 participants who completed baseline assessments, 1 died from rapidly progressing

cancer prior to treatment, and 1 moved to another centre, leaving 124 participants included in

the outcome analysis. Participants received a median of 6 chemotherapy cycles (range 1-17).

60 (48%) completed their planned chemotherapy course. Reasons for discontinuation in the

remaining 64 (52%) were: disease progression (31), patient preference (11), toxicity (11),

miscellaneous (7), relocation to another cancer centre (2), and death (2).

64 (52%) experienced a grade ≥3 chemotherapy-related toxicity over their treatment course, in

26 of 64 (21%) following cycle 1. 43 (35%) experienced a grade ≥3 non-haematological

toxicity, and 38 (31%) experienced a grade ≥3 haematological toxicity. The type and frequency

of severe and all-grade toxicities are outlined in Supplementary Tables 2 and 3. The most

common grade ≥3 non-haematological toxicities were non-neutropenic infection (12, 10%) and

fatigue (9, 7%); the most common all-grade toxicities were fatigue (113, 91%) and nausea (67,

54%). 54 (44%) were hospitalised during their treatment course.

151

33 (28%) patients commenced cycle 1 of chemotherapy at a reduced dose. The proportion of

patients commencing cycle 1 at a reduced dose according to their oncologist-rated ECOG-PS

was ECOG-PS 0/1 21% and ECOG-PS ≥2 75%.The proportion of patients commencing cycle

1 at a reduced dose by their CARG Score risk group was low-risk 42% , intermediate risk 25%,

and high risk 25%. During their chemotherapy, 49 (40%) patients had a reduction in

dose/regimen intensity, 31 (25%) had a treatment delay, and 15 (12%) had an increase in

dose/regimen intensity.

6.5.4 Predictive value of the CARG Score and oncologists’ estimates

The CARG Score did not predict severe chemotherapy-related toxicity (OR 1.04, 95%CI 0.92-

1.18, p=0.5) and had low discriminatory value (AU-ROC 0.52, 95%CI 0.42-0.62).

Oncologists’ estimates also did not predict severe chemotherapy-related toxicity (OR 1.00,

95%CI 0.98-1.02, p=0.8) and had low discriminatory value (AU-ROC 0.52, 95%CI 0.42-0.62).

(Figure 3) Rates of severe toxicity in low-, intermediate-, and high-risk groups by CARG Score

were 58%, 47%, and 58% respectively, with no association between risk group and toxicity

(p=0.4). Rates of severe toxicity in low-, intermediate-, and high-risk groups by oncologist

estimate were 63%, 44% and 67% respectively (p=0.1). (Supplementary Figure 1) The addition

of the CARG Score to oncologists’ estimates in a combined model did not improve its

predictive value (AUC-ROC 0.52). There was no relationship between the CARG Score and

the burden of grade 2 toxicities, or a sum of all-grade toxicities (Supplementary Figure 2). In a

post-hoc analysis, there was no significant difference in proportions of patients hospitalised or

completing planned treatment across risk groups by CARG Score or oncologist estimate

(Supplementary Table 4). Details of hospitalisations are in Supplementary Table 5.

152

6.5.5 Other predictors

Univariate logistic regression of the 8 pre-specified clinical variables (Table 4) and the 11 items

of the CARG Score (Supplementary Table 6) revealed: (i) better functional status (by TUG) to

be negatively associated with severe grade 3-5 toxicity (OR 0.24, 95%CI 0.06-0.92, p=0.04);

(ii) better cognition (by OMC test) to be negatively associated with severe grade 3-5 toxicity

(OR 0.37, 95%CI 0.14-0.96, p=0.04); and, (iii) impaired social activity due to health (assessed

via the Medical Outcomes Study item of the CARG Score) to be positively associated with

severe grade 3-5 toxicity (OR 2.19, 95%CI 1.05-4.59, p=0.04). No satisfactory multivariate

model was obtained using either forward or backward selection approaches.

6.6 Discussion

Our study’s main findings were that neither the CARG Score nor oncologists’ estimates of

likelihood of severe chemotherapy-related toxicity were useful predictors of severe

chemotherapy-related toxicity. Patients classified as low-risk by CARG Score experienced

equivalent rates of severe toxicity as those classified as high-risk. There was no relationship

between patients’ CARG Score and oncologists’ estimates, and oncologists tended to

underestimate risk. Prolonged TUG, impaired social activity, and an abnormal OMC test were

associated with severe toxicity.

Two published studies have reported on the utility of the CARG Score in populations external

to the derivation cohort. Nie et al (148) tested the CARG Score in 120 patients receiving

chemotherapy for lung cancer. Rates of toxicity significantly increased across low-,

intermediate-, and high-risk groups (9%, 40%, and 60% respectively, p<0.001). Alibhai et al

(130) tested the CARG Score in a cohort of 46 patients having docetaxel for metastatic prostate

cancer. There was a non-significant increase in toxicity across risk groups (rates of 0%, 17%,

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and 27% respectively; p=0.65), the study limited by small sample size and low event rate.

Hurria et al (48) published a validation study of 250 patients across 8 centres in the United

States, 6 of which participated in model development. The predictive value of the CARG Score

was insignificantly lower in the validation cohort (AU-ROC 0.65, 95%CI 0.58-0.71) than in

the development cohort (AU-ROC 0.72, 95%CI 0.68-0.77; p=0.09) (49), with smaller

differences in rates of toxicity between intermediate and high-risk groups (low, intermediate,

and high-risk groups with rates of 37%, 62%, and 70% respectively). In our study, there was

no difference in toxicity rates across risk groups, and the model showed a predictive ability

close to chance (AU-ROC of 0.52), with a confidence interval (95%CI 0.42-0.62) entirely

below that reported in the development study by Hurria et al. (49)

Differences in study population may explain the differences in AU-ROC estimates between

our study and the development and validation studies by Hurria et al. (48, 49) Hurria et al’s

validation study (48) was conducted in similar centres in the United States to the derivation

study, (49) resulting in study populations with comparable characteristics. Our population had

significantly more patients with gastrointestinal cancers and less with lung and breast cancer

affecting (i) algorithm scoring for ‘tumour type’ and (ii) planned chemotherapy regimens and

consequent toxicity. Our population also had significantly less patients with “impaired ability

to walk one block” possibly reflecting better overall fitness. Other considerations include

differences in local practices that affect the actual rates or grades of toxicity, for example, the

use of prophylactic colony-stimulating factor support [0% in our study v 18% in Hurria et al

(49)] and other means of supportive care. Further differences in practice patterns and healthcare

systems that are less easily measured may also account for our findings. For example, having

been developed in patients starting chemotherapy, the CARG Score is sensitive to what

judgements have already been made about patients deemed ‘fit for chemotherapy’. Australian

154

oncologists may select patients for chemotherapy differently to US oncologists, altering the

score’s performance.

Methodological differences potentially influencing results on the performance of the CARG

Score include the prospective recording of toxicity in our study (traditional overestimation

bias) versus Hurria et al’s (49) retrospective design (underestimation bias). Opposing the

direction of these traditional biases, the prospective nature of our study may have introduced a

bias to under-reporting or early supportive intervention for toxicity, as oncologists were aware

these outcomes were being scrutinised. Noted, however, are the comparable toxicity rates and

rates of dose modification between the two studies.

Oncologists’ estimates did not predict severe chemotherapy-related toxicity. To our

knowledge, the study by Alibhai et al (130) is the only other published study that has evaluated

the predictive value of oncologists’ estimates of chemotherapy-related toxicity in older adults,

and showed similar results. Oncologists’ estimates on a 10-point scale (low to high) were

poorly correlated with the CARG Score (r=0.109, p=0.46) and were not predictive of toxicity

(OR 1.04, 95%CI 0.71-1.52, p=0.83). Reasons to explain this include: oncologists not being

used to assigning a single numerical value to the overall likelihood of a severe toxicity;

difficulty translating rates of toxicity published in clinical trials to older, frailer adults in real-

world practice; and, oncologists lacking confidence in their ability to predict chemotherapy-

related toxicity in their older patients. (311)

Previous attempts at identifying clinical predictors of chemotherapy-related toxicity from GA

in older adults with mixed tumour types have used heterogeneous methods and given varied

results. (88, 92) The TUG has been associated with an increased risk of falls, (156) early

155

mortality (139) and functional decline (138) in older adults receiving chemotherapy. It has been

evaluated against chemotherapy toxicity in only one other study, where a cut off of >10s was

associated with severe toxicity. (49) Impaired cognition has been associated with severe non-

haematologic toxicity in one study of older adults with mixed tumour types (68) and with

severe all-cause toxicity in those receiving chemotherapy for colon cancer. (136) However, no

association between cognition and toxicity was found in studies of patients with mixed tumour

types, (49, 143) or in studies of patients with lung (314) or ovarian cancer. (144) Impaired

social activity due to health (derived from the Medical Outcomes Study social functioning item,

and a component of the CARG Score), maintained predictive value for toxicity in our study.

This measure asks patients how often their health has limited their normal social activities and

so reflects global, and often subtle, impairments in multiple health domains, and so showed

good discrimination within our population selected for fitness for chemotherapy.

6.6.1 Strengths and limitations

Strengths of our study include it being the first reported study testing the CARG Score in a

heterogeneous cancer population outside of the United States and the largest study comparing

oncologists’ estimates of chemotherapy-related toxicity with a risk prediction tool. Prospective

collection of toxicities at the point of care strengthens the outcome data. Rather than use an

ordinal scale, oncologists nominated expected toxicity rates (0-100%) for a group of similar

patients to allow calibration between oncologists (what is low to one oncologist may be

considered moderate or high to another). Oncologists were also blinded to patients’ CARG

Scores and GA results so that this information did not influence their clinical assessments,

planned chemotherapy, and study outcomes.

Limitations of our study include the modest sample size. A larger sample size would have

improved power to declare modest associations between measures of risk and toxicity as

156

statistically significant, and to generate a multivariate predictive model. The narrow 95%

confidence interval surrounding our estimate of the AU-ROC for the CARG Score fell entirely

below that of Hurria et al’s derivation study, (49) suggesting that an increase in sample size

would not significantly alter our conclusions regarding the predictive ability of the CARG

Score in our population. The modest sample size and small number of centres (n=2) and

oncologists (n=10) limits the wider generalisability of the results. We also recognise a lack of

patient reported outcomes as a weakness of the study. Our primary outcome measure was the

occurrence of any grade 3 to 5 chemotherapy-related toxicity across the course of treatment

which included haematological toxicities. Although haematological toxicities may result in

treatment delay or modification, they may be less important to patients. The prospective design

of our study may have inadvertently introduced bias to the testing of the CARG Score. Despite

blinding of oncologists to its results, learning of the CARG Score over time by the involved

clinicians and modification to planned treatment or supportive interventions in line with

expected risk category, cannot be discounted. The proportion of high risk (by CARG Score)

patients receiving planned reduced dose chemotherapy was, however, low (25%), when

compared to measures for which the oncologist was unblinded (rate of planned dose reduction

in those ECOG-PS ≥2 of 75%, and in those considered frail of 47%), suggesting that this was

unlikely to be the case.

The results of our study do not support the implementation of the CARG Score in routine

practice in the local setting. The results do suggest a need for improved risk prediction, and

education and support of oncologists to improve their prediction of treatment toxicity for older

adults with cancer. The importance of validating geriatric assessment tools in varied healthcare

settings and geographic locations is highlighted. Identifying strong and reproducible predictors

of chemotherapy toxicity in cohorts of older patients with varied cancer types treated with

157

varied chemotherapy regimens is problematic, so future studies could focus on single tumour

types or treatment regimens. This would enable focus on the impact of regimen-specific lower

grade toxicities (such as capecitabine related diarrhoea, or taxane neuropathy). Additional

research implications are the validity of other risk prediction tools in our population, and the

optimal assessment of older adults who declined or were deemed unsuitable for chemotherapy.

6.7 Conclusion

Neither the CARG Score nor oncologists’ estimates of toxicity based on clinical judgement

were significant predictors of severe chemotherapy-related toxicity in our cohort of older adults

with a solid organ cancer. Aspects of the GA, namely the Prolonged Timed Up and Go,

impaired social activity due to health, and an abnormal Orientation Memory Concentration test,

predicted severe chemotherapy-related toxicity. Methods to improve risk prediction in the local

setting are needed.

158

Table 1. Participant characteristics

Characteristic Number (%)

Sex Male

Female

75 (60)

51 (40)

Cancer centre Concord Cancer Centre

The Chris O’Brien Lifehouse

88 (70)

38 (30)

Age 65 to 69 years

70 to 74 years

75 to 79 years

≥80 years

37 (29)

41 (33)

39 (31)

9 (7)

Employment status Retired or not working

Working

110 (87)

16 (13)

Marital status Married / de facto

Widowed

Divorced / separated

Single

89 (71)

18 (14)

7 (6)

12 (9)

Living arrangements Lives with others

Lives alone

Care facility

100 (79)

26 (21)

0 (0)

Language spoken at home English

Non-English

90 (79)

36 (21)

Community services Yes

No

11 (9)

115 (91)

Cancer type Colorectal

Upper gastrointestinal*

Lung / pleura

Prostate

Bladder

Ovarian

Breast

Other

45 (36)

23 (18)

13 (10)

10 (8)

9 (7)

9 (7)

8 (6)

9 (7)

Stage of cancer I

II

III

IV

0 (0)

16 (13)

32 (25)

78 (62)

Line of treatment Neoadjuvant

Adjuvant

1st line palliative

Subsequent line palliative

11 (9)

33 (26)

55 (44)

27 (21)

Chemotherapy regimen* Single agent

Combination chemotherapy

51 (41)

75 (60)

Primary G-CSF Yes

No

0 (0)

126 (126)

Initial dose plan for cycle 1 Dose reduced

Standard dose

35 (28)

91 (72)

Abbreviations: G-CSF, granulocyte colony stimulating factor

*upper gastrointestinal includes pancreaticobiliary, gastric, and oesophageal cancers

159

Table 2. Baseline geriatric assessment results

Characteristic Category n(%) Median Range Range of scores

Self-rated health Excellent or very good

Good

Fair or poor

41 (33)

54 (43)

31 (25)

CSHA Clinical Frailty Rating Fit, or well

Vulnerable or frail

92 (73)

34 (27)

Performance status

ECOG Performance Status

Karnofsky Performance Rating Scale

0

1

2

3

4

90-100

80

≤70

37 (29)

73 (58)

15 (12)

1 (1)

0

53 (42)

42 (33)

31 (25)

Functional status

KATZ Activities of Daily Living (77, 157)

OARS Instrumental ADLs (158)

MOS Physical Functioning (159)

Timed Up and Go (154, 315)

Falls in last 6 months

Independent (score 6)

Dependent ≥1 task

Independent (score 14)

Dependent ≥1 task

No limitation

Some limitation

≥14s

<14s

Yes

111 (88)

15 (12)

72 (57)

54 (43)

13 (10)

113 (90)

14 (11)

108 (89)

18 (14)

6

14

26

11s

2 - 6

6 - 14

13 - 30

6.7 -55.7

0 - 6

0 - 14

10 - 30

secs

Comorbidities

CIRS-G (161) Total Score

CIRS-G Index

Severe comorbidity

Life-threatening comorbidity

17 (13)

6 (5)

4

1.55

0 - 12

0 - 3

Polypharmacy

160

Number of medications 4 0 – 16

Social Supports

Social Support Survey (167)

Complete social supports

Some deficit in support

75 (60)

51 (40)

20

4 - 20

0 - 20

Mood and Cognition

5-Item Geriatric Depression Scale (165)

Orientation-Memory-Concentration Test* (162)

Score 0 or 1

Score ≥2 (abnormal)

Score 0 to 4 (normal)

Score 5 to 9

Score ≥10

94 (75)

32 (25)

102 (81)

12 (10)

12 (10)

1

2

0 – 5

0 - 21

0 – 5

0 – 30

Nutrition

Weight loss in last 6 months

Mini-Nutritional Assessment Short Form (169)

Yes

Normal nutrition (score 12 to 14)

At risk (score 8 to 11)

Malnourised (score 0 to 7)

75 (60)

56 (44)

62 (49)

8 (6)

11

6 - 14

0 – 14

G8 (170) >14

≤14 (at risk)

45 (36)

81 (64)

Geriatric assessment score* 0 or 1

2 or 3

≥4

62 (49)

50 (40)

14 (11)

* Categories reported under the Blessed Orientation Memory Concentration test are those suggested by Morris et al’s Memory and Aging Project (1989), with normal cognition

(score of 0 to 4), requiring evaluation (score of 5 to 9), or likely consistent with dementia (score ≥10).

**Geriatric assessment score (range 0 to 7) is a summary score for the geriatric assessment performed, where a point is scored for a deficit in a geriatric health domain as

follows:

- performance status ECOG 2 or more

- functional status: TUG >/= 14s, any dependency in ADLs

- nutrition: MNA at risk or malnourished

- cognition: at risk or likely consistent with dementia

- social supports: <18 (lowest quartile)

- psychological state: GDS >/= 2

- comorbidity: CIRS G score >6 (highest quartile)

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Table 3. Predictors of severe chemotherapy-related toxicity

Characteristic

% with

toxicity

OR 95% CI P-value

Age group 65 to 69y

70 to 74y

75 to 79y

≥80y

44%

48%

59%

67%

0.40

0.45

0.72

ref.

0.09 – 1.86

0.10 – 2.07

0.16 – 3.31

-

0.45

ECOG PS 0 or 1

≥2

52%

50%

1.08 0.38 – 3.08 0.89

CSHA Frailty Fit to well

Vulnerable to frail

51%

55%

0.85 0.38 – 1.89 0.69

Cognition (OMC) Normal cognition (score 0 to 4)

Needs evaluation (score ≥5)

47%

71%

0.37 0.14 – 0.96 0.04

Nutrition (MNA-SF) Normal nutrition (score ≥12)

At risk or malnourished (score <12)

46%

56%

0.69 0.34 – 1.40 0.30

Timed Up and Go <14 seconds

≥14 seconds

47%

79%

0.24 0.06 – 0.92 0.04*

g8 Not vulnerable (score >11)

Vulnerable (score ≤11)

43%

56%

0.59 0.28 – 1.24 0.17

Geriatric assessment

score

Score 0 or 1

Score 2 or 3

Score ≥4

44%

54%

71%

0.33

0.47

ref.

0.09 – 1.16

0.13 – 1.70

-

0.20

Abbreviations: ECOG PS, Eastern Cooperative Oncology Group Performance Status; CSHA Frailty, Canadian

Study of Health and Aging Frailty Index; OMC, Short Blessed Orientation Memory Concentration Test; MNA,

Mini Nutritional Assessment Short Form

*maintained significance on multivariate analysis with forward selection

162

A.

B.

Figure 1. Distribution of the CARG Toxicity Score (A) and Oncologists’ estimates

(B) in study population (n=126)

0

5

10

15

20

25

30

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Nu

mb

er o

f p

atie

nts

CARG Toxicity Score (0 to 23)

0

5

10

15

20

25

30

35

40

0 3 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Nu

mb

er o

f p

atie

nts

Oncologists' estimates (0 to 100%)

163

Figure 2. Relationship between CARG Toxicity Score and Oncologists’ estimates of

the likelihood of severe chemotherapy-related toxicity. Spearman’s correlation coefficient

r=-0.03.

y = -0.033x + 35.983R² = 3E-05

0

10

20

30

40

50

60

70

80

90

0 2 4 6 8 10 12 14 16 18

On

colo

gist

est

imat

e o

f se

ver

e to

xici

ty

(0 t

o 1

00

%)

CARG Toxicity Score (0 to 23)

164

A. B.

C.

Figure 3. Predictive value for toxicity of the CARG Toxicity Score (A), Oncologists’

estimates (B), and a combined model of the two (C) as modelled by Receiver operating

characteristic (ROC) curves

165

Supplementary Table 1. Comparison of Study Population versus Hurria et al (49) Population by components of the CARG Score

Comparison of study cohort with Hurria et al Scoring amongst CARG risk groups in study population

Risk factor Score*

Study cohort

(n=126)

Hurria et al

(n=500) p-value**

Low risk

(n=25)

Int. risk

(n=77)

High risk

(n=24)

n (%) % n (%) n (%) n (%)

Age ≥72 years 2 70 (56) 54 0.7 5 (20) 43 (56) 22 (92)

Cancer type GI or GU 2 90 (71) 37 <0.001 6 (24) 62 (80) 22 (92)

Standard dose chemotherapy 2 91 (72) 76 0.4 15 (60) 59 (76) 18 (75)

More than one drug 2 75 (60) 70 0.03 10 (40) 53 (69) 12 (50)

Haemoglobin <11g/dL (male),

<10g/dL (female)

3 19 (15) 12 0.4 0 7 (10) 12 (50)

Creatinine clearance (Jellife,

ideal weight) <34 mL/min

3 5 (4) 9 0.07 0 1 (1) 4 (17)

Hearing fair or poor 2 38 (30) 25 0.3 0 1 (1) 13 (54)

Reported falls in last 6 months 3 18 (14) 18 0.3 2 (8) 8 (10) 8 (33)

Medications taken with at

least some assistance

1 8 (6) 8 0.5 1 (4) 3 (4) 4 (17)

Walking one block at least

somewhat limited

2 13 (10) 22 0.003 1 (4) 5 (6) 7 (29)

Social activity limited at least

sometimes due to health

1 49 (39) 44 0.3 9 (36) 25 (32) 15 (63)

Abbreviations: GI, gastrointestinal; GU, genitourinary.

*Reflects points scored for presence of each item. The CARG Toxicity Score is a sum of scores for all 11-items.

**p-value reflects comparison of proportions of patients scoring on each item between study population and Hurria et al’s population (chi-test)

166

Supplementary Table 2. Most common severe (grade 3 to 5) chemotherapy-related

toxicities

Toxicity* Grade 3 to 5 Grade 3 Grade 4 Grade 5

Non-haematological

infection with normal ANC 12 9 1 2

fatigue 9 9 0 0

diarrhoea 5 4 1 0

thrombosis 4 3 1 0

nausea 3 3 0 0

dehydration 3 3 0 0

hyponatraemia 3 3 0 0

hypokalaemia 3 3 0 0

acute kidney injury 3 2 1 0

abdominal pain 2 2 0 0

infection with abnormal ANC 2 1 1 0

pneumonitis 2 1 1 0

Haematological

neutropenia 29 19 10 0

leucopenia 23 21 2 0

anaemia 9 9 0 0

thrombocytopenia 9 6 3 0

febrile neutropenia 5 5 0 0

Abbreviations: ANC, absolute neutrophil count

*Most common severe (grade 3 to 5) chemotherapy related toxicities (experienced by >1 participant).

Additional causes of grade 3 to 5 toxicity were: constipation, vomiting, oral mucositis, ALT increased,

gastritis, hypomagnesemia, infusion reaction, lower gastrointestinal haemorrhage, hypertension, colitis,

duodenal ulcer, stroke, small bowel obstruction, hypersensitivity reaction, AST increased, lymphopaenia

167

Supplementary Table 3. All-grade chemotherapy-related toxicities

Toxicity All grades Grade 1 Grade 2 Grade 3 Grade 4 Grade 5

fatigue 113 65 39 9 0 0

nausea 67 45 19 3 0 0

constipation 54 45 8 1 0 0

diarrhoea 48 26 17 4 1 0

anorexia 46 32 14 0 0 0

alopecia 44 16 18 0 0 0

sensory neuropathy 41 27 14 0 0 0

malaise 37 29 8 0 0 0

neutropenia 29 - - 19 10 0

hand foot syndrome 28 19 9 0 0 0

limb oedema 27 15 12 0 0 0

infection with normal ANC 23 0 11 9 1 2

vomiting 23 15 7 1 0 0

leucopenia 23 - - 21 2 0

oral mucositis 17 13 3 1 0 0

maculopapular rash 16 12 4 0 0 0

creatinine increased 15 13 0 2 0 0

ALT increased 15 13 1 1 0 0

Gastritis 14 9 4 1 0 0

Dyspnoea 14 10 4 0 0 0

acneiform rash 14 9 5 0 0 0

GGT increased 13 11 2 0 0 0

Abbreviations: ANC, absolute neutrophil count; ALT, alanine aminotransferase; GGT, gamma glutamyl

transferase

Grade reported is the worst grade experienced across chemotherapy course

Toxicities listed are those experienced by at least 10% participants

Haematological toxicities were captured if ≥ grade 3 on day 1 of a treatment cycle and led to a treatment

alteration or intervention, or if were present at a time of clinical presentation for toxicity between cycles.

168

Supplementary Table 4. Hospitalisations and completion of planned treatment by

toxicity risk groups

Hospitalised Completed planned treatment†

Risk Group n(%) p-value n(%) p-value

CARG Score*

0.98 0.70

Low risk (n=24) 10 (42%) 16 (67%)

Intermediate risk (n=76) 33 (43%) 56 (71%)

High risk (n=24) 10 (42%) 16 (67%)

Oncologist estimate**

0.53 0.57

Low risk (n=30) 14 (47%) 21 (70%)

Intermediate risk (n=79) 31 (39%) 58 (73%)

High risk (n=15) 8 (53%) 9 (60%)

p-values of comparison of proportions (chi-test)

*CARG Score risk groups: low risk (score 0 to 5), intermediate risk (score 6 to 9), high risk (score ≥10).

**Oncologist estimate risk groups defined by 25th and 75th quartiles, low risk (estimate <30%), intermediate

risk (estimate 30-50%), high risk (estimate >50%)

† Completion of planned treatment was defined as: completion of all planned cycles of chemotherapy, or

discontinuation for disease progression (not toxicity/patient preference) in cases of palliative intent treatment

where no minimum number of cyces was specified.

169

Supplementary Table 5. Details of 67 Hospitalisations in 126 older adults receiving

chemotherapy

n (%)

Admission type

Emergency

Planned

56 (84)

11 (16)

Timing of admission

After cycle 1

Subsequent cycle

22 (33)

45 (67)

Length of stay

Median (range) 5 days (1 to 80)

Primary reason for

admission*

Primarily treatment toxicity

Primarily symptoms of complications of malignancy

Unrelated to treatment or malignancy

34 (51)

22 (33)

11 (16)

Primary admission

diagnosis**

Biliary tract infection or obstruction

Oxaliplatin desensitisation†

Febrile neutropenia

Colitis

Bowel obstruction

Dysphagia

Dehydration

Gastrointestinal haemorrhage

Non-neutropenic lower respiratory tract infection

Constipation

Pneumonitis

Congestive cardiac failure

Palpitations

Monitoring

8 (12)

7 (10)

6 (9)

5 (7)

4 (6)

3 (4)

2 (3)

2 (3)

2 (3)

2 (3)

2 (3)

2 (3)

2 (3)

2 (3)

* Primary admission reason as classified by clinician investigator

** One primary diagnosis was recorded for each admission. It is recognised that more than one diagnosis or

problem may have been present for each admission.

170

Supplementary Table 6. Association between the 11-Items of the CARG Score and

toxicity

Characteristic

% with

toxicity

OR 95% CI P-

value

Age >/= 72 years 54 1.28 0.63 – 2.60 0.50

Cancer type Gastrointestinal or genitourinary 48 0.62 0.28 – 1.38 0.24

Chemotherapy

dosing

Standard 52 1.19 0.54 – 2.59 0.67

Number of drugs More than 1 49 0.79 0.39 – 1.63 0.53

Haemoglobin <11 (M) <10(F) 53 1.05 0.39 – 2.79 0.92

Creatinine

clearance

<34mL/min 80 3.93 0.43 – 36.24 0.23

Hearing Fair or poor 39 0.53 0.24 – 1.16 0.11

Falls in 6 months 1 or more 61 1.57 0.57 – 4.36 0.39

Medications Needs assistance 63 1.61 0.37 – 7.05 0.53

Walking 1 block Somewhat limited or limited a lot 69 2.29 0.67 – 7.88 0.19

Decreased social

activity

Limited at least sometimes 63 2.19 1.05 – 4.59 0.04*

*maintained significance on multivariate analysis with backward elimination

171

A.

B.

Supplementary Figure 1. Severe chemotherapy toxicity according to risk group by CARG

Score (A) and oncologists’ estimate (B)

0

5

10

15

20

25

30

35

40

45

50

G3-5toxicity

No G3-5toxicity

G3-5toxicity

No G3-5toxicity

G3-5toxicity

No G3-5toxicity

Low risk(score 0-5)

Intermediate risk(score 6-9)

High risk (score ≥10)

Nu

mb

er o

f p

atie

nts

CARG Score Risk Group

G3-5 Non-haematological toxicity G3-5 haematological toxicity only No G3-5 toxicity

0

5

10

15

20

25

30

35

40

45

50

G3-5toxicity

No G3-5toxicity

G3-5toxicity

No G3-5toxicity

G3-5toxicity

No G3-5toxicity

<30% 30 to 50% >50%

Nu

mb

er o

f p

atie

nts

Oncologist estimate of likelihood of severe toxicity (%)

G3-5 Non-haematological toxicity G3-5 haematological toxicity only No G3-5 toxicity

172

Supplementary Figure 2. Relationship between CARG Toxicity Score and all-grade

toxicity

173

7. Oncologists’ perceptions on the usefulness of

geriatric assessment measures and the CARG

Toxicity Score when prescribing chemotherapy for

older patients with cancer.

7.1 Overview

This is a published chapter of a companion study to that presented in Chapter 6, and aimed at

a general oncology clinical audience. The value of the CARG Toxicity Score and geriatric

assessment to oncologists when making a decision about chemotherapy for their patients with

cancer is evaluated, and the potential impact of these assessments on chemotherapy prescribing

and patient management is explored. The published manuscript is included verbatim.

Publication details

Moth EB, Kiely BE, Stefanic N, Naganathan V, Martin A, Grimison P, Stockler MR, Beale

P, Blinman P. Oncologists' perceptions on the usefulness of geriatric assessment measures

and the CARG toxicity score when prescribing chemotherapy for older patients with cancer. J

Geriatr Oncol, 2018 10(2): 210-215.

Contribution of authors

I, Dr Erin Moth, contributed to study conception and design, was responsible for ethics

submissions, data acquisition, data analysis and interpretation of results, preparation of the draft

manuscript, manuscript editing and revision.

Dr Belinda Kiely contributed to study conception and design, interpretation of results,

manuscript editing and revision.

174

Dr Natalie Stefanic contributed to study design, data acquisition, interpretation of results,

manuscript editing and revision.

Prof Vasi Naganathan contributed to study conception and design, interpretation of results,

manuscript editing and revision.

A/Prof Andrew Martin contributed to study conception and design, data analysis, interpretation

of results, manuscript editing and revision.

A/Prof Peter Grimison contributed to study design, interpretation of results, manuscript editing

and revision.

Prof Martin Stockler contributed to study design, interpretation of results, manuscript editing

and revision.

A/Prof Philip Beale contributed to study design, interpretation of results, manuscript editing

and revision.

Dr Prunella Blinman contributed to study conception and design, interpretation of results,

manuscript editing and revision.

Acknowledgement of funding

This project was funded by a Sydney Local Health District Cancer Services Research Grant

(CIA Dr EM, other investigators: BK, PLB, PG, VN). Dr EM was supported in this work by

two PhD scholarships: a University of Sydney Australian Postgraduate Award (APA), and PhD

funding support from Sydney Catalyst: the Translational Cancer Research Centre of Central

Sydney and regional NSW, University of Sydney, NSW, Australia and Cancer Institute NSW.

175

7.2 Abstract

Background

The use of geriatric assessment (GA) and the Cancer and Aging Research Group (CARG)

Toxicity Score by Australian oncologists is low. We sought oncologists’ views about the value

of GA and the CARG Score when making decisions about chemotherapy for their older

patients.

Methods

Patients aged ≥65yrs with a plan to start chemotherapy for a solid organ cancer underwent a

GA and had their CARG Score calculated. Results of the GA and CARG Score were provided

to treating oncologists who then completed a questionnaire on the value of these measures for

each patient.

Results

We enrolled 30 patients from eight oncologists. Patients had a median age of 76 years and most

(77%) were ECOG performance status 0 or 1. Risk category for severe chemotherapy toxicity

by CARG Score was low in 7 patients (23%), intermediate in 18 (60%), and high in 5 (17%).

The GA provided oncologists new information for 12 patients (40%), most frequently in the

domains of function and nutrition. Knowledge of the GA prompted supportive interventions

for 7 patients (23%). Oncologists considered modifications to recommended chemotherapy

based on the CARG Score for 2 patients (7%) (one more intensive and one less intensive), and

based on GA for no patients. Oncologists judged the GA and CARG Score as useful in 26

(87%) and 25 (83%) patients, respectively.

176

Conclusion

Although oncologists valued the GA and CARG Score, they rarely used them to modify

chemotherapy. The GA provided new information that prompted supportive interventions in

one quarter of patients.

177

7.3 Introduction

Determining the suitability of an older adult with cancer for chemotherapy ideally involves

geriatric assessment (GA) and the use of clinical risk prediction tools, both now recommended

in international guidelines. (78, 316) GA identifies co-existent geriatric problems that can

affect tolerance of anti-cancer therapies and prompt supportive interventions, (78, 92) may alter

treatment decisions, (86, 87) and improve treatment tolerance and completion. (87, 282) The

Cancer and Aging Research Group’s (CARG) Toxicity Score (48, 49) is a clinical risk

prediction tool that may aid decision-making about chemotherapy by estimating the likelihood

of severe chemotherapy toxicity in older adults. Eleven clinical and GA variables are used to

classify patients as low, intermediate, or high risk of severe chemotherapy toxicity. (48, 49)

Despite their potential benefits to patient care, use of the GA and CARG Score by Australian

oncologists is low. (84, 311)

Barriers to the implementation of clinical tools are multifaceted. Accessibility, time burden,

and resource availability were cited as the most frequent barriers to the use of a GA in a recent

cross-sectional survey of 69 Australian oncologists, (84) with most of these oncologists

agreeing that a GA would add to their clinical assessment (71%) and would influence their

clinical decision-making (65%). The CARG Score has been externally validated (48) and tested

in a small number of external cohorts, (130, 148, 149, 317) but there are no published studies

on barriers to its use or its perceived value to oncologists and its potential to influence decision-

making.

We performed a prospective observational study evaluating the CARG Score and comparing it

to oncologists’ clinical judgement in predicting for severe chemotherapy toxicity. (317) This

parent study provided the opportunity to prospectively determine the value of the GA and

178

CARG Score to oncologists prescribing chemotherapy for older adults. Specific objectives of

this substudy were to determine (i) oncologists’ views on the usefulness of the GA and CARG

Score, (ii) new information gained from the GA, and (iii) the potential impact of the GA and

CARG Score on chemotherapy prescribing and patient management.

7.4 Methods

7.4.1 Design and participants

Participants of this substudy were the oncologists of a subset of older adults with cancer

participating in the parent study described and referenced above. (317) Eligibility criteria for

the parent study included age ≥65 years, diagnosis of a solid organ malignancy (any type or

stage), and plan to start an initial or new line of systemic cytotoxic chemotherapy.

Ethics approval for this study was provided by the Sydney Local Health District Human

Research Ethics Committee of Concord Repatriation General Hospital (HREC/15/CRGH/102)

and the study was open at two cancer centres in Sydney, Australia.

7.4.2 Procedures

Oncologists determined plans for chemotherapy for each patient as per usual clinical practice.

A trained study researcher (NS) or clinician researcher (EM) not involved in clinical care then

completed a GA and calculated the CARG Score for each patient. The GA was performed in

the outpatient clinic at an agreed time to minimise additional visits, and took less than 30

minutes. Geriatric health domains assessed were: functional status by the Timed Up and Go,

(154, 315) Katz Index of Activities of Daily Living, (77, 157) OARS Multidimensional

Functional Assessment Instrumental Activities of Daily Living, (158) the Medical Outcomes

Study (MOS) Physical Functioning Scale, (159) and a self-reported history of falls;

179

comorbidity by the Cumulative Illness Rating Scale in Geriatrics (CIRS-G); (161) cognition

by the Short Blessed (Orientation Memory Concentration) Test; (162) psychological health by

the Geriatric Depression Scale 5-Item Short Form (GDS-5); (165) social supports by the

modified MOS Social Support Survey; (167) and nutrition by unintentional weight loss and the

Mini Nutritional Assessment Short Form (MNA-SF). (169)

For the first 126 patients, oncologists were blinded to the results of the GA and CARG Score

and independently estimated the risk of severe chemotherapy toxicity for each of their

participating patients. Results of this first part have been published. (317) For the final 30

patients reported here, treating oncologists were provided with results of the GA and CARG

Score as a pro forma written report (Appendix C) and then invited to complete a study-specific

questionnaire (Appendix D). For all 156 patients, a chemotherapy treatment recommendation

had been made prior to the GA and CARG Score being performed. The GA and CARG Score

results were presented to oncologists for this substudy prior to the commencement of

chemotherapy or shortly after starting chemotherapy, as it was apparent from Part 1 that GAs

were most feasibly performed on day 1 of treatment. (317)

7.4.3 Oncologist questionnaire

The substudy questionnaire addressed themes of (i) new information gained from the GA; (ii)

the impact of the GA and CARG Score on chemotherapy recommendations; (iii) GA-prompted

interventions; and (iv) the usefulness and ease of interpretation of the GA and CARG Score

(Appendix D). Of note, the impact of the GA and CARG Score on chemotherapy

recommendations was evaluated retrospectively. Oncologists were first asked how their

chemotherapy recommendation compared to standard treatment for a younger, fitter patient

with the same type and stage of cancer. Deviations from standard treatment here were

180

considered to have been made based on usual clinical judgement. Oncologists were then asked

how their recommendation would have been modified based on the (i) GA and (ii) CARG

Score had they known these results prior to making a treatment decision. A post-hoc brief

written survey asked oncologists to comment on the barriers to implementation of the GA and

CARG Score in clinical practice.

7.4.4 Analysis

Questionnaire responses were described using frequencies and proportions (%). Proportions of

responses within answer categories using Likert scales were presented using stacked bar charts.

A post-hoc analysis explored the relationship between a patient’s CARG Score and their

oncologist-rated CSHA Clinical Frailty Rating using a 2 x 3 contingency table and Fisher’s

Exact Test. We aimed to enrol 50 patients to provide 95% confidence intervals of estimated

proportions of within +/- 15%.

7.5 Results

Between December 2016 and March 2017, eight oncologists provided survey responses for 30

patients with a response rate of 100%. The sample size was smaller than planned due to

resource availability and presentation of results for Part 1, in which the CARG Score did not

predict severe chemotherapy toxicity in our local population. (317) Given that this may have

influenced oncologists’ responses to the questionnaire for the substudy, recruitment was

stopped at 30 patients.

Of the eight oncologists, the median years in practice was 14 (range 7-25 years), most (six of

eight) worked mainly in the public setting, and nearly all (seven of eight) never used GA tools

181

in their routine practice. Most (five of eight) reported patients ≥70 years comprising between

50% and 75% of their practice.

Table 1 outlines patient characteristics. Table 2 outlines GA results. Oncologists rated most

patients as fit or well (21, 70%), and a minority as vulnerable or frail (9, 30%). Seven patients

(23%) were classified as low, 18 (60%) as intermediate, and 5 (17%) as high risk of severe

chemotherapy toxicity by CARG Score. The relationship between patients’ CARG Score Risk

Group and CSHA Clinical Frailty Scale is explored in Supplementary Table 1, with these

measures being independent (p-value for Fisher’s exact test of 0.46). Treatment plans

compared to standard treatment for a younger, fitter patient with the same type and stage of

cancer were: ‘no different’ for 17 patients (57%); ‘same regimen at reduced dose’ for 4 patients

(13%); ‘less intensive regimen at standard dose’ for 5 patients (17%); and ‘less intensive

regimen at reduced dose’ for 4 patients (13%).

The median time from clinic consultation to: (i) GA was 5.5 days (range 0-23 days); and (ii)

start of chemotherapy was 6 days (range 0-23 days). The median time from GA to the start date

for chemotherapy was 0 days (range 0-7 days), with most performed on day 1 of treatment.

Though not mandated in the study design, for 11 patients the results of the GA and CARG

Score were presented to their treating oncologist prior to the start of planned chemotherapy

The GA was consistent with oncologists’ ‘overall clinical impression’ for most patients (24,

80%). The GA provided oncologists with new information for 12 patients (40%) as follows:

functional status (n=6), cognition (n=3), psychological health (n=3), polypharmacy (n=3),

comorbidity (n=2), nutrition (n=6), and social supports (n=2). The GA triggered interventions

not otherwise considered for seven patients (some with ≥1 intervention): social work (one

182

patient); dietitian (four patients); psychologist or psychiatric service (one patient); medication

review (one patient), and community services (one patient).

Oncologists reported that they would have modified their chemotherapy recommendation

based on the GA for none of the 30 patients, and based on the CARG Score for 2 (7%) patients;

one patient with a CARG Score of 4 (low risk) would have been changed to a more intensive

regimen and a patient with a score of 12 (high risk) would have been changed to a less intensive

regimen. (Figure 1) Oncologists thought the GA was useful for most patients (26, 87%) and

easy to interpret (29, 97%), and the CARG Score was useful for most patients (25, 83%) and

easy to interpret (30, 100%). (Figure 2) Perceived barriers to the implementation of GA and

CARG Score are in Table 3, with recurring themes being time and uncertainty about

contribution to decision-making and usual practice.

7.6 Discussion

Key findings of this study were that oncologists found the results of a GA and CARG Score

useful for most patients but were unlikely to use them to make changes to recommendations

about chemotherapy. Patients’ performances on a GA were mostly consistent with the clinical

impression of their treating oncologist, but for some the GA provided new information that

prompted supportive interventions (in 23%).

The GA provided new information and prompted non-oncological supportive interventions for

one in four patients, lower than other studies. In a recent systematic review by of 19 studies,

Hamaker et al (87) identified at least one non-oncological intervention occurred for a median

of 72% of patients (range 26 to 100%) undergoing GA in the oncology setting. Some studies

reported any intervention following a GA, (281) whereas others required the intervention

183

would not have happened as part of usual care (as in our study), (63) in part explaining the

wide range of results. Oncologists in our study reported that the GA was consistent with their

overall clinical impression for most patients. This implies that, whilst not having formally

assessed geriatric domains, oncologists had formed a clinical impression of these and were not

surprised by their patient’s performance on formal GA measures, and may explain the

comparatively lower rate of interventions prompted by the GA in our study.

Oncologists would not use the results of the GA to modify their chemotherapy recommendation

in our study. Possible reasons for this include choice-supportive bias (a reluctance to report a

different decision might have been present once a decision has already been made), lack of

experience with GA, inconsistent evidence regarding the components of the GA that best

predict treatment toxicity, (82, 92) fear about under-treatment, and again the reported

consistency of the GA with oncologists’ overall clinical impression. Using similar

methodology, Decoster et al (303) found cancer treatment plans for older adults (n=902) were

modified from standard therapy based on clinical judgement for 44% (43% in our study), with

further changes based on GA in only an additional 6%. The recent review by Hamaker et al

(87) found higher rates of change in treatment decisions based on GA. Across 11 studies that

reported treatment choice before and after GA, a change in oncological management (not only

chemotherapy) occurred for a median of 28% of patients (range 8-54%), the majority to less

intensive treatment. Methodologic differences between the studies included in this review,

particularly with regard to the baseline treatment plan used as the comparator, should be noted.

For example, if the baseline treatment plan was nominated prior to a cancer specialist seeing

the patient, any modifications made based on clinical judgement that was separate to the effect

of the GA might be missed, overestimating the impact of the GA on treatment decisions.

184

To our knowledge, oncologists have not previously been asked to report on the impact of the

CARG Score on their chemotherapy recommendation and prescribing. We have previously

shown that expected rates of chemotherapy toxicity influence chemotherapy recommendations.

(311) As a chemotherapy toxicity risk score, it would be anticipated that the CARG Score

would have a similar influence. Three studies (130, 149, 317) have shown a lack of correlation

between oncologists’ estimates of the likelihood of chemotherapy toxicity and a patient’s

CARG Score, raising potential for the score to be providing information different to clinical

judgement. Oncologists in our study were unlikely to use the CARG Score to guide

chemotherapy treatment decisions. One reason for this is the observation that for 13 patients

(43%) the chemotherapy treatment plan had already been modified based on clinical

assessment. Other potential reasons proposed by the authors include lack of familiarity with

the score, challenges translating the score into modifications to chemotherapy, and uncertainty

about its local applicability. Whilst our larger prospective study (317) did not demonstrate

predictive value of the CARG score in our population, oncologists in this substudy were not

aware of this when completing the questionnaires.

Nishijima et al (149) determined the value of the CARG Score in decision-making about

chemotherapy by assessing the agreement between treatment decision (reduced or standard

intensity chemotherapy) based on clinical impression and based on the CARG Score in 58 older

adults. An assumption of this study was that patients with a CARG Score ≥10 (high-risk)

should be recommended reduced intensity chemotherapy. Patients who received standard

intensity chemotherapy (based on oncologist impression) yet had a CARG Score ≥10 had

higher rates of severe toxicity (88% v 40%, p=0.006), suggesting the addition of the CARG

Score to clinical judgement may improve treatment decision-making, at least for high-risk

patients. Whether oncologists would modify their treatment recommendations for these high-

185

risk patients, considering there may be competing benefits of proceeding with standard

intensity treatment in some settings, is a question our study sought in part to explore. For only

one of the five patients in our study with a CARG Score of ≥10 would their oncologist have

changed their recommendation to a less intensive chemotherapy regimen. This is a recognised

area for further enquiry.

Strengths of this study include providing novel local data on the value and use of the GA and

CARG Score and being the first study to seek to evaluate oncologist reported impact of the

CARG Score on chemotherapy recommendations. Limitations include the small number of

oncologists (n=8) and centres (n=2), thus results are unlikely to reflect the views of all

Australian oncologists practicing in varied geographic settings and practice types. The

oncologists involved did not routinely use GAs or screening and so their views may be biased

against their use. Choice-supportive bias is possible due to the design of the study, as

oncologists responded to the questionnaire after they had made a recommendation about

chemotherapy. The study focussed on the GA and CARG Score with respect to treatment

decision-making but did not evaluate other potential benefits or uses of these measures.

7.7 Conclusion

Oncologists found the results of a GA and CARG Score useful for their older patients

commencing chemotherapy. The GA was consistent with oncologists’ clinical impressions

for most patients and provided new information that prompted supportive interventions for 1-

in-4 patients. Oncologists were unlikely to modify their chemotherapy recommendations

based on the GA or CARG Score, and as such their potential to impact decision-making

about chemotherapy prescribing in this setting was low. Barriers to the use of such tools in

routine practice, and their recommended role in guiding chemotherapy treatment decision-

186

making and prescribing, need to be addressed to facilitate implementation of these tools into

routine clinical practice.

187

Table 1. Patient (n=30) characteristics

Characteristic n (%)

Sex Male

Female

19 (63)

11 (37)

Cancer centre Concord Cancer Centre

The Chris O’Brien Lifehouse

14 (47)

16 (53)

Age 65 to 69 years

70 to 74 years

75 to 79 years

≥80 years

Median age (years)

8 (27)

4 (13)

12 (40)

6 (20)

75.5 years

Employment status Retired or not working

Working

28 (93)

2 (7)

Marital status Married / de facto

Widowed

Divorced / separated

Single

18 (60)

1 (3)

7 (23)

4 (13)

Living arrangements Lives with others

Lives alone

Care facility

7 (23)

22 (73)

1 (3)

Language spoken at home English

Non-English

22 (73)

8 (27)

Receiving community services Yes

No

5 (17)

25 (83)

Cancer type Colorectal

Ovarian

Upper gastrointestinal*

Lung / pleura

Prostate

Bladder

Other

14 (47)

4 (13)

3 (10)

3 (10)

3 (10)

1 (3)

2 (7)

Stage of cancer I

II

III

IV

0 (0)

1 (3)

12 (40)

17 (57)

Line of treatment Neoadjuvant

Adjuvant

1st line palliative

Subsequent line palliative

2 (7)

8 (27)

13 (43)

7 (23)

Chemotherapy regimen Single agent

Combination chemotherapy

13 (43)

17 (57)

Primary G-CSF Yes

No

0 (0)

30 (100)

Initial dose plan for cycle 1** Dose reduced

Standard dose

10 (33)

20 (67)

*Upper gastrointestinal includes pancreaticobiliary, gastric, and oesophageal cancers

**Initial dose plan for cycle 1 was defined as per the item of the Cancer and Aging Research Group’s

(CARG) Toxicity Score, as standard or reduced dose for that regimen according to NCCN guidelines

188

Table 2. Baseline geriatric assessment results (N=30)

Characteristic Category n(%) Median Range Range of scores

Self-rated health Excellent or very good

Good, fair or poor

13 (43)

17 (57)

CSHA Clinical Frailty Rating (176) Fit, or well

Vulnerable or frail

21 (70)

9 (30)

Performance status

ECOG Performance Status (150)

Karnofsky Performance Rating Scale (151)

0 or 1

≥2

90-100

80

≤70

23 (77)

7 (23)

11 (37)

12 (40)

7 (23)

Functional status

Katz Activities of Daily Living (77)

OARS Instrumental ADLs (158)

MOS Physical Functioning (159)

Timed Up and Go (154)

Falls in last 6 months

Independent (score 6)

Dependent ≥1 task

Independent (score 14)

Dependent ≥1 task

No limitation

Some limitation

≥14s

<14s

Yes

29 (97)

1 (3)

19 (63)

11 (37)

1 (3)

29 (97)

4 (13)

21 (70)

6 (20)

6

14

26

11.7s

5 - 6

4 - 14

15 - 30

7.3 -21.5

0 - 6

0 - 14

10 - 30

secs

Comorbidities

CIRS-G Total Score (161)

CIRS-G Index

Severe comorbidity

Life threatening comorbidity

2 (7)

2 (7)

3.5

2

0 - 10

1 - 3

Polypharmacy

Number of medications

2

0 - 11

Social Supports

Social Support Survey (167)

Complete support

Some deficit in support

19 (63)

11 (37)

20

4 - 20

0 - 20

189

Mood and Cognition

5-Item Geriatric Depression Scale (165)

Orientation-Memory-Concentration (162)

Score 0 or 1

Score ≥2 (abnormal)

Score 0 to 4 (normal)

Score ≥5

27 (90)

3 (10)

25 (83)

5 (17)

0

2

0 – 3

0 - 14

0 – 5

0 – 30

Nutrition

Weight loss in last 6 months

Mini-Nutritional Assessment Short Form (169)

Yes

Normal (score 12-14)

At risk (score 8-11)

Malnourised (score 0-7)

22 (73)

13 (43)

16 (53)

1 (3)

11

6 - 14

0 – 14

G8 (33) >14

≤14 (at risk)

11 (37)

19 (63)

Geriatric assessment score* 0 or 1

2 or 3

≥4

15 (50)

14 (47)

1 (3)

CARG Toxicity Score (49) Low-risk

Intermediate-risk

High-risk

7 (23)

18 (60)

5 (17)

8 4 - 12 0 – 23

Abbreviations: CSHA- Canadian Study of Health and Aging; ECOG- Eastern Cooperative Oncology Group; OARS- Older Americans Resources and Services; ADL-

Activities of Daily Living; MOS- Medical Outcomes Study; CIRS-G- Cumulative Illness Rating Scale in Geriatrics; G8- Geriatric 8; CARG Toxicity Score- Cancer and

Aging Research Group’s Toxicity Score

*Geriatric assessment score (range 0 to 7) is a summary score for the geriatric assessment performed, where a point is scored for a deficit in a geriatric health domain as

follows:

- performance status ECOG 2 or more

- functional status: TUG >/= 14s, any dependency in ADLs

- nutrition: MNA at risk or malnourished

- cognition: at risk or likely consistent with dementia

- social supports: <18 (lowest quartile)

- psychological state: GDS >/= 2

- comorbidity: CIRS G score >6 (highest quartile)

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Table 3. Comments from Oncologists on barriers to use of the Geriatric

Assessment and Cancer and Aging Research Group’s (CARG) Toxicity

Score

Question Comments

What do you see as the main barriers

to the implementation

of a GA in clinical practice?

“Time limitation in a busy clinic, lack of support staff and space to

conduct the assessment”

“Time”

“Ease of access and time”

“Limited clinic time”

“Expertise and time”

“Time, uncertain how formal assessment will change decision-

making”

“Time in a busy practice”

“Uncertainty about its value / benefit; and time”

What do you see as the main barriers

to the implementation

of the CARG Score in clinical

practice?

“Time, practical aspects for example, it would need to be added to

the electronic medical record for ease of completion and storage;

training on how to complete it; not convinced it improves on current

practice”

“Time”

“Time, space, and staff”

“Limited clinic time”

“Expertise and time”

“Time, and uncertain if it adds to clinical assessment”

“Time in a busy practice”

“Uncertainty about its value; and time”

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Figure 1. Perceived clinical value and impact on chemotherapy prescribing of the

Cancer and Aging Research Group’s (CARG) Score and Geriatric Assessment. For the

30 enrolled patients, their treating oncologist was asked to complete a questionnaire regarding

the GA and CARG Score in that patient. The proportion of responses in each answer category

reflect the proportion of patients for whom their treating oncologist answered ‘yes’ or ‘no’ to

the presented questions.

0% 20% 40% 60% 80% 100%

Would you have modified your existing chemotherapyrecommendation on the basis of the patient's CARG

Score?

Would you have modified your existing chemotherapyrecommendation on the basis of any of the information

gained from the GA?

Was the information contained in the GA consistentwith your clinical impression?

Did the GA provide you with any new information aboutyour patient?

% of patientsNo Yes

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Figure 2. Oncologist ratings of ease of use of the Cancer and Aging Research Group’s

(CARG) Score and Geriatric Assessment. For the 30 enrolled patients, their treating

oncologist was asked to complete a questionnaire regarding the GA and CARG Score in that

patient. The proportion of responses in each answer category reflect the proportion of patients

for whom their treating oncologist agreed, on a 5-point Likert scale, with the statements

presented.

0% 20% 40% 60% 80% 100%

I found geriatric assessment useful

I found the geriatric assessment easy tointerpret

I found the CARG Score useful

I found the CARG Score easy to interpret

Strongly disagree Disagree Neutral Agree Strongly Agree

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Supplementary Table 1

Patients’ Cancer and Aging Research Group’s (CARG) Toxicity Risk Group by

Oncologist-rated Clinical Frailty Rating

CARG Toxicity Score (8) Risk Group*

CSHA Clinical Frailty (31) Group ** Low-risk Intermediate-risk High-risk

Fit or well 4 14 3

Vulnerable or frail 3 4 2

p-value for Fisher’s Exact Test = 0.46

*Cancer and Aging Research Group’s Toxicity Score (range 0 to 23) grouped as follows:

Low-risk, score of 0 to 4; intermediate-risk, score of 5 to 9; high-risk, score ≥10

**Canadian Study on Health and Aging (CSHA) Clinical Frailty Ratings (1 through 7) dichotomised as

follows:

Fit or well refers to clinician ratings of ‘1 - very fit’, ‘2 - well’, ‘3 - well with treated comorbid disease’

Vulnerable or frail refers to clinican ratings of ‘4 - apparently vulnerable’, ‘5 - mildly frail’, ‘6 -

moderately frail’, or ‘7 - severely frail’

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8. Estimating survival time in older adults with

advanced cancer

8.1 Overview

This is a published chapter aimed at a general oncology clinical audience. Informed decision-

making involves a shared understanding of expected outcomes, including survival time. This

allows oncologists and their patients a context within which to weigh the likely benefits and

harms of chemotherapy, and to prioritise goals for care. The accuracy and nature of

oncologists’ estimates of survival time for older adults commencing palliative chemotherapy

for an advanced cancer are described in this chapter. The prognostic value of measures from a

geriatric assessment, including oncologist assessed frailty and performance status, are also

explored. The published manuscript is included verbatim.

Publication details

Moth EB, Kiely BE, Stefanic N, Naganathan V, Martin A, Grimison P, Stockler MR, Beale

P, Blinman P. Estimating survival time in older adults with advanced cancer. J Geriatr Oncol,

2019. Doi: 10.1016/j.jgo.2019.08.013. [Epub ahead of print]

Contribution of authors

I, Dr Erin Moth, contributed to study conception and design, was responsible for ethics

submissions, data acquisition, data analysis and interpretation of results, preparation of the draft

manuscript, manuscript editing and revision.

Dr Natalie Stefanic contributed to study design, data acquisition, interpretation of results,

manuscript editing and revision.

195

Prof Vasi Naganathan contributed to study conception and design, interpretation of results,

manuscript editing and revision.

A/Prof Andrew Martin contributed to study conception and design, data analysis, interpretation

of results, manuscript editing and revision.

A/Prof Peter Grimison contributed to study design, interpretation of results, manuscript editing

and revision.

Prof Martin Stockler contributed to study design, interpretation of results, manuscript editing

and revision.

A/Prof Philip Beale contributed to study design, interpretation of results, manuscript editing

and revision.

Dr Prunella Blinman contributed to study design, interpretation of results, manuscript editing

and revision.

Dr Belinda Kiely contributed to study conception and design, interpretation of results,

manuscript editing and revision.

Acknowledgement of funding

This project was funded by a Sydney Local Health District Cancer Services Research Grant

(CIA Dr EM, other investigators: BK, PLB, PG, VN). Dr EM was supported in this work by

two PhD scholarships: a University of Sydney Australian Postgraduate Award (APA), and PhD

funding support from Sydney Catalyst: the Translational Cancer Research Centre of Central

Sydney and regional NSW, University of Sydney, NSW, Australia and Cancer Institute NSW.

196

8.2 Abstract

Purpose

We determined the accuracy of oncologists’ estimates of expected survival time (EST) for older

adults with advanced cancer, and explored predictors of survival from a geriatric assessment

(GA).

Methods

Patients aged ≥65 years starting a new line of palliative chemotherapy were eligible. For each

patient at enrolment, oncologists estimated EST and rated frailty (Canadian Study on Health

and Aging Clinical Frailty Scale, 1=very fit, to 7=severely frail), and a researcher completed a

GA. We anticipated estimates of EST to be: imprecise [<33% between 0.67-1.33 times the

observed survival time (OST)]; unbiased (approximately 50% of participants living longer than

their EST); and, useful for estimating individualised worst-case (10% living ≤¼ times their

EST), typical (50% living half to double EST), and best-case (10% living ≥3 times EST)

scenarios for survival time. Logistic regression was used to identify independent predictors of

OST.

Results

The 102 participants [median age 74 years, vulnerable to frail (4-7 on scale) 35%] had a median

OST of 15 months. 30% of estimates of EST were within 0.67-1.33 times the OST. 54% of

participants lived longer than their EST, 9% lived ≤1/4 of their EST and 56% lived half to

double their EST. Follow-up was insufficient to observe those living ≥3 times their EST.

Independent predictors of OST were frailty (HR 4.16, p<0.0001) and cancer type (p=0.003).

197

Conclusions

Oncologists’ estimates of EST were imprecise, but unbiased and accurate for formulating

scenarios for survival. A pragmatic frailty rating was identified as a potentially useful predictor

of OST.

198

8.3 Introduction

Oncologists are frequently asked to provide information about expected survival times to

patients with advanced cancer. This information helps patients and their caregivers make

decisions about treatment, set goals, establish priorities, and make plans concerning future care.

Prognostic information is desired by most patients. (21, 23) For older adults with advanced

cancer, decision-making about palliative systemic treatment can be complex, particularly with

regard to balancing its potential benefits and harms in the setting of comorbidities, coexistent

functional impairments, and concern about treatment toxicity. Patient goals may also differ

from younger patients, often with an emphasis on maintenance of function over length of life.

(318) Estimates of survival time for patients with advanced cancer are frequently imprecise,

(319) with a tendency for health professionals to overestimate survival time, especially for

patients close to the end-of-life with a median observed survival of <90 days. (183, 320-323)

The heterogeneity of older adults with cancer, their demonstrated poorer survival compared to

younger patients, (324) and their relative under-representation in the pertinent clinical trials

that often inform estimates of expected survival times, (201, 325) may alter the accuracy and

nature of oncologists’ estimates of expected survival time in this cohort.

We have previously shown that presenting prognostic information to patients in the form of

expected best-case, typical, and worst-case scenarios for survival time offers hope, reassurance,

and is preferred over a single point estimate of median survival. (38) These scenarios for

survival time can be calculated using simple multiples of the observed median survival time in

a clinical trial to estimate the outcomes of the other patients in that trial, or by using simple

multiples of the expected survival time estimated for an individual patient. (36, 37, 39, 41-43,

326, 327) Our previous studies have shown that 5 to 10% of patients live ≥3 times the median

survival time (best-case scenario), about 50% will live between half and double the median

199

survival time (typical scenario), and 5 to 10% will live ≤ 1/4 times the median survival time

(worst-case scenario). (41) The accuracy of using these simple multiples of an oncologist’s

estimate of “expected survival” to calculate expected worst-case, typical and best-case

scenarios for individual patients has been demonstrated in patients with mixed advanced cancer

types (median age 64 years) starting palliative chemotherapy in routine practice. (41) How this

method of using simple ‘multiples of the expected survival time’ to calculate scenarios for

survival applies to older adults receiving chemotherapy for advanced cancer in everyday

practice is not known.

There may be other ways to inform survival times for older adults with cancer. Prognostic tools

in older adults have largely been developed for use in the setting of hospitalisation, (328) in

the community to inform decisions about cancer screening, (329, 330) or for those with

advanced cancer close to the end-of-life. (331-333) An assessment of multiple health domains

using a ‘geriatric assessment’ (GA) is recommended for older adults with cancer (78, 316)

although the particular components of the GA that are most predictive of survival are unclear.

(82, 88, 92) From the GA, a summary measure to indicate frailty can be derived which has

been shown to be predictive of all-cause mortality. (93, 334) Frailty can be thought of as a

condition of decreased physiological reserve and ability to withstand stressors. (176, 335)

Frailty can be determined without the need for a GA which is time consuming. The Canadian

Study on Health and Aging (CSHA) Clinical Frailty Scale (176) is a subjective measure of

frailty that has appeal for use in the oncology setting as a single-item, easy-to-use subjective

tool that can be completed by the oncologist. It has been shown to predict mortality in general

geriatric populations, (336-338) but there is a lack of evidence for its use in the oncology

setting.

200

The primary objective of this study was to determine the nature and accuracy of oncologists’

estimates of survival time for older adults with advanced cancer being treated with

chemotherapy. The secondary exploratory objective was to identify useful predictors of

observed survival in this cohort, using measures from a brief geriatric assessment (GA), that

included oncologists’ assessments of frailty.

8.4 Methods

8.4.1 Participants

Participants were selected from a larger prospective study that compared oncologists’

predictions of chemotherapy toxicity with the Cancer and Aging Research Group’s (CARG)

Toxicity Score; and evaluated the impact of the GA on decision-making about chemotherapy.

(317, 339) Participating patients in these studies (N=156) were aged ≥65 years, and starting an

initial or new line of cytotoxic chemotherapy for a solid organ cancer of any type or stage, and

were recruited consecutively at two tertiary referral cancer centres in Sydney. For this substudy,

only patients who had an incurable cancer were included. Patients with stage III disease were

included if unable to tolerate curative multimodality treatment. Supplementary Figure 1

outlines the sample selection. There was no lower limit on anticipated survival time for patients

to be included in this study, though all had been judged appropriate to receive chemotherapy.

Participating oncologists were the treating medical oncologists of participating patients. All

patients provided written, informed consent. Ethics approval was granted by the Sydney Local

Health District’s Human Research Ethics Committee (Concord Repatriation General Hospital),

(HREC/15/CRGH/102).

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8.4.2 Procedures

At enrolment, oncologists recorded an estimate of expected survival time (EST) in months for

each patient (the estimated “median survival for a group of identical patients”). Oncologists

also recorded Eastern Cooperative Oncology Group (ECOG) performance status (150) and

frailty by the CSHA Clinical Frailty Scale. (176) The CSHA Clinical Frailty Scale asks

clinicians to classify patients into one of seven categories ranging from ‘very fit’ to ‘severely

frail’ using clinical judgement and guided by a description of general appearance, comorbidity,

and comparison to peers. An abbreviated GA was then completed with each patient by a

researcher (EM, NS). Geriatric health domains assessed by GA were: functional status by the

Timed Up and Go, (154) Katz Index of Activities of Daily Living, (157) OARS

Multidimensional Functional Assessment Instrumental Activities of Daily Living, (158) the

Medical Outcomes Study (MOS) Physical Functioning Scale, (159) and history of falls;

comorbidity by the Cumulative Illness Rating Scale in Geriatrics (CIRS-G); (161) cognition

by the Short Blessed (Orientation Memory Concentration) Test; (162) psychological health by

the Geriatric Depression Scale 5-Item Short Form (GDS-5); (165) social supports by the

modified MOS Social Support Survey; (167) nutrition by the Mini Nutritional Assessment

Short Form (MNA-SF); (169) and chemotherapy toxicity risk by the CARG Toxicity Score.

(49) Oncologists were not aware of the results of the GA when assessing EST and frailty. Data

on survival was captured prospectively.

8.4.3 Statistical analysis

Survival analysis was conducted at a timepoint 13 months from the date of enrolment of the

last participant. We calculated the ratio of observed survival time (OST) to EST for each

patient. The distribution of OST/EST ratios was estimated from a Kaplan-Meier analysis to

accommodate instances where OST was censored. The accuracy of oncologists’ estimates of

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EST was determined by the proportions of patients with OSTs falling within simple multiples

of their oncologist’s estimate of EST. We hypothesised that oncologists’ estimates of EST

would be unbiased, meaning approximately 50% of patients would live longer than their EST

and 50% would live less than their EST. Based on previous work, (37, 39, 41, 326, 327) we

anticipated approximately 5 to 10% of patients would die within one quarter of their

oncologist’s estimate (OST/EST <0.25, worst-case scenario), 50% would live from half to

double their oncologist’s estimate (OST/EST between 0.5 and 2, typical range), and 5 to 10%

would live three or more times their oncologist’s estimate (OST/EST of ≥3, best-case scenario).

For consistency with previous studies (37, 41, 183-185) we defined an oncologist’s estimate of

EST as ‘precise’ if it fell within 0.67 to 1.33 times the OST. This method allows a wider interval

for precision for patients with longer observed survival times, where estimates are inherently

more uncertain, than for patients with shorter observed survival times.

Associations between OST and prespecified clinical and GA variables, including EST, were

assessed using Cox proportional hazards regression. We dichotomised continuous variables for

the regression analysis as follows (cutpoints taken from sample medians): BMI <26kg/m2

versus ≥26kg/m2; creatinine >78mmol/L versus ≤78mmol/L; and haemoglobin <125g/dL

versus ≥125g/dL. The ordinal scale of the CSHA Clinical Frailty Scale (176) was dichotomised

for inclusion in the regression analysis as follows: “very fit”, “well”, and “well, with treated

comorbid disease” (scores 1 to 3) were grouped as “fit to well”; and “apparently vulnerable”,

“mildly frail”, “moderately frail”, and “severely frail” (scores 4 to 7) were grouped as

“vulnerable to frail”. Variables that reached the 5% level of significance on univariable analysis

were included in the multivariable analysis using stepwise backward elimination. Oncologists’

estimates of EST did not demonstrate significant variance to justify use of a random effects

203

model. Relationships between variables included in the regression analysis were explored using

Spearman’s rank correlation coefficient.

8.5 Results

8.5.1 Participant characteristics

Between August 2015 and March 2017, 102 patients with a median age of 74 years (range 65

to 86) were enrolled by 10 oncologists at two centres. Patient characteristics are summarised

in Table 1 and findings on GA in Table 2. The most common cancer type was colorectal (33%)

followed by upper gastrointestinal (16%). Most patients were receiving palliative

chemotherapy in the first-line setting (67%). Oncologists rated most patients to be of good

performance status (ECOG-PS 0/1, 80%), and ‘fit to well’ (scores 1 to 3) on the frailty scale

(65%). The frequency distribution of ECOG-PS v CSHA Clinical Frailty Scale is in

Supplementary Table 1.

8.5.2 Observed and estimated survival times

After a median follow-up of 19 months (range 0 to 27 months), there were 58 deaths (57%).

One participant did not return for follow-up after baseline assessments. The median OST was

15 months (range 0.5 to 27+ months). Figure 1 shows the frequency distribution of oncologists’

estimates of EST. The median estimate of EST was 15.5 months (range 4 to 60 months). Most

estimates of EST were multiples of 3 or 4 months (97%), or of 6 months (80%). The shortest

EST of 4 months was for a 72 year-old male, ECOG-PS of 2, being treated with dose-reduced

capecitabine/oxaliplatin for advanced colorectal cancer. The longest EST of 60 months was for

a 70 year-old male, ECOG-PS of 0, starting standard dose docetaxel for advanced castrate

resistant prostate cancer. Exploratory analysis revealed oncologists’ estimates of EST were

204

significantly associated with cancer type, ECOG-PS, CSHA Clinical Frailty Scale, and CARG

Toxicity Score.

8.5.3 Accuracy of estimated survival time

Oncologists’ estimates of EST were unbiased (no systematic tendency towards over-estimation

or under-estimation), with 46% of patients (95%CI 35 to 56%) living shorter than their EST

and 54% (95%CI 47 to 58%) living longer than their EST. Oncologists’ estimates of EST were

imprecise; only 30% of patients (95%CI 18 to 42%) had an estimate of EST within 0.67 to 1.33

times their OST. 9% of patients (95%CI 4 to 14%) lived ≤ one quarter of their EST (worst-case

scenario, expected 5-10%); 56% of patients (95%CI 42 to 69%) lived half to double their EST

(typical scenario, expected 50%), and due to insufficient follow-up, no patients were observed

to live three or more times their EST (best-case scenario, expected 5-10%). A plot of the

relationship between EST and OST is in Figure 2. Kaplan-Meier curves of observed and

expected survival times are in Figure 3A. Oncologists’ estimates of EST had moderate

discriminative value (c-statistic of 0.64).

8.5.4 Predictors of observed survival time

Predictors of OST on univariable and multivariable analysis are shown in Table 3. Independent

predictors of OST were CSHA Clinical Frailty Scale (HR 4.16, 95%CI 2.34-7.40, p<0.0001)

and cancer type (p=0.003, hazard ratios for cancer types referent to prostate cancer are shown

in Table 3). There was a 4-fold increase in the risk of death for patients classified as ‘vulnerable

to frail’ (CSHA Frailty Scale of 4-7) compared to patients classified as ‘fit, to well with treated

comorbidity’ (CSHA Frailty Scale of 0-3). Figure 3B shows observed survival by frailty group.

Patients with upper gastrointestinal, breast and lung cancers had more than 5-fold increase in

the risk of death compare to patients with prostate cancer.

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Because the accuracy of cancer type as a predictor of OST may be affected by the small

numbers within each subgroup, a multivariable analysis excluding cancer type was also

performed. When cancer type was excluded CSHA Clinical Frailty Scale (HR 2.78, 95%CI

1.57-4.93, p=0.0004) and EST (HR 0.96, 95%CI 0.93-0.99, p=0.03) were independently

associated with OST. Here, the hazard ratio for EST represents a 4% reduction in the risk of

death for every one month increase in estimated survival time.

8.6 Discussion

Oncologists’ estimates of expected survival time for older adults commencing chemotherapy

for advanced cancer were unbiased (no systematic tendency towards over- or under-

estimation), yet as point estimates were imprecise. Multiples of each individual’s expected

survival time were accurate for estimating individualised typical (half to double EST), and

worst-case (≤ one-quarter EST) scenarios for survival. Follow-up was too short to observe the

accuracy of using ≥3 times EST to determine the accuracy of the individualised best-case

scenario; however based on our previous studies, we expect this to also be accurate. (37, 326,

327) In this study, cancer subtype and a simple, pragmatic rating of frailty by the treating

oncologist were independently associated with OST.

Oncologists’ estimates of expected survival time in our study were unbiased but imprecise,

consistent with studies in patients of all ages with advanced cancer. (37, 41, 327, 340) For

example, in one study assessing the accuracy of oncologists’ estimates of survival time for 114

patients with advanced cancer and a median age of 63 years (341) 29% of estimates met the

same arbitrary definition of precision (within 0.67 to 1.33 times the observed survival time).

(37) Similarly, in two further studies: one involving 152 patients with advanced gastric cancer

and a median age of 62 years, (327) and, the other in a cohort of routine practice patients with

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advanced cancer (median age 64 years), (41) 29% of survival estimates in both studies were

precise by similar definitions. The consistent imprecision of a single point estimate of survival

time is a result of the inherent variability of survival time in the setting of advanced cancer,

regardless of patient age.

Medical oncologists’ estimates of expected survival time in our study did not show a tendency

towards either optimism or pessimism. It is generally considered that oncologists have a

tendency to overestimate survival, however we and others have shown this is not true,

especially for patients with recently diagnosed advanced cancers and median survival times

around 12 months. (37, 41, 340) Overly optimistic estimates of survival are more frequent

when patients are at the end of life with survival times measured in days. (183, 322) For

example, in a study of 468 patients referred to a hospice who had a median survival of 24 days,

63% of estimates of survival time made by referring physicians were too optimistic (and 17%

too pessimistic). (183) Variation in biases of survival time estimates between studies may also

be explained, in part, by differences in the expertise of clinicians providing the estimates.

Number bias was observed in our study, with oncologists preferentially selecting estimates of

expected survival time that were multiples of 3, 4 or 6 months. This is revealing of human

tendency to think in round numbers (or multiples), to place patients within groups (longer and

shorter survivors), and also of these time points being commonly used in the reporting of

survival in clinical trials.

Despite the imprecision of oncologists’ point estimates of expected survival time in our study,

the proportion of patients with an OST bounded by simple multiples of their EST was

remarkably close to what was expected from previous studies. (36, 37, 39, 41, 43, 327) We

expected and found that approximately 5 to 10% of patients died within one quarter of their

207

oncologist’s estimate (worst-case scenario), and 50% lived from half to double their

oncologist’s estimate (typical scenario). With longer follow-up we expect to find 5 to 10%

lived three or more times their oncologist’s estimate (best-case scenario). These typical, best-

case, and worst-case scenarios provide a useful framework for explaining survival time to

patients. When an older patient with advanced cancer requests information about their expected

survival time, we recommend their treating oncologist start by estimating the patient’s expected

survival time based on the estimated median in a group of similar patients. Scenarios for

survival time can then be calculated using multiples of this estimate, as follows: best-case

scenario ≥3 times the estimate, typical scenario half to double the estimate, and worst-case

scenario ≤1/4 times the estimate. To illustrate, if an oncologist estimates the survival for a

patient as 12 months, rather than providing the patient this single number estimate they could

explain that they would expect: 5-10 of 100 similar patients to live 36 months or longer, the

best-case scenario; about 50 of 100 similar patients to live between 6 and 24 months, the typical

scenario; and 5-10 of 100 similar patients to die within 3 months, the worst-case scenario. We

have previously shown that patients prefer this format of explaining expected survival time

finding the three scenarios make more sense, offer greater hope, and are more reassuring than

a single point estimate of median survival. (38)

While oncologists’ estimates of EST were significantly associated with OST on univariable

analysis, they were only independently associated with OST when cancer type was not

considered. Given we have previously shown oncologists’ estimates of EST are independently

predictive of OST in several other cohorts, further evaluation in a larger cohort of older patients

is warranted.

208

The CSHA Clinical Frailty Scale, (176) a simple and pragmatic measure of frailty, was

identified as an independent predictor of observed survival in our study. Though frailty was

retained as significant in the multivariable analysis over ECOG-PS, the hazards attributable to

each in the univariate analysis were comparable, and indeed there was an association between

the two. Such a measure of frailty is appealing for use in clinical practice by non-geriatricians,

given it is easy to use, and provides a single number summary assessment that considers more

than patient function. An interesting observation was that of patients given an ECOG-PS rating

of 1 by their oncologist, one-quarter were rated ‘vulnerable to frail’ suggesting the frailty scale

may be better than performance status in characterising a population of older patients. The

frailty scale also proved more valuable for predicting survival in this cohort than any one

component of the GA, which may have implications for services without the resources for GA

implementation, though recognising that a GA has value beyond that of informing prognosis.

8.6.1 Strengths and limitations

The main strengths of our study are its prospective design, a priori hypotheses, and inclusion

of ‘real-world’ older adults receiving chemotherapy, rather than clinical trial participants,

improving the applicability and generalisability of results to day-to-day clinical practice.

Collection of estimated survival times before starting a new line of chemotherapy is clinically

relevant, as this is often a time when prognostic discussions take place. This study provides

novel data on the value of the CSHA Clinical Frailty Scale for informing survival times in this

population.

The main limitations of our study are its modest sample size, which limits our ability to identify

statistically significant associations between baseline variables and survival, and limits the

precision of our estimates. Longer follow-up would result in more precise estimates of

observed survival time (44 of the 102 patients were still alive at the time of analysis), and in

209

particular would allow estimates of survival time in those who lived longest (only 2 deaths

among 10 patients observed for 30 months or longer). Patients in our study were being treated

with chemotherapy, and hence results cannot necessarily be generalised to those older adults

who decline chemotherapy or are not fit enough to receive chemotherapy. The small number

of oncologists (n=10) and centres (n=2) in the study also limits the wider generalisability of

the results. As each oncologist provided estimates of expected survival time for a small number

of their patients, variation in estimates (and their accuracy) driven by individual oncologist

factors was unable to be adequately evaluated in this dataset. We also do not know if the

estimates of survival provided by oncologists in this study were communicated to patients who

requested prognostic information.

8.6.2 Implications for practice and future research

When providing older patients with information about prognosis, oncologists should be aware

of the imprecision of a single point estimate of expected survival time. Our study supports the

use of this point estimate as a starting point for estimating and describing expected best-case,

typical, and worst-case scenarios for survival time for older adults with incurable cancer. A

simple, single-item subjective rating of frailty, the CSHA Clinical Frailty Scale, (176) provided

valuable prognostic information in older adults starting chemotherapy, and may perform as

well or better in everyday practice and clinical trials than traditional measures of performance.

The results of our study at least support further research to evaluate the utility of this frailty

measure in practice. Further research should also study older patients’ preferences for receiving

prognostic information, what informs oncologists’ estimates of survival time, and how

knowledge of expected survival time impacts on older patients’ preferences for palliative

systemic treatment.

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8.7 Conclusion

Oncologists’ estimates of expected survival time in older adults commencing chemotherapy

for advanced cancer were unbiased, imprecise, and provided a useful basis for describing

expected typical, best-case, and worst-case scenarios for survival time. Frailty, as assessed by

oncologists using the CSHA Clinical Frailty Scale, was identified as a predictor of observed

survival time in this older population, and warrants further study.

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Table 1. Characteristics of 102 participants

Characteristic Category N

Sex Male

Female

65

37

Age (years) Median = 74 (65 to 86)

65 to 69

70 to 74

75 to 79

≥80

26

28

36

12

ECOG Performance Status

(oncologist-assessed)

0

1

2

3

4

19

63

18

2

0

CSHA Clinical Frailty Scale

(oncologist-assessed)

1-Very fit

2-Well

3-Well, with treated comorbid disease

4-Apparently vulnerable

5-Mildly frail

6-Moderately frail

7-Severely frail

5

32

29

30

5

1

0

Employment status Retired or not working

Working

88

14

Marital status Married / de facto

Widowed

Divorced / separated

Single

71

10

10

11

Living arrangements Lives with others

Lives alone

Care facility

83

19

0

Language spoken at home English

Non-English

75

27

Community services Yes

No

10

92

Cancer type Colorectal

Upper gastrointestinal

Prostate

Gynaecological

Lung or pleura

Other genitourinary

Breast

Other*

34

16

13

10

9

9

6

5

Stage of cancer III**

IV

9

93

Line of chemotherapy for metastatic

disease

1st line

Subsequent line

68

34

Chemotherapy regimen Single agent

Combination chemotherapy

56

46

Abbreviations: ECOG, Eastern Cooperative Oncology Group; CSHA, Canadian Study on Health and

Aging

*Other cancer types included: glioblastoma multiforme (1), neuroendocrine tumour (2), adenocarcinoma

unknown primary (1), merkel cell carcinoma unknown primary (1)

**Patients with stage III disease were included if they were unable to tolerate curative treatment, and

therefore the intent if chemotherapy was palliative

212

Table 2. Geriatric assessment measures at baseline on 102 participants

Characteristic Category n Median Range

Range of

scores

Self-rated health Excellent, or very good

Good

Fair, or poor

34

42

25

Functional status

Katz Activities of Daily Living

(157)

OARS Instrumental ADLs (158)

MOS Physical Functioning (159)

Timed Up and Go (154)

Falls in last 6 months

Independent (score 6)

Dependent ≥1 task

Independent (score 14)

Dependent ≥1 task

No limitation

Some limitation

≥14s

<14s

Yes

89

13

56

46

96

7

12

84

17

6

14

26

2 - 6

4 - 14

14 - 30

0 - 6

0 - 14

10 - 30

Comorbidities CIRS-G (161) Total Score

CIRS-G Index

Number with category 3 (severe)

Number with category 4 (life-

threatening)

4

1.67

5

0 – 12

0.5 - 3

Polypharmacy Number of medications

3.5

0 - 16

Social Supports

Social Support Survey (167)

Complete social supports

Some deficit in support

60

42

20

4 - 20

0 - 20

Mood and Cognition

5-Item Geriatric Depression Scale

(165)

Orientation-Memory-Concentration

Test (162)

Score 0 or 1

Score ≥2 (abnormal)

Score 0 to 4 (normal)

Score ≥5

82

20

84

18

0

2

0 - 5

0 - 21

0 – 5

0 – 30

Nutrition Weight loss in last 6 months

Mini-Nutritional Assessment Short

Form (169)

Yes

Normal nutrition (score 12-14)

At risk (score 8-11)

Malnourised (score 0-7)

59

49

46

7

11

6 - 14

0 – 14

G8 Score (170) >14

≤14 (at risk)

38

64

Geriatric assessment score* 0 or 1

2 or 3

≥4

50

42

10

CARG Toxicity Score (49) risk

group

Low-risk

Intermediate-risk

High-risk

20

60

22

*Geriatric assessment score (range 0 to 7) is a summary score for the geriatric assessment performed, where a point

is scored for a deficit in a geriatric health domain as follows:

- performance status ECOG 2 or more

- functional status: TUG >/= 14s, any dependency in ADLs

- nutrition: MNA at risk or malnourished

- cognition: at risk or likely consistent with dementia

- social supports: <18 (lowest quartile)

- psychological state: GDS >/= 2

- comorbidity: CIRS G score >6 (highest quartile)

213

Table 3. Factors associated with observed survival time

Univariable analysis Multivariable analysis

#1*(all variables

significant on univariable

analysis included)

Multivariable analysis

#2** (all variables significant on

univariable analysis except

cancer type)

Variable HR 95% CI P HR 95%CI P HR 95%CI P

Estimate of expected

survival time (EST)

0.94 0.91-0.98 0.001 -

0.96 0.93-0.99 0.03

ECOG PS 0 or 1 0.34 0.19-0.62 0.0004 - -

Age group (years) 65 to 69

70 to 74

75 to 79

≥80

0.58

0.69

0.62

Ref

0.23-1.39

0.29-1.60

0.27-1.40

Ref

0.63

Cancer type Breast

Colorectal

Upper GIT

Lung / pleura

Gynaecological

GU (other)

Other

Prostate

5.12

2.71

6.64

3.79

1.20

1.68

1.64

Ref

1.37-19.15

0.92-8.00

2.16-20.41

1.07-13.50

0.27-5.35

0.42-6.72

0.30-8.98

Ref

0.007 5.45

2.75

6.36

5.79

0.97

1.91

1.29

Ref

1.44-20.6

0.93-8.12

2.06-19.60

1.59-21.09

0.22-4.37

0.48-7.69

0.24-7.11

Ref

0.003

NA

Line of therapy First line 1.63 0.90-2.94 0.12

Haemoglobin‡ ≥125 (median) 0.58 0.34-0.98 0.04 - -

Creatinine‡ ≤78 (median) 0.97 0.58-1.63 0.91

CSHA Frailty Vulnerable to frail‡ 3.45 2.04-5.88 <0.0001 4.16 2.34-7.40 <0.0001 2.78 1.57-4.93 0.0004

Self rated health Excellent or very good

Good

Fair or poor

0.59

0.45

Ref

0.31-1.13

0.24-0.85

Ref

0.04 - -

Timed Up and Go <14 seconds 0.55 0.27-1.13 0.10

214

Katz ADLs Independent in all

ADLs

0.96 0.44-2.12 0.92

IADLs Independent in all

IADLs

0.48 0.28-0.81 0.006 - -

Cognition (OMC) Normal cognition (score

0-4)

0.90 0.47-1.73 0.75

Nutrition (MNA-SF) Normal nutritional

screening

0.64 0.38-1.10 0.10

BMI‡ ≥27 (median) 1.01 0.60-1.71 0.97

Weight loss No weight loss 0.52 0.30-0.90 0.02 - -

Comorbidity CIRS-G Score ≤4 0.65 0.38-1.09 0.10

CARG Toxicity Score Low risk

Intermediate risk

High risk

0.42

0.44

Ref

0.19-0.95

0.24-0.81

Ref

0.02 - -

g8 vulnerability score Not vulnerable (score

>11)

0.63 0.36-1.12 0.11

Geriatric assessment

score

Score 0 or 1 0.47 0.27-0.81 0.006 - -

Notes:

The hazard ratios reported represent the risk of death, such that a HR >1 represents an increased risk of death, and a HR<1 represents a reduced risk of death.

Abbreviations: NA = not analysed; ECOG-PS = Eastern Cooperative Oncology Group Performance Status; CSHA = Canadian Study on Health and Ageing; ADL = activities of daily living;

IADL = instrumental activities of daily living; OMC = orientation memory concentration test; MNA = Mini Nutritional Assessment Short Form; BMI = Body Mass Index; CARG = Cancer

and Aging Research Institute.

*Multivariable analysis #1: All variables on univariable analysis significant at the 0.05 level of statistical significance were included.

**Multivariable analysis #2: All variables on univariable analysis significant at the 0.05 level of statistical significance were included other than cancer type.

†Categories on the CSHA Clinical Frailty Scale were dichotomised for the regression analysis as follows: “Very fit”, “Well”, and “Well, with treated comorbid disease” on the CSHA

Clinical Frailty Scale grouped as “Fit to well”; “Apparently vulnerable”, “Mildly frail”, “Moderately frail”, and “Severely frail” on the CSHA Clinical Frailty Scale grouped as “Vulnerable to

frail”

‡Handling of BMI using clinical cutpoints [BMI ≥25 ‘overweight’ versus BMI <25 (only 1 patient with BMI <20 ‘underweight’)] rather than the sample median did not alter this result (HR

0.94, 95%CI 0.41-2.2, p=0.89). Handling of serum creatinine using clinical cutpoints for normal range (females <90, males <110) rather than the sample median did not alter this result (HR

0.76, 95%CI 0.25-2.27, p=0.62). Handling of haemoglobin using clinical cutpoints for normal range (females ≥120, males ≥130) rendered the association non-significant (HR 0.50, 95%CI

0.23-1.1, p=0.08). This did not alter the independent predictors of survival identified on subsequent multivariable analysis.

215

Figure 1. Distribution of oncologists’ estimates of expected survival time (EST) for

102 older adults commencing palliative chemotherapy

0

5

10

15

20

25

4 6 8 9 10 11 12 15 16 18 20 24 30 36 48 60

Nu

mb

er o

f p

atie

nts

Estimate of expected survival time (months)

216

Figure 2. Relationship between observed and estimated survival times for each

individual patient. Points falling on the 45-degree line represent people who lived exactly as

long as predicted. Points above the line represent people who lived longer than predicted, and

points below the line represent people who lived shorter than predicted. Note those who were

alive at last follow-up (represented by a triangle), may with longer follow-up have an observed

survival time that changes their position on the y-axis (and for some this may move them from

below to above the line).

0

10

20

30

40

0 6 12 18 24 30 36 42 48 54 60 66 72

Ob

serv

ed s

urv

ival

tim

e (m

on

ths)

Estimated survival time (months)

Patients who have died Patients who are still alive

217

A.

B.

C.

Figure 3. Kaplan Meier curves of: A. observed and estimated survival times for 102

older adults starting palliative chemotherapy; B. observed survival times by frailty; C.

estimates of expected survival time by frailty. Estimates of uncertainty (95% CI) are

provided for observed survival times.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 10 20 30 40 50 60

Ali

ve

(Pro

po

rtio

n)

Months

Expected survival time (EST)

Bounds of 95% CI for OST

Observed survival time (OST)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 5 10 15 20 25 30

Ali

ve

(pro

po

rtio

n)

Observed survival time (months)

Bounds of 95% CI

Apparently vulnerable, toseverely frail (CSHA ClinicalFrailty Scale 4-7)

Bounds of 95% CI

Very fit, to well with treatedcomorbidity (CSHA Clinical FrailtyScale 1-3)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 10 20 30 40 50 60Ali

ve

(pro

po

rtio

n)

Estimate of expected survival time (months)

Very fit, to well with treatedcomorbidity (CSHA Clinical FrailtyScale 1 to 3)

Apparently vulnerable, to severelyfrail (CSHA Clinical Frailty Scale 4 to7)

218

Supplementary Table 1. ECOG Performance Status by CSHA Clinical Frailty Scale

CSHA Clinical Frailty Scale (1 to 7)

ECOG-

PS Very fit Well Well, with treated comorbid disease Apparently vulnerable Mildly frail Moderately frail Severely frail Total

0 4 12 2 0 0 1 0 19

1 1 20 26 15 1 0 0 63

2 0 0 1 15 2 0 0 18

3 0 0 0 0 2 0 0 2

4 0 0 0 0 0 0 0 0

Total 5 32 29 30 5 1 0 102

219

Prior to chemotherapy:

· Geriatric assessment, CARG Score

· Oncologist-assessed ECOG-PS, CSHA Clinical Frailty

rating, estimate of expected survival time

Cohort 2, n=30

(Recruited consecutively)

Value of the GA to oncologists

evaluated (ref 40)

Prospective observational study (n=156)

Inclusion:

· Aged ≥65 years

· Solid organ cancer of any type or stage

· Starting initial or new line of chemotherapy

Exclusion: concurrent radiotherapy, targeted therapy

given as single agents, immunotherapy

Cohort 1, n=126

Value of oncologists’ estimates of

the likelihood of chemotherapy

toxicity compared to the CARG

Score evaluated (ref 41)

Patients with incurable disease

n=82

Subgroup reported here

n=102

Patients with incurable disease

n=20

Supplementary Figure 1. Schema

Subgoup selection for this analysis

220

9. Older adults’ preferred and perceived roles in

decision-making about palliative chemotherapy;

decision priorities, and information preferences.

9.1 Overview

This is a published chapter aimed at a general oncology clinical audience. Here, the role that

older adults considering palliative chemotherapy for a diagnosis of incurable cancer wish to

play in making a decision about systemic treatment is evaluated. The role they perceive they

have played when making a decision about treatment with chemotherapy is compared to their

preferred role. The priorities of older adults when making a decision to have (or not have)

chemotherapy, as well as their information needs and understanding of their prognosis is also

explored. The published manuscript is included verbatim.

Publication details

Moth EB, Kiely BE, Martin A, Naganathan V, Della-Fiorentina S, Honeyball F, Zielinski R,

Steer C, Mandaliya H, Ragunathan A, Blinman P. Older adults’ preferred and perceived roles

in decision-making about palliative chemotherapy, their decision priorities, and information

preferences. J Geriatr Oncol, 2019. Doi: 10.1016/j.jgo.2019.07.026. [Epub ahead of print]

Contribution of authors

I, Dr Erin Moth, was responsible for study conception and design, was responsible for ethics

submissions, survey distribution, data acquisition, management of data and data analysis,

interpretation of results, preparation of the draft manuscript, manuscript editing and revision.

221

Dr Belinda Kiely contributed to study conception and design, data acquisition, analysis and

interpretation of data, revision and approval of the final manuscript.

Prof Vasi Naganathan contributed to study conception and design, final interpretation of data,

revision and approval of the final manuscript.

A/Prof Andrew Martin contributed to study conception, analysis and interpretation of data,

revision and approval of the final manuscript.

A/Prof Stephen Della-Fiorentina contributed to data acqusition, final interpretation of results,

revision and approval of the final manuscript.

Dr Florian Honeyball contributed to data acqusition, final interpretation of results, revision and

approval of the final manuscript.

Dr Rob Zielinski contributed to data acqusition, final interpretation of results, revision and

approval of the final manuscript.

Dr Christopher Steer contributed to data acqusition, final interpretation of results, revision and

approval of the final manuscript.

Dr Hiren Mandaliya contributed to data acqusition, final interpretation of results, revision and

approval of the final manuscript.

Dr Abiramy Ragunathan contributed to data acqusition, final interpretation of results, revision

and approval of the final manuscript.

Dr Prunella Blinman contributed to study conception and design, data acquisition, analysis and

interpretation of data, revision and approval of the final manuscript.

222

Acknowledgement of funding

Dr EM was supported in this work by two PhD scholarships: a University of Sydney Australian

Postgraduate Award (APA), and PhD funding support from Sydney Catalyst: the Translational

Cancer Research Centre of Central Sydney and regional NSW, University of Sydney, NSW,

Australia and Cancer Institute NSW.

223

9.2 Abstract

Aim

Patients with cancer have varied preferences for involvement in decision-making. We sought

older adults’ preferred and perceived roles in decision-making about palliative chemotherapy;

priorities; and information received and desired.

Methods

Patients ≥65y who had made a decision about palliative chemotherapy with an oncologist

completed a written questionnaire. Preferred and perceived decision-making roles were

assessed by the Control Preferences Scale. Wilcoxon rank-sum tests evaluated associations

with preferred role. Factors important in decision-making were rated and ranked, and receipt

of, and desire for information was described.

Results

Characteristics of the 179 respondents: median age 74y, male (64%), having chemotherapy

(83%), vulnerable (Vulnerable Elders Survey-13 score ≥3) (52%). Preferred decision-making

roles (n=173) were active in 39%, collaborative in 27%, and passive in 35%. Perceived

decision-making roles (n=172) were active in 42%, collaborative in 22%, and passive in 36%

and matched the preferred role for 63% of patients. Associated with preference for an active

role: being single/widowed (p=0.004, OR=1.49), having declined chemotherapy (p=0.02,

OR=2.00). Ranked most important (n=159) were “doing everything possible” (30%), “my

doctor’s recommendation” (26%), “my quality of life” (20%), and “living longer” (15%). A

minority expected chemotherapy to cure their cancer (14%). Most had discussed expectations

of cure (70%), side effects (88%) and benefits (82%) of chemotherapy. Fewer had received

quantitative prognostic information (49%) than desired this information (67%).

224

Conclusion

Older adults exhibited a range of preferences for involvement in decision-making about

palliative chemotherapy. Oncologists should seek patients’ decision-making preferences,

priorities, and information needs when discussing palliative chemotherapy.

225

9.3 Introduction

With an ageing population, oncologists are seeing increasing numbers of older adults with

incurable cancer who require discussions and decisions about anti-cancer treatment. Such

decisions ideally reflect a collaborative process between patient and oncologist with

consideration of potential harms and benefits, provision of adequate information about the

cancer and expected outcomes, acknowledgement of patient goals and priorities, and

incorporation of patient preferences. This is the framework provided by the model of shared

decision-making (SDM). (342-344) SDM is increasingly advocated in cancer care, (344, 345)

especially where there may be more than one acceptable option or approach to treatment, or

where treatment decisions are sensitive to the preferences and priorities of patients. This is

often the case with older adults considering palliative chemotherapy for incurable cancer.

Whilst SDM promotes a collaborative approach to decision-making, (2) preferences for

involvement in decision-making of patients with cancer range from active through to passive

roles. (9) The preferred decision-making roles of older adults however is unclear. Studies

including patients of all ages in the setting of early breast cancer (346-348) and mixed advanced

cancers (349, 350) have found that increasing age was associated with a preference for a passive

role. Two studies performed specifically in older adults with advanced cancers found

contrasting results. One, a study of older adults with advanced colorectal cancer, showed about

half of patients (52%) preferred a passive role, (99) whereas a recent study reporting qualitative

interviews of older adults with advanced cancer of mixed types found most preferred to play

an active role. (100) There is evidence to support greater decision satisfaction where patients

are able to play the role in decision making that they prefer, (17, 18) but none on whether or

not older adults actually played the role they prefer in decisions about chemotherapy.

226

Decisions about palliative chemotherapy are influenced by a patient’s priorities, their

understanding of their cancer, the proposed treatment, and likely prognosis. Whilst oncologists

frequently prioritise performance status as the most important factor when making

recommendations about palliative chemotherapy for older adults, (120, 311) older adults have

different priorities such as maintenance of function and quality of life. (101, 318) Patients with

advanced cancer may not understand the goals of palliative chemotherapy nor their prognosis,

(107) and may not always recall what their oncologist has told them. (106) Being adequately

informed about prognosis and the goals of treatment, however, allows patients to come to a

decision that is consistent with their priorities. Understanding the perspectives of older adults

who have recently made such decisions is an important step in better informing and improving

treatment decision-making in this population.

The aim of this study was to determine older patients’ preferred role in decision-making about

palliative chemotherapy and to compare it to the role they perceived they had played.

Secondary objectives were to determine (i) factors associated with preference for an active role

in decision-making and for achieving preferred role; (ii) factors considered important in

decision-making about palliative chemotherapy; (iii) patients’ understanding of their cancer,

its treatment, and survival time; and (iv) information needs.

9.4 Methods

9.4.1 Study design and setting

A cross-sectional survey study was conducted across two metropolitan and four regional cancer

centres in New South Wales, Australia. Ethics approval for the study was provided by the

Sydney Local Health District Human Research Ethics Committee of Concord Repatriation

General Hospital (HREC/15/CRGH/208).

227

9.4.2 Participants

Eligibility criteria included patients aged ≥65 years with a new diagnosis of an advanced solid

organ cancer, who had seen a medical oncologist and discussed palliative chemotherapy as a

treatment option. A decision regarding chemotherapy had to have been made in the last 12

weeks. Adequate English language proficiency was also required.

9.4.3 Participant questionnaire

Distribution and design

The study questionnaire was piloted on a focus group of 6 older adults with advanced cancer.

Feedback on content and readability was obtained and incorporated into the final version.

(Appendix E)

Potential participants were identified through their treating oncologist, as well as review of

outpatient clinic referrals by study investigators. Questionnaires were distributed in person by

either treating oncologists, or a research officer, nurse, or trainee oncologist (site specific) who

was trained in the study inclusion criteria, and who may or may not have been involved in the

patient’s care. The number of questionnaires distributed at each site was recorded.

Questionnaires were provided in a sealed, take-home envelope and responses were anonymous.

Completion and return of the questionnaire constituted consent to participate. Questionnaires

could be returned by reply-paid envelope, or in person to the cancer centre.

Participating oncologists were familiar with the content of the questionnaire. Study posters

were displayed in clinical areas at each site, and monthly returned survey tallies sent to

investigators to encourage recruitment. The survey was distributed as an anonymous survey

228

with waiver of face-to-face consent to allow ease of distribution at multiple centres that were

geographically distant, with varied study-specific resources.

Patient characteristics

Sociodemographics, tumour, and treatment details were obtained by self-report. Patient-rated

global quality of life was measured by visual analogue scale, with anchor points defined as

“worst possible” and “best possible” quality of life. Patient-rated performance status (Pt-PS)

(191) required participants to select one of five statements that best described their function,

ranging from “normal with no limitations” to “pretty much bedridden, rarely out of bed.” The

Vulnerable Elders’ Survey (VES-13), (80) was used as a measure of vulnerability, with an “at

risk” cut-off score of ≥3. (80, 172)

Involvement in decision-making

Participants’ preferences for involvement in decision-making were assessed using the validated

Control Preferences Scale (CPS). (7, 348) The CPS asks participants to select one of five

statements, A to E, that best describes their preferred role in decision-making from an active

role (A = “I prefer to make the final selection about which treatment I will receive”) to a passive

role (E = “I prefer to leave all decisions regarding treatment to my doctor”). Participants’

preferred role was obtained by asking which of the responses on the CPS best described the

role they preferred to play in decision-making about chemotherapy. Participants’ perceived

role was obtained by asking which role best described the role they perceived they had played,

on a version of the CPS modified to past tense. (186) (Supplementary Table 1).

Decision satisfaction, factors influencing decisions, and information needs

229

Decision satisfaction was determined using the 6-item Satisfaction with Decision Scale

(SWDS). (190) The importance of 12 pre-specified factors in making decisions about palliative

chemotherapy were elicited by a 4-point Likert scale (from “not at all important” to “very

important”) with participants also asked to rank one of these factors as the most important

factor in decision-making. The 12 factors affecting decision-making about chemotherapy were

selected by the study investigators following literature review, and no changes to these factors

were suggested by the patient focus group during questionnaire design. The importance of the

opinion of ‘significant others’ was assessed on a 5-point scale (189) with responses ranging

from “I do not care at all about their opinion” to “I take their opinion very seriously”.

Participants were also asked if their doctor discussed (i) if their cancer was able to be cured,

(ii) was expected to shorten their life, (iii) the length of time they may live, (iv) the benefits

and risks of chemotherapy, and then if they had desired this information (yes/no). Participants

were asked “what is your understanding of how long you have to live?” with response as

quantitative estimate of survival time in months or years.

9.4.4 Statistical analysis

The population was described using frequencies and proportions (%) for categorical variables,

and means and medians for continuous variables. For Likert scales, the proportion of responses

in each answer category was determined, and mean ratings calculated by assigning ordinal

values to each answer category.

Responses on the CPS and modified CPS were categorised into three decision-making roles:

active (responses A and B), collaborative (response C), and passive (responses D and E).

Proportions of patients with responses within these categories were described for preferred and

perceived roles. The five possible responses on the CPS and modified CPS were then assigned

230

ordinal scores from 1 to 5 (1=most active, 2=active, 3=collaborative, 4=passive, 5=most

passive) to measure the difference between preferred and perceived role, with 0 indicating no

difference and ±4 indicating maximal difference. Differences between preferred and perceived

roles were evaluated using the Wilcoxon signed-rank test (omitting ties). Factors associated

with preferred role were determined using the Wilcoxon rank sum test (Mann-Whitney U test)

with ordinal scale, and preference for an active role (over collaborative or passive) summarised

by odds ratios. Associations with concordance between preferred and perceived roles were

assessed using Chi-squared tests of association and summarised by odds ratios. Agreement

between receipt and desire for items of information were assessed using Cohen’s kappa.

We aimed to include responses from 200 patients across all sites, with a study of N=200

providing for confidence intervals of estimated proportions of +/- <7%.

9.5 Results

323 surveys were distributed, with 179 surveys returned and included in the analysis, giving a

response rate of 55%. Surveys with one or more missing or incomplete responses were

included, with the median number of missing responses per item on the questionnaire being

seven, (range 1 to 20). The number of complete responses per survey item is detailed with

presentation of the results.

9.5.1 Patient-reported measures, demographic and clinical details

Patient demographics, clinical details, and patient-reported measures are summarised in Table

1. The median age was 74 years (range 65 to 92 years), and most (148, 83%) had decided to

have chemotherapy. Patient-reported global quality of life was good (median of 68 on a VAS

scale of 0 to 100; range 1-100). Most patients classified themselves as of good performance

231

status (Pt-PS 0 or 1 in 126, 72%), though half (92, 52%) were vulnerable by VES-13 (score of

≥3). Decision satisfaction was high (mean SWDS = 4.52) with little variance (IQR 4 to 5).

9.5.2 Preferred and perceived roles in decision-making

Preferred decision-making roles (n=173) were active in 39%, collaborative in 27%, and passive

in 35%. Perceived decision-making roles (n=172) were active in 42%, collaborative in 22%,

and passive in 36% (Figure 1). Preferred and perceived decision-making roles were concordant

for 63% (n=109) of participants (Table 2). For those where preferred and perceived roles were

discordant (n=63), most (n=43) had a discrepancy score between roles of +/- 1, representing a

single move in either direction on the CPS. Near equal proportions perceived having played a

more active role than preferred (19%), as perceived having played a more passive role than

was preferred (18%) (Supplementary Table 2).

The assessment of factors associated with preferred role in decision-making is summarised in

Supplementary Table 3. Preferring a more active role was associated with being

single/widowed [WRS p=0.004, OR 1.49 (for active role over passive/collaborative)] and

declining chemotherapy (WRS p=0.02, OR 2). The assessment of associations of achieving the

desired role (concordance between preferred and perceived roles) is summarised in

Supplementary Table 4. University educated were less likely to report achieving their desired

role [Chi-test p=0.002, OR 0.32 (for concordance)], and those who were married/in a de facto

relationship were more likely to report achieving their desired role (Chi-test p=0.02, OR 2.24).

9.5.3 Factors important in decision-making

The five factors rated as the most important in making a decision about palliative chemotherapy

were “my quality of life” (mean importance rating of 2.88 on a scale of 0 to 3), “my doctor’s

232

recommendation” (2.84), “the benefits of chemotherapy” (2.72), and “doing everything

possible to fight the cancer” (2.71), with 80% or more of respondents rating these as “very

important”. The single most important factor (n=159) in decreasing frequency were “doing

everything possible to fight the cancer” (30%), “my doctor’s recommendation” (26%), “my

quality of life” (20%), and “living longer” (15%). Only one-third of respondents considered

“how old I am” to be very important to their decision-making about chemotherapy. (Table 3)

Significant others whose opinions were rated as most important were the cancer specialist

(mean importance rating of 4.71 on a scale of 1 to 5), general practitioner (4.37), and partner

or spouse (4.08). (Figure 2)

9.5.4 Information needs and understanding

Participants’ expectations of palliative chemotherapy are outlined in Figure 3. A minority (24,

14%) expected palliative chemotherapy to cure their cancer. Agreement between information

received and desired is presented in Supplementary Figure 1. Of those (108 of 162, 67%) who

desired quantitative information on expected survival time, 69% (74 of 108) received it. When

asked about their understanding of expected survival time, over half (99 of 170, 58%) stated

they “did not know”, and one-fifth (34 of 170, 20%) preferred not to answer. One-third (33 of

99) of participants who “did not know” had discussed expected survival time with their

oncologist. A minority (20 of 167, 12%) of participants provided a quantitative estimate of

their expected survival time, the median estimate being 23 months (range 3 to 120 months).

Having discussed the concept of cure with their oncologist was not associated with the

expectation of cure (p=0.55).

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9.6 Discussion

When making decisions about palliative chemotherapy, an equal proportion of older adults

preferred an active or passive role, and least preferred a collaborative role. Most older adults

reported playing their preferred role in decision-making. ‘Doing everything possible’ was the

most frequently ranked number one consideration for decision-making about chemotherapy.

The opinion of the cancer specialist and GP were held in high regard. Some patients were

uncertain about the treatment goals of palliative chemotherapy, and many received less

information from their oncologist than they desired, particularly about prognosis.

In contrast to our results for preferred decision-making roles, prior studies performed in

patients of all ages with predominantly early breast or prostate cancers, showed most patients

preferred a collaborative role (9) and older age predicted a preference for a more passive role.

(346-350) In our study of older adults, older age (≥75 years versus <75 years) was not

associated with preferred role. Elkin et al (99) determined the decision-making preferences of

73 older adults (aged ≥70 years) with metastatic colorectal cancer where over half (38 of 73,

52%) preferred a passive role. Contrasting with this, in a decade-later study by Puts et al (100)

reporting on qualitative interviews in 32 older adults (aged ≥70 years) who had made a decision

about chemotherapy for varied advanced cancers, most (12 of 29, 41%) preferred an active

role. The small sample size of these studies and the fact that they were conducted years apart

(cohort effect) may explain the difference in role preferences between these studies.

The rates of concordance between preferred and perceived decision-making roles in our study

(63%) were within the ranges found in a systematic review by Tariman et al (16) of between

42 and 72%, and where there was discrepancy between preferred and perceived roles, this was

low (that is, the role that was played was close to the preferred role). Interestingly, those in our

234

study who were married were more likely to report achieving their desired decision-making

role, and were less likely to prefer playing an active role. This may reflect a tendency over a

lifetime of partnership to involve others in decision-making, although this has not always been

observed. (10, 186, 350-353) Having declined chemotherapy predicted a preference for a more

active role, perhaps indicating a group of older adults who have upheld their preferences about

treatment. To our knowledge, patient-rated performance status has not been evaluated as a

determinant of role preference previously, and was not associated with preferred role in our

study. The recognised lack of consistency in preferred roles and predictors of preferred

decision-making roles across studies, (9, 354) and that role preferences may change over time,

(10-12, 14) supports the need to elicit patients’ role preferences at regular clinical encounters.

Helping patients achieve their desired role in decision-making reduces anxiety about decisions,

(5) improves patient satisfaction and reduces decisional conflict. (17, 18)

Older adults in our study were motivated to have chemotherapy, wanted to live longer, maintain

quality of life, and be guided by their oncologist. In a systematic review by Puts et al (101) the

‘doctor’s recommendation’ was identified as the most consistent reason for older adults to

accept or decline treatment, a finding supported by a study from the same authors using semi-

structured interviews. (100) Soto et al (318) used 3 different tools to evaluate the priorities of

older adults with cancer (n=121) with regard to health outcomes. A large proportion of

respondents (44%) rated other outcomes (function, freedom from pain, or freedom from

symptoms) as more important than survival, and most agreed they would rather maintain their

function and/or thinking ability over living longer. Direct comparison with our study is difficult

due to methodological differences, though both support a need to incorporate patient goals and

priorities into treatment decision-making. Of note, and consistent with previous studies, (100)

patients in our study were less concerned with “how old I am” or their “other health problems”,

235

which are often concerns for their oncologist. (311) Of some interest were patients’ subjective

ratings of their performance status. Of those patients who rated their performance status as

good (0 or 1), about one-third (38 of 115) were ‘vulnerable’ on the VES-13 screening tool.

The role of the oncologist and GP in decisions about palliative chemotherapy for older adults

were affirmed in this study. The importance of the oncologist in guiding treatment acceptance

or rejection for older adults has been found previously. (100, 101, 355) The importance of the

GP likely reflects the trust older adults have with their GP as coordinators of their care over

many years. The knowledge GPs have of their patients enables them to tailor the provision of

information, frame discussion about relative benefits and harms, and help patients to make the

right decision for them. Older adults and oncologists should endeavour to involve GPs more in

key decisions about cancer treatments including chemotherapy. GPs have expressed a desire

for such involvement. (101)

Of concern was our finding that not all patients in our study understood that palliative

chemotherapy would not cure their cancer. This is consistent with other studies in older adults

with cancer (99, 101) and in studies of patients of all ages with advanced cancer. (107, 356-

358) In the largest of these studies (n=1193), only 31% of patients with metastatic lung cancer

and 19% of patients with metastatic colorectal cancer understood that palliative chemotherapy

was ‘not at all likely’ to lead to cure their cancer. (107) These results may reflect gaps in patient

understanding, patients maintaining hope, or patients’ preference not to be aware of prognosis,

or oncologists not having discussed the concept of cure. Given most participants in our study

(71%) reported their oncologist had discussed with them whether their cancer could be cured,

at least for some patients there were appreciable gaps in understanding or acceptance of the

message conveyed.

236

Oncologists provided less information than was desired by some patients and did not always

match patients’ information preferences. Whilst most patients with advanced cancer want some

indication of their prognosis, (21, 23) information needs are often varied with respect to the

desire for more detailed quantitative prognostic information, (23) similar to our study.

Interestingly, a greater proportion of patients in our study desired quantitative prognostic

information (67%) than in Elkin et al’s (99) decade-earlier study (44%), which may reflect

changing expectations for information over time. Interestingly, very few patients provided a

quantitative estimate of their survival time when asked. This could reflect patients not having

discussed this with their oncologist, not wishing to commit an estimate to paper, adopting a

fatalistic approach and hence answering “I do not know” (as in no one knows, it is up to fate),

or being unable to recall this information.

This study adds comprehensive knowledge on the decision-making preferences of older adults

with advanced cancer, including determining preferred and perceived decision-making roles

and exploring the relationship between preferences and geriatric measures of vulnerability.

The multi-centre design across metropolitan and regional centres provides a broad geographical

cross section of older adults with cancer in Australia. Limitations of our study include the use

of pre-specified response options to evaluate factors important in decision-making, limiting

understanding into other motivating factors. Sampling bias is also a limitation. Patients from

culturally and linguistically diverse backgrounds were likely underrepresented due to the

requirement for English proficiency. The majority (87%) of included patients had decided to

have chemotherapy, likely due to their continued contact with participating cancer centres that

facilitated recruitment. Their attitudes, preferences and priorities may differ to older adults who

had declined or were not suitable for chemotherapy. Treating oncologists were also involved

in identifying patients for the study, possibly leading to over-representation of those who had

237

a positive decision-making experience and an informative consultation, and under-

representation of those who were in very poor health or psychological distress, or with whom

oncologists perceived a difficult interaction. Oncologists were also familiar with the survey

content which may have influenced clinic consultations. Response bias is also considered.

Participants receiving surveys from treating oncologists also may report more favourably on

their experiences, and those who chose to complete and return surveys may have had different

experiences or be differently motivated from those who chose not to. Affirmation bias,

introduced through asking patients about a decision that they have already made, also should

be considered. Lack of data on non-responders is also a weakness of the study.

9.7 Conclusion

Older adults with incurable cancer hold varied preferences for involvement in decision-making

about palliative chemotherapy, and most played the role that they preferred. To facilitate shared

decision-making, oncologists should seek patients’ decision-making preferences, priorities,

and information needs when discussing and making a recommendation about palliative

chemotherapy.

238

Table 1. Respondent characteristics (n=179)

Characteristic Number % ‡

Age, median (range)

74y (65 to 92)

Cancer centre Metropolitan

Regional

95

84

53

47

Sex Male

Female

114

64

64

36

Cancer type Lung / pleura

Colorectal

Prostate

Upper gastrointestinal

Gynaecological

Genitourinary (non-prostate)

Breast

Other*

41

40

33

18

13

8

7

17

23

23

19

10

8

4

4

9

Marital status Married / de facto

Separated / widowed / single

123

55

69

31

Language spoken at home English

Non-English

168

10

94

6

Living arrangements Lives alone

Lives with others

34

144

19

81

Children Yes 163 92

Dependent children Yes 6 3

Educational status Schooling

Trade or technical qualification

University or college degree

98

56

23

56

32

13

Employment status Retired or on a pension

Employed

Unemployed

139

38

1

78

21

1

Close friend / relative died

from cancer

Yes 146 83

Friend / relative available for

care

None of the time

Some of the time

Most, or all of the time

10

42

125

6

24

70

Time to travel to cancer centre ≤1 hour>1 hour 146

32

82

18

Having chemotherapy Yes

No

Unsure

148

23

7

83

13

4

Significant other present at

most recent consultation**

On my own

Partner or spouse

Other family, friend, or carer

25

110

85

14

62

24

Patient-rated performance

status (191)

Normal, no limitations

Not normal self, up and about most of the day

Not feeling up to most things, but in bed or

chair less than half of day

Little activity, most of day in bed or chair

Pretty much bed-ridden, rarely out of bed

30

96

33

17

0

17

55

19

10

0

Global quality of life score

VAS

(mean) 67 (1-100)

Vulnerable Elders Survey

(VES-13) (80)

VES-13 of 0, 1 or 2

VES-13 of ≥3 (vulnerable)

85

92

48

52

Satisfaction with decision

scale† (190)

(mean) 4.52 (4-5)

*Other includes: head and neck (4); brain (1); unknown primary (6); anal (1); ‘unsure’ (1); ‘liver’ (3); and ‘lymph nodes’

(1); **Percentages do not total 100%, as more than one significant other could be selected;

†Range from 0 to 5, where 5 is greatest satisfaction.

‡Percentages not inclusive of missing responses (max. missing responses for any patient characteristic tabled was 3)

239

Table 2. Preferred versus perceived roles in decision making about palliative chemotherapy

Preferred role

Perceived role

A

Patient alone

B

Patient with

doctor input

C

Shared decision

D

Doctor with

patient input

E

Doctor alone

Total (%)

A Patient alone 7* (4) 6 (3) 1 (1) 1 (1) 0 (0) 15 (9)

B Patient with doctor input 5 (3) 37* (22) 8 (5) 7 (4) 0 (0) 57 (33)

C Shared decision 2 (1) 6 (3) 26* (15) 3 (2) 1 (1) 38 (22)

D Doctor with patient input 0 (0) 2 (1) 6 (3) 22* (13) 5 (3) 35 (20)

E Doctor alone 1 (1) 1 (1) 4 (2) 4 (2) 17* (10) 27 (16)

Total (%) 15 (9) 52 (30) 45 (26) 37 (22) 23 (13) 172 (100%) †

*Complete agreement between preferred and actual roles in 109 (63%) patients.

**No evidence of a preference for a more active role than was experienced (p=0.97, WRS)

†172 of the 179 returned surveys had completed responses for both preferred and perceived roles (7 missing or uninterpretable responses)

240

Table 3. Rating and ranking of factors considered important by older adults in making a decision about palliative chemotherapy

Factor N

Not at all

important

N (%)

A little

important

N (%)

Moderately

important

N (%)

Very

important

N (%)

Mean

importance

rating* (0-3)

N (%) ranking as

single most

important

factor**

My quality of life 168 0 (0) 0 (0) 20 (12) 148 (88) 2.88 31 (19%)

My doctor’s recommendation 173 3 (2) 3 (2) 12 (7) 155 (9) 2.84 42 (26%)

The benefits of chemotherapy 170 8 (5) 1 (1) 21 (12) 140 (82) 2.72 1 (1%)

Doing everything possible to fight the cancer 172 4 (2) 7 (4) 24 (14) 137 (80) 2.71 48 (30%)

Maintaining my independence 169 4 (2) 5 (3) 36 (21) 124 (73) 2.66 1 (1%)

Living longer 169 5 (3) 7 (4) 35 (21) 122 (72) 2.62 24 (15%)

Having someone to look after me during treatment 169 15 (9) 13 (8) 32 (19) 109 (64) 2.39 0 (0%)

The side effects of chemotherapy 170 7 (4) 21 (12) 54 (32) 88 (52) 2.31 3 (2%)

Being able to look after my partner / spouse or family 165 28 (17) 14 (8) 25 (15) 98 (60) 2.17 6 (4%)

My other health problems 167 30 (18) 29 (17) 45 (27) 63 (38) 1.84 1 (1%)

How old I am 164 42 (26) 21 (13) 45 (27) 56 (34) 1.70 1 (1%)

How far I would need to travel for treatment 168 49 (29) 24 (14) 38 (23) 57 (34) 1.61 1 (1%)

N = the number of responses received for that item

*Mean importance rating represents the arithmetic mean of importance scores assigned, with scores for each Likert category as: 0=not at all important; 1=a little important;

2=moderately important; 3=very important

**After rating each factor, participants were asked to select just one factor as the single most important factor when making a decision about treatment. 20 responses were

missing or unable to be included (selected more than one factor).

241

Figure 1. Distribution of preferred and perceived roles in decision-making. “Active

role” includes choice options A and B on the Control Preferences Scale, “collaborative role”

choice option C, and “passive role” choice options D and E. Complete responses for preferred

decision-making roles N=173 (6 missing), and for perceived decision-making roles N=172 (7

missing).

0

5

10

15

20

25

30

35

40

45

Passive Collaborative Active

Pro

po

rtio

n o

f p

atie

nts

(%

)

Decision-making role

Preferred role

Perceived role

242

Figure 2. Importance of the opinion of significant others. Respondents were asked to

rate the importance of the opinion of each significant other using a scale ranging from “I do

not care at all” to “I take their opinion very seriously”. The number of responses provided are

in parentheses. Scores assigned to each Likert category for calculation of means were: 1= do

not care at all; 2=care a little; 3=care somewhat; 4=take opinion seriously; 5=take opinion very

seriously.

2.35

2.59

2.86

3.79

4.08

4.37

4.71

1 2 3 4 5

Colleagues (164)

Friends (164)

Other family (162)

Children (168)

Partner (170)

Local doctor (168)

Cancer specialist (170)

Mean Importance Rating

243

Figure 3. Patient expectations of palliative chemotherapy. Participants were asked,

based on the information their oncologist gave them, in their situation if they expected

chemotherapy to do each of the above. The number of responses to each question are provided

in parentheses.

63

13

57

76

14

8

51

17

6

56

30

36

26

19

30

0% 50% 100%

Live longer? (171)

Make you feel worse? (166)

Make you feel better? (171)

Control the growth or spread of your cancer? (172)

Cure your cancer? (167)

Proportion of patients

Yes No Unsure

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Supplementary Table 1. The Control Preferences Scale (CPS) (7, 8)

Response Control Preferences Scale Modified CPS Decisional role

A I prefer to make the final selection about which treatment I will

receive

I made the final selection about which treatment I would receive Active

B I prefer to make the final selection of my treatment after

seriously considering my doctor’s opinion

I made the final selection of my treatment after seriously

considering my doctor’s opinion

Active

C I prefer that my doctor and I share responsibility for deciding

which treatment is best for me

My doctor and I shared responsibility for deciding which

treatment was best for me

Collaborative

D I prefer that my doctor make the final decision about which

treatment, but seriously consider my opinion

My doctor made the final decision about which treatment would

be used but seriously considered my opinion

Passive

E I prefer to leave all decisions regarding treatment to my doctor My doctor made all the decisions regarding my treatment Passive

Supplementary Table 2. Discrepancy between preferred and perceived decision-making roles

Difference in preferred and perceived roles at baseline* N Discrepancy score**

Played a more passive role than preferred

1 (1) + 4

1 (1) + 3

8 (5) + 2

21 (12) + 1

Achieved desired role 108 (63) 0

Played a more active role than preferred

22 (13) - 1

9 (5) - 2

2 (1) - 3

0 (0) - 4

*No evidence of preference for a more active role than was experienced (p=0.97, WRS)

**Discrepancy score calculated by as follows: decision-making roles by the Control Preferences Scale were assigned ordinal scores, with 1 = most active, 2 = active, 3 =

collaborative, 4 = passive, 5 = most passive. The discrepancy score was the ordinal score of the preferred role subtracted from the perceived role, such that a discrepancy

score of 0 represents concordance between preferred and perceived roles.

245

Supplementary Table 3. Associations with preferred role in decision-making about palliative chemotherapy

Characteristic n

More active role

(role A or B)

More passive role

(roles C, D, or E) p-value* OR**

Age <75 years 93 34 (37) 59 (63) 0.5 0.82

>/= 75 years 80 33 (41) 47 (59)

Sex Male 113 45 (40) 68 (60) 0.8 1.14

Female 60 22 (37) 38 (63)

Education University degree 22 9 (41) 13 (59) 0.6 1.11

Schooling or trade 151 58 (38) 93 (62)

Employed Employed at diagnosis 38 8 (21) 30 (79) 0.1 0.34

Unemployed or retired 135 59 (44) 76 (56)

Marital status Married/de facto 120 43 (36) 77 (64) 0.004 0.67

Single/divorced/widowed 53 24 (45) 29 (55)

Friend or relative with cancer Yes 142 52 (37) 90 (63) 0.09 0.62

No 29 14 (48) 15 (52)

Available care Most or all of the time 122 43 (35) 79 (65) 0.2 0.59

None or some of the time 50 24 (48) 26 (52)

Travel time More than an hour 31 12 (39) 19 (61) 0.9 1.00

Less than an hour 142 55 (39) 87 (61)

Centre type Regional or rural 81 34 (42) 47 (58) 0.3 1.29

Metropolitan 92 33 (36) 59 (64)

Treatment decision No chemotherapy 22 12 (55) 10 (45) 0.02 2.00

Chemotherapy 144 54 (38) 90 (62)

Patient rated performance >/=2 48 21 (44) 27 (56) 0.5 1.37

0 or 1 124 45 (36) 79 (64)

VES-13 score Vulnerable 87 36 (41) 51 (59) 0.3 1.23

Not vulnerable 85 31 (36) 54 (64) *p-value derived from Wilcoxon Rank Sum test; **odds ratio represents odds of preferring a more active role (role A or B) over a collaborative or passive role (roles

C, D, or E)

246

Supplementary Table 4. Associations with concordance between preferred and perceived role in decision-making

Characteristic n played desired role

did not play

desired role p-value* OR**

Age <75 years 93 60 (65) 33 (35) 0.74 1.11

>/= 75 years 79 49 (62) 30 (38)

Sex Male 113 73 (65) 40 (35) 0.64 1.17

Female 59 36 (61) 23 (39)

Education University degree 38 16 (42) 22 (58) 0.002 0.32

Schooling or trade 134 93 (69) 41 (31)

Employed Employed at diagnosis 38 28 (74) 10 (26) 0.13 1.83

Unemployed or retired 134 81 (60) 53 (40)

Marital status Married/de facto 120 83 (69) 37 (31) 0.02 2.24

Single/divorced/widowed 52 26 (50) 26 (50)

Friend or relative with cancer Yes 141 90 (64) 51 (36) 0.86 1.08

No 29 18 (62) 11 (38)

Available care Most or all of the time 121 75 (62) 46 (38) 0.62 0.84

Some or none of the time 50 33 (66) 17 (34)

Travel time More than an hour 31 15 (48) 16 (52) 0.06 0.47

Less than an hour 141 94 (67) 47 (33)

Centre type Regional or rural 81 48 (59) 33 (41) 0.29 0.72

Metropolitan 91 61 (67) 30 (33)

Treatment decision No chemotherapy 21 11 (52) 10 (48) 0.2 0.55

Chemotherapy 144 96 (67) 48 (33)

Patient rated performance >/= 2 48 30 (63) 18 (37) 0.91 0.96

0 or 1 123 78 (63) 45 (37)

VES-13 score Vulnerable 86 55 (64) 31 (36) 0.83 1.07

Not vulnerable 85 53 (62) 32 (38) Role preference Active

Collaborative

Passive

67

46

60

44 (66)

26 (57)

29 (48)

23 (34)

20 (43)

21 (52)

0.57 Ref

0.68

0.97

*p-value derived from Chi-test of association; **OR represents odds of having matching preferred and perceived roles in decision-making versus not

247

A. B.

C. D. E.

Supplementary Figure 1. Agreement charts showing receipt and desire for five items of

information. The agreement chart provides a visual representation for comparing the concordance in

paired categorical data, in this case the receipt of information (yes/no) versus the desire for that

information (yes/no). For each figure, the x- “axis” is divided into those who received the item of

information and those who did not (counts in parentheses); the y- “axis” is divided into those who

desired the item of information and those who did not (counts in parentheses). The largest square

represents the number of respondants; the darkest grey squares represent patients where there was

complete agreement between receipt and desire for information; the light grey areas represent where

there was discordance between information received and desired. Figures are to scale.

248

10. Discussion

10.1 Overview

This chapter is a discussion of the work in this thesis as a whole, and complements the more

detailed discussion of the component studies presented with each published chapter. Covered

within this chapter is: (i) a summary of the principal findings of the thesis, (ii) the significance

of the work with respect to its clinical and research implications, and (iii) strengths and

limitations. This is followed by concluding remarks.

249

10.2 Principal findings

The principal findings, as they relate to the experimental chapters, are outlined in Figure 1.

10.2.1 There is a paucity of clinical trial evidence for the management of older

adults with colon cancer with most clinical trials including predominantly younger, fitter

patients.

The narrative review presented in Chapter 4 described the evidence for adjuvant and palliative

chemotherapy for older adults with colon cancer, and discussed key aspects of treatment

decision-making in these settings. Older adults were under-represented in clinical trials that

form the basis for chemotherapy recommendations in early and advanced colon cancer, and

much of the evidence for the relative benefits and harms of chemotherapy in older adults is

derived from subgroup analyses and large population studies. A need for further prospective

treatment trials inclusive of older adults was identified, as was the need for treatment decisions

to consider goals of care and patients’ treatment preferences.

10.2.2 Factors important for oncologists when making a recommendation about

chemotherapy for older adults with cancer were different in curative and palliative

settings. The likelihood of recommending chemotherapy varied by patient age and

anticipated risk of chemotherapy toxicity.

In a survey of Australian oncologists reported in Chapter 5, patient performance status was the

most important factor when recommending chemotherapy for an older adult with cancer. Other

factors considered important depended on treatment intent: in the adjuvant setting, estimated

survival benefit of treatment and life expectancy in the absence of cancer were the next most

important factors, whereas in the palliative setting, patient preference and quality of life were

250

the next most important. Whilst oncologists reported making an assessment of most geriatric

health domains prior to recommending chemotherapy, formal geriatric assessment tools or

geriatric assessment were rarely used. In hypothetical patient scenarios, oncologists were less

likely to prescribe both adjuvant and palliative chemotherapy as patient age and anticipated

rates of severe toxicity increased. Of note, approximately half of oncologists agreed they were

able to anticipate which of their older patients would experience severe chemotherapy toxicity.

10.2.3 Neither the CARG Toxicity Score nor oncologists’ estimates of severe

chemotherapy toxicity predicted severe chemotherapy toxicity in a local population of

older adults.

In the prospective study reported in Chapter 6, neither the CARG Toxicity Score nor

oncologists’ estimates of the likelihood of severe chemotherapy toxicity predicted severe

chemotherapy toxicity in a population of older adults starting chemotherapy for solid cancers

of varied type and stage. Both measures had a discriminative value for toxicity no better than

the play of chance.

10.2.4 Most Australian oncologists do not routinely use geriatric assessments for

older adults with cancer. When used, a geriatric assessment is more likely to trigger

supportive care interventions than to alter chemotherapy prescribing.

In the survey of Australian oncologists reported in Chapter 5, few oncologists reported routine

use of a geriatric assessment. When results of a geriatric assessment were presented back to

treating oncologists in the prospective study reported in Chapter 7, oncologists reported that

knowledge of the results of a geriatric assessment would not have altered their chemotherapy

prescribing. Despite this, oncologists found the geriatric assessment was useful for most

251

patients, and triggered supportive care interventions and referrals for approximately one-in-

four patients.

10.2.5 Components of a geriatric assessment and assessments of frailty may assist

to inform outcomes in older adults commencing chemotherapy.

In the prospective observational study of older adults commencing chemotherapy presented in

Chapter 6, impairments in functional status (by the Timed Up and Go), social activity due to

health, and cognition (by the Orientation Memory Concentration test) were independent

predictors of severe chemotherapy-related toxicity. In the subset of those with incurable cancer

presented in Chapter 8, a subjective measure of frailty (the CSHA Clinical Frailty Scale) and

cancer type independently predicted observed survival time.

10.2.6 Most older adults with advanced cancer want information about their

prognosis. Quantitative information about prognosis can be provided in the format of

individualised best-case, typical, and worst-case scenarios for expected survival time. For

older adults with advanced cancer, these scenarios can be accurately derived using simple

multiples of an oncologist’s estimate of the expected survival time.

In the cross-sectional survey of older adults with advanced cancer reported in Chapter 9, most

(67%) desired quantitative information about their expected survival time. Chapter 8 reported

the nature and accuracy of oncologists’ estimates of expected survival time for 102 older adults

receiving palliative chemotherapy for advanced cancer. Although oncologists’ point estimates

of survival time were imprecise, estimates were unbiased (did not show a systemic tendency

towards optimism or pessimism), and simple multiples of these estimates accurately described

best-case, typical, and worst-case scenarios for survival time.

252

10.2.7 Older adults with advanced cancer have varying preferences for

involvement in decision-making about chemotherapy. Their decisions are motivated by a

desire to control their cancer, live longer, and maintain quality of life.

In the survey study presented in Chapter 9, older adults with advanced cancer had preferences

for involvement in decision-making that spanned active (39%), collaborative (27%), and

passive roles (35%), and more than half perceived having played their preferred role when

making a decision with their oncologist about treatment with palliative chemotherapy. Most

frequently ranked as the most important consideration when making a decision about

chemotherapy were “doing everything possible”, “my doctor’s recommendation”, “my quality

of life”, and “living longer”. Contrasting considerations of surveyed oncologists were presented

in Chapter 5, with performance status, quality of life, and patient preference being the highest

ranked considerations in the setting of making a recommendation about palliative

chemotherapy.

253

254

10.3 Significance of findings

This thesis has significant clinical and research implications that are outlined below. As the

field of Geriatric Oncology grows, and guidelines begin to encourage or even stipulate the use

of additional often resource intensive clinical tools and assessments when making decisions

about treatment for older adults with cancer, it is important to evaluate the value of these

additional components of patient assessment to the decision-making process, and to patient

outcomes. The work in this thesis provides novel data on how oncologists and their older

patients make and prefer to make decisions about treatment with chemotherapy, but also begins

to challenge what additional measures should be implemented to improve this process, and for

what value.

10.3.1 Clinical implications

This thesis has important clinical implications. Australian oncologists can improve their

assessment of older adults with cancer. Their low utilisation of formal geriatric assessment or

its component tools provides rationale for education and support of oncologists in the

assessment of older adults, but also for the field of geriatric oncology to continue to research

the role and value of the geriatric assessment and associated clinical tools in practice, and how

best to implement them into routine care.

This work does not support the widespread implementation and use of the CARG Toxicity

Score in clinical practice in Australia. An extension of this is the implication that such scores

may not hold value in other clinical practices outside of those in which they were developed.

This is significant given the use of the tool is presently advocated in international guidelines

for the assessment and care of older adults with cancer. Clinicians should be aware of the

validity of clinical prediction tools in local populations, and evaluate such tools prior to their

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implementation. They should also be aware of the limitations of these tools in guiding decisions

about chemotherapy for individual patients, and consider the evidence to date (or lack thereof)

that decisions guided by use of such tools improves patient care.

Older adults with cancer vary in the degree to which they wish to be involved in decisions

about chemotherapy, and place importance on a variety of factors during the decision-making

process, some that may directly compete such as quality of life and survival. Oncologists are

influenced by various factors beyond patient age when making a recommendation about

chemotherapy for their older patients. These findings support the approach that oncologists ask

older adults how much they wish to be involved in the treatment decision at hand, and about

their preferences for or against chemotherapy. In doing so, the potential benefits and harms of

the proposed chemotherapy can be considered in a context relevant to the individual patient,

and the final treatment decision will more likely be consistent with the patient’s values,

priorities, and circumstances. This is not dissimilar to the advocated approach to treatment

decision-making for adults of all ages.

Oncologists, if not already, should ask older adults with cancer about their information needs,

paying attention to the desire for qualitative or quantitative prognostic information. Oncologists

should be aware of the imprecision of a single point estimate of expected survival time.

Prognostic information, where desired, should be provided in the format of individualised

expected best-case, typical, and worst-case scenarios for survival, as is the supported approach

for adults of any age with advanced cancer.

Finally, oncologists should be aware that there is some value in geriatric assessment for older

adults with cancer beyond treatment recommendations. For a proportion of older patients,

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geriatric assessment can uncover deficits unidentified by initial routine clinical assessment and

prompt supportive care intervention. Additionally, it provides clinicians with a language with

which to communicate about their older patients objectively. The content of this assessment

should cover all health domains, but its optimal format, population it should be applied to, and

subsequent referral pathways are unclear and are likely to be shaped by available resources

within a clinical practice. Additionally, the magnitude of the value of any new service over and

above usual care will also depend on the existing service and resources available. Required

resources may be minimised if self-complete survey-based geriatric assessment with guided

intervention or referral that falls within a service’s existing scope of practice is used. The

clinical implications of this work therefore extend beyond that of individual oncologists to

clinical services and models of care.

The ideal model of care for older adults with cancer locally cannot be adequately informed by

the work in this thesis, which primarily focusses on decision-making about chemotherapy.

Acknowledging limitations of the current evidence for the geriatric assessment in oncology, a

reasonable approach to the care of the older adult with cancer would be to utilise a form of

geriatric assessment with guided intervention, with or without initial geriatric screening, with

requisite evaluation of the chosen model’s value to clinical care and patient outcomes, balanced

against its cost. This may need to occur within a clinical trial. Successful implementation would

require collaboration and engagement with geriatricians, particularly regarding interpretation

of the initial geriatric assessment and subsequent interventions.

10.3.2 Research implications

Research implications of the work in this thesis include exploration of the barriers to the

implementation of geriatric assessments in the evaluation of older adults with cancer. Work to

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establish the optimal format of the geriatric assessment best suited to Australian oncologists

and their patients is needed. Further research to better establish the role of the geriatric

assessment in guiding decisions about chemotherapy is also required. Randomised clinical

trials can determine whether a change in treatment allocation occurs based on the results of

geriatric assessment, and whether cancer treatment or supportive care interventions guided by

geriatric assessment result in meaningful improvements in patient outcomes and care. Given

geriatric assessment is recommended in international guidelines as part of the care of all older

adults with cancer, this work should be a priority.

Improved methods to predict the likelihood of severe chemotherapy toxicity (and other

outcomes) in older adults with cancer are needed. Attempts to date to identify consistent

predictors of outcome from geriatric assessments that have varied in quality and content and

have been applied to cohorts of older adults who are heterogeneous with respect to cancer type,

stage, and treatment, have been relatively unsuccessful to date. A continuation of this approach

would not be ideal, and there would be value in the field reaching a consensus on the minimum

measures to be applied in geriatric assessments used within trials. Alternative approaches to

improve prediction of outcomes in older adults include the local validation of alternative

previously developed risk prediction tools and the development of local prediction tools in

homogeneous populations defined by tumour type or treatment regimen. Research is also

required to determine the value of risk-prediction tools over and above clinical judgement, and

their role in modifying chemotherapy recommendations. Prospective evaluation of the value of

the predictors of chemotherapy toxicity and observed survival time identified in this work is

warranted, with the comparison of the single item CSHA Clinical Frailty Scale with ECOG

performance status of particular interest.

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Further evaluation of the treatment preferences of older adults with cancer would complement

this work. A possible approach is determining quantitative estimates of preferences such as the

survival benefits required to justify the side effects and inconveniences of chemotherapy.

Consideration should be given to the development of decision aids specific to older adults with

cancer to facilitate shared, informed decision-making, by eliciting patients’ priorities and

presenting individualised information regarding absolute benefits and harms of treatment. An

extension of this, and particularly relevant in geriatric oncology, is how best to support

decision-making for older adults with cognitive impairment, which may involve surrogate

decision-makers.

The work on estimating and communicating prognosis to older adults could be extended. For

example, the predictive value of prognostic tools in older adults with resected solid organ

cancers and their influence on decisions about adjuvant chemotherapy is an area for further

investigation. Oncologists’ estimates of expected survival time could equally be evaluated in

this setting. How understanding of expected survival time impacts an older adults’ preferences

for both adjuvant and palliative systemic treatment is also an area of interest.

Moving forward in the field of geriatric oncology

One of the biggest contributors to the complexity of decision-making about chemotherapy for

older adults with cancer is underrepresentation in clinical treatment trials. Key to moving

forward in this field is a concerted effort to design treatment trials that are inclusive of ‘real-

world’ patients by expanding trial inclusion criteria and addressing barriers to enrolment of

older adults. Parallel to this should be the incorporation of baseline assessment measures (such

as those of a geriatric assessment or frailty score) that are relevant to older adults and that will

help to inform those most likely to benefit from treatment. Similarly, increased use of outcome

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measures relevant to older age, such as loss of physical function or independence, as well as

other patient-reported outcome measures would help to more meaningfully inform decisions

about cancer treatment for older adults. The field would benefit from consensus definition on

key terms and concepts within geriatric oncology. For example, whilst chronological age is

convenient, conceptualising older adults as ‘fit’, ‘vulnerable’, or ‘frail’ allows for tailored

clinical trial design and rationalised use of clinical resources. Identifying and defining universal

working criteria for ‘fit’, ‘vulnerable’, and ‘frail’ is needed.

10.4 Strengths

This section discusses the strengths of the thesis as a whole. The strengths of the individual

studies were presented in Chapters 5, 6, 7, 8, and 9. The main strength of this thesis arises from

its investigation of contemporary and novel aspects of decision-making about chemotherapy

for older adults with cancer, and so adding original data to the body of international geriatric

oncology research but with direct clinical relevance to the Australian context for the benefit of

older adults with cancer and their oncologists.

Key novel aspects of the thesis include the studies evaluating the CARG Toxicity Score that

asked questions pertinent to its implementation: does it maintain value in our population, does

it perform better than usual clinical judgement, and is it likely to alter decisions about

treatment? The prospective observational study in Chapter 6 is the first reported study testing

the CARG Toxicity Score in a heterogeneous cancer population outside of the United States,

contributing to evidence relevant to external validation of the tool. It is also the largest study

comparing oncologists’ estimates of chemotherapy-related toxicity with a risk prediction tool.

Novel local data on the value and use of the GA and CARG Toxicity Score to oncologists for

individual patients was able to be provided using a consecutively recruited companion study

260

reported in Chapter 7. This study was the first to seek to evaluate oncologist reported impact

of the CARG Toxicity Score on chemotherapy recommendations. Other novel work in this

thesis includes the applicability of using oncologists’ estimates of expected survival time to

formulate individualised scenarios for survival in a real-world population of older adults with

advanced cancer and determining how older adults with cancer and oncologists make decisions

about chemotherapy.

Another strength of this thesis is the design of the individual studies to investigate similar

themes of enquiry from different perspectives. For example, older adults with cancer and

oncologists were asked in separate survey studies about the factors that influenced their

decisions about chemotherapy. Pre-specified response options were similar between the

studies, to allow for some comparison to be drawn regarding priorities in decision-making, at

least in the palliative setting. Another example is the theme of chemotherapy toxicity as a driver

of oncologists’ decisions about chemotherapy addressed by three studies. The impact of the

risk of severe chemotherapy toxicity on oncologists’ likelihood to recommend chemotherapy

to older adults was evaluated in the survey study of Chapter 5, providing evidence for the

potential value of a chemotherapy risk prediction tool, such as the CARG Toxicity Score

evaluated in Chapter 6, to the decision to recommend chemotherapy, with the potential for the

CARG Toxicity Score to impact oncologists’ recommendations about chemotherapy for their

actual patients evaluated in Chapter 7.

An additional strength of the research presented in this thesis was the increased awareness of

the involved study centres with the emerging sub-specialty of geriatric oncology and with the

geriatric assessment. The geriatric assessment proved feasible for use in everyday practice,

familiarising participating centres and oncologists with validated tools for the assessment of

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each health domain. Valuable collaboration and learning across centres occurred, providing

interest and a platform for further geriatric oncology research and ongoing partnership. A

substantial proportion of most oncologists’ practices involves the care of older adults, and in

the presentation and distribution of the results of the work in this thesis, it became apparent

that dialogue within the specialty is changing over time to value clinical research relevant to

the older population.

10.5 Limitations

Limitations of the thesis as a whole are discussed in this section. Limitations of each individual

study were presented in Chapters 5, 6, 7, 8, and 9.

The results of this thesis represent the populations studied, and so may not be generalisable to

all older adults with cancer in all settings. Relevant to this is the definition of the ‘older adult’.

We defined ‘older adult’ as aged 65 years and older so that the inclusion criteria of our

prospective study allowed for direct comparison with the CARG Toxicity Score development

study. Our survey study of Australian oncologists showed that most respondents thought older

adults were aged 75 years and above. Our studied population may therefore not best represent

the group of patients for whom oncologists perceive difficulties with decision-making about

treatment.

Older adults represented by this work were limited to those older adults with cancer who had

been referred to a medical oncologist, and who were planned to receive chemotherapy or had

treatment with chemotherapy discussed by their oncologist as a treatment option. Conclusions

cannot be drawn about aspects of decision-making about chemotherapy that occur outside of

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this setting, for example, older adults with cancer who were not referred to an oncologist or

who saw an oncologist but were not recommended chemotherapy.

The studies of this thesis were not inclusive of culturally and linguistically diverse older adults

due to the requirement of English language proficiency for participation in the research studies.

Older adults from diverse backgrounds may differ in their approach to decision-making about

treatment, desire for information, and preferred and perceived involvement in decisions about

care. This weakness is not limited to this thesis, but rather is a common theme of all studies in

geriatric oncology (and beyond). Validated geriatric assessment tools in languages other than

English are required for widespread implementation of the geriatric assessment in clinical

practice.

Methodologic limitations of the thesis include a lack of complementary qualitative data that

would have allowed more in-depth analysis of identified themes, motivations and reasons

behind decisions about chemotherapy. The use of pre-specified response options in the survey

studies potentially provided restricted views of respondents. Collection of data on patient

reported outcomes during chemotherapy would have provided valuable information to older

adults considering the treatment. Drivers of decision-making for oncologists and patients were

evaluated using hypothetical decisions or decisions about treatment that had already been

made, reducing the applicability of results to real-world decisions and introducing bias. The

included prospective studies used a geriatric assessment implemented on a single occasion

prior to planned chemotherapy. The ongoing value and information provided by such an

assessment during the course of an older adults’ cancer journey cannot be inferred from these

studies. Additionally, the simplification of a treatment decision about chemotherapy was

simplified by these studies to a single point in time, when in clinical practice decisions about

263

suitability for chemotherapy can be made over a number of visits. Once receiving

chemotherapy, an assessment of ongoing suitability for treatment is made at each clinical

encounter, and has not been considered within this thesis.

Older adults included in the prospective observational work in this thesis were heterogeneous

with respect to cancer type, stage of disease, treatment line, and chemotherapy regimen, again

chosen to reflect the inclusion criteria of the CARG Toxicity Score development study.

Limitations of previous research aimed to identify consistent predictors of chemotherapy

toxicity and survival outcomes in older adults with cancer are the heterogeneity of study

populations and geriatric assessment tools. The work in this thesis did thus not improve on

existing literature in this regard, other than ensuring commonly used geriatric assessment

instruments were used for the assessment of each health domain.

There are inherent limitations in identifying predictors of outcome from components of a

geriatric assessment in selected populations of older adults who are suitable for chemotherapy.

Only a minority of older adults in such populations will score outside of normal range on any

one tool used. This relative homogeneity in performance on measures used as candidate

variables means that either larger populations are required to identify associations with

outcome or more discriminatory measures are required. Whilst independent predictors of

severe chemotherapy toxicity and observed survival time were identified in this work, absolute

consistency with prior studies was lacking, precision of the estimates was limited by sample

size, and strength of identified associations were generally limited (other than for more global

measures such as the CSHA Clinical Frailty Scale). Additionally, population heterogeneity

limits application of the results to individual patients highlighting a need for studies in older

adults homogeneous for tumour type and treatment regimen.

264

This thesis evaluated aspects of decision-making in a limited number of clinical practices with

a modest number of oncologists. In particular, the perceived role and frequency of use of

additional patient assessments and clinical tools in evaluating older adults prior to making a

decision about cancer treatment is very much influenced by available local resources,

department structure, and wider referral pathways. These are often practice or service

dependent, rather than oncologist dependent, and so broader representation of oncologists

practising in different settings with varied access to geriatric specific resources should be

sought in future studies.

10.6 Concluding remarks

Decision-making about chemotherapy for older adults with cancer is complex, with multiple

factors affecting the decision outcome. Medical oncologists use traditional measures such as

performance status to determine suitability for chemotherapy and rely largely on clinical

intuition and experience rather than formal geriatric assessments. Geriatric assessments can

provide oncologists with useful information about the health of their older patients, though do

not have an established role in decision-making about chemotherapy treatment. Clinical risk-

prediction tools may provide objective value but must be appropriately validated to the local

setting prior to implementation. Better representation of older adults in clinical trials may

remove some of the complexity for oncologists in making a chemotherapy recommendation.

Finally, older adults with cancer have varied decision-making preferences, priorities, and

information needs that should be elicited by treating oncologists during discussions about

chemotherapy. This would allow for patient engagement in the decision-making process,

enable a shared understanding of patient priorities and goals of care, and is key to tailoring

discussions about treatment to each individual patient.

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Appendices

310

Appendix A. Oncologists’ survey (Chapter 5)

MEDICAL ONCOLOGIST SURVEY

Chemotherapy Prescribing for Older Adults with Cancer

Thank you for agreeing to participate in this survey on chemotherapy prescribing for older adults with

cancer.

The aim of this survey is to understand how Australian oncologists make decisions regarding the use

of chemotherapy in older adults with cancer.

All your responses will be anonymous and are non-identifiable.

The survey should take less than 15 minutes.

311

SECTION 1 Demographics of respondents

1. What is your current position?

o Consultant

o Trainee

2. How old are you?

o 20-39 years

o 40-59 years

o 60 + years

3. Are you?

o Male

o Female

4. How many years have you worked in Medical Oncology (including advanced training)?

o 1 to 5 years

o 6 to 10 years

o 10 to 20 years

o >20 years

5. Is most of your clinical practice in?

o Public practice

o Private practice

o An equal mix of public and private practice

o N/A (I do not currently have a clinical practice)

6. How many NEW patients, on average, do you see per year?

____________ new patients per year

7. What proportion of your practice is aged 65 years or older?

o < 10%

o 10 to 25%

o 26 to 50%

o 51 to 75%

o > 75%

8. What proportion of your practice is aged 80 years or older?

o < 10%

o 10 to 25%

o 26 to 50%

o 51 to 75%

o > 75%

9. What cancer type(s) do you predominantly treat?

(Select all that apply)

o Breast

o Lung / thoracic

o Colorectal

o Genitourinary

o Upper gastrointestinal

312

o Neurological

o Gynaecological

o Head and neck

o Melanoma

o Sarcoma

o Other _____________________

10. How would you define an “older patient with cancer”?

o 65 and older

o 70 and older

o 75 and older

o 80 and older

313

SECTION 2 Factors important in chemotherapy decision-making

1. When making a recommendation regarding palliative chemotherapy for an older adult with

advanced cancer, many patient factors are considered.

a. How do you rate the importance of the following factors when making a decision about

treating an older adult with palliative chemotherapy for an advanced cancer?

Not at all

important

Very

important

Performance status o 0 o 1 o 2 o 3 o 4 o 5

Cancer type o 0 o 1 o 2 o 3 o 4 o 5

Burden of disease o 0 o 1 o 2 o 3 o 4 o 5

Comorbidities o 0 o 1 o 2 o 3 o 4 o 5

Age o 0 o 1 o 2 o 3 o 4 o 5

Patient preference o 0 o 1 o 2 o 3 o 4 o 5

Survival benefit with treatment o 0 o 1 o 2 o 3 o 4 o 5

Quality of life o 0 o 1 o 2 o 3 o 4 o 5

Treatment toxicity o 0 o 1 o 2 o 3 o 4 o 5

Cancer-related symptoms o 0 o 1 o 2 o 3 o 4 o 5

Social supports o 0 o 1 o 2 o 3 o 4 o 5

Functional status o 0 o 1 o 2 o 3 o 4 o 5

Cognition o 0 o 1 o 2 o 3 o 4 o 5

Life expectancy in the absence

of cancer

o 0 o 1 o 2 o 3 o 4 o 5

b. Of the following factors, which do you rank as the three most important when making a

decision about treating an older adult with palliative chemotherapy for an advanced cancer.

(Rank 1 to 3 in order of importance)

o Performance status of the patient

o Cancer type

o Burden of disease

o Comorbidities

o Age

o Patient preference

o Survival benefit with treatment

o Quality of life

o Treatment toxicity

o Cancer-related symptoms

o Social supports

o Functional status

o Cognition

o Life expectancy in the absence of cancer

314

2. When making a recommendation regarding adjuvant chemotherapy for an older adult with

early stage cancer, many clinical and patient factors are considered.

a. How do you rate the importance of the following factors when making a decision about

treating an older adult with adjuvant chemotherapy for an early cancer?

Not at all

important

Very

important

Performance status o 0 o 1 o 2 o 3 o 4 o 5

Cancer type o 0 o 1 o 2 o 3 o 4 o 5

Burden of disease o 0 o 1 o 2 o 3 o 4 o 5

Comorbidities o 0 o 1 o 2 o 3 o 4 o 5

Age o 0 o 1 o 2 o 3 o 4 o 5

Patient preference o 0 o 1 o 2 o 3 o 4 o 5

Survival benefit with treatment o 0 o 1 o 2 o 3 o 4 o 5

Quality of life o 0 o 1 o 2 o 3 o 4 o 5

Treatment toxicity o 0 o 1 o 2 o 3 o 4 o 5

Cancer-related symptoms o 0 o 1 o 2 o 3 o 4 o 5

Social supports o 0 o 1 o 2 o 3 o 4 o 5

Functional status o 0 o 1 o 2 o 3 o 4 o 5

Cognition o 0 o 1 o 2 o 3 o 4 o 5

Life expectancy in the absence

of cancer

o 0 o 1 o 2 o 3 o 4 o 5

b. Of the following factors, which do you rank as the three most important when making a

decision about treating an older adult with adjuvant chemotherapy for an early cancer.

(Rank 1 to 3 in order of importance)

o Performance status of the patient

o Cancer type

o Burden of disease

o Comorbidities

o Age

o Patient preference

o Survival benefit with treatment

o Quality of life

o Treatment toxicity

o Cancer-related symptoms

o Social supports

o Functional status

o Cognition

o Life expectancy in the absence of cancer

315

3. Is there an age above which adjuvant chemotherapy should generally not be considered? (for

whatever reason)

o Yes

o No

If yes, what age?

o > 65 years

o > 70 years

o > 75 years

o > 80 years

o >85 years

4. Is there an age above which palliative chemotherapy should generally not be considered? (for

whatever reason)

o Yes

o No

If yes, what age?

o 65 years

o > 70 years

o > 75 years

o > 80 years

o >85 years

SECTION 3 Attitudes towards chemotherapy decision-making in older adults

1. To what extent do you agree with the following statements regarding decision-making about

chemotherapy for older adults with cancer?

Strongly

disagree

Disagree Neither

agree nor

disagree

Agree Strongly

agree

I am confident in my ability to assess

an older patient’s suitability for

chemotherapy

o o o o o

I am confident in my ability to predict

which older patients are likely to

experience toxicity from chemotherapy.

o o o o o

A clinical tool that predicts the

likelihood of significant chemotherapy-

related toxicity in older adults would be

useful.

o o o o o

There is a role for geriatricians in

treatment decision-making for older

adults with cancer.

o o o o o

There is a role for geriatricians in the

management of older adults with

cancer.

o o o o o

My clinical practice has adequate

access to a geriatric medicine service

o o o o o

316

SECTION 4 Clinical assessment of older patients prior to a decision about chemotherapy

1. Consider how you routinely evaluate an older patient to make a decision regarding

chemotherapy. (‘Routinely’ means >50% of the time)

When evaluating an older patient prior to making a decision about chemotherapy, I:

(Select all that apply)

o Take a history and perform a clinical examination

o Assess performance status

o Assess cognition

o Assess nutrition

o Assess psychological state

o Assess functional status

o Assess social supports

o Assess number of medications

o Enquire about a history of falls

o Use a geriatric screening tool

o Use a geriatric assessment

o Consult with a geriatrician

2. To assess functional status, I use:

o An informal assessment based on my history and examination

o A formal assessment of functional status, such as Katz or Barthel Indices of ADLs/IADLs

o Other ____________

o I do not routinely assess functional status

3. To assess cognition, I use:

o An informal assessment based on my history and examination

o A formal assessment of cognition, such as the MMSE

o Other ____________

o I do not routinely assess the cognition of older patients

4. To assess nutrition, I use:

o An informal assessment based on my history and examination

o A formal assessment of nutrition, such as the mini-nutritional assessment (MNA)

o Other _____________

o I do not routinely assess the nutrition of older patients

5. To assess psychological state, I use:

o An informal assessment based on my history and examination

o A formal assessment or screening tool, such as the Geriatric Depression Scale (GDS), or

Hospital and Anxiety Depression Score (HADS)

o Other ______________

o I do not routinely assess psychological state

317

SECTION 5 Advanced cancer scenario

Consider a hypothetical patient with a new diagnosis of an incurable cancer. Their current

comorbidities are hypertension that is well managed with medication, and mild asthma managed with

bronchodilators. They live with a supportive partner and are independent in all instrumental and basic

activities of daily living. You rate their ECOG Performance Status as 1. They have some cancer-

related symptoms that are interfering with their quality of life. They are willing to be guided by your

recommendation regarding treatment.

The only evidence of benefit is for intravenous chemotherapy. Clinical trials of this chemotherapy

showed response rates of 40% with an improvement in median overall survival of 3 months (from 6

months to 9 months).

1. Consider the rate of severe (G3 to 5) toxicity of this chemotherapy as 10%. How likely are

you to recommend this chemotherapy if the patient were aged?

Very likely Likely Neutral Unlikely Very unlikely

70 years o o o o o

75 years o o o o o

80 years o o o o o

≥85 years o o o o o

2. Consider the rate of severe (G3 to 5) toxicity of this chemotherapy as 40%. How likely are

you to recommend this chemotherapy if the patient were aged?

Very likely Likely Neutral Unlikely Very unlikely

70 years o o o o o

75 years o o o o o

80 years o o o o o

≥85 years o o o o o

318

SECTION 6 Early stage cancer scenario

Consider a hypothetical patient with a completely resected early-stage cancer. The risk of disease

recurrence of this cancer at 5 years is 40%, and the 5-year survival rate is 70% without further

treatment. Their current comorbidities are hypertension that is well managed with medication, and

mild asthma managed with bronchodilators. They live with a supportive partner and are independent

in all instrumental and basic activities of daily living. You review them 6 weeks following surgery.

They have recovered well and you rate their ECOG Performance Status as 1.

For this type of cancer, treatment with adjuvant intravenous chemotherapy reduces the risk of

recurrence by 25% (from 40% to 30%), and gives an absolute improvement in overall survival at 5

years of 5%. The patient has no strong preference regarding adjuvant chemotherapy and is willing to

be guided by your recommendation.

1. Consider the rate of severe (G3 to 5) toxicity of this chemotherapy as 10%. How likely are

you to recommend this chemotherapy if the patient were aged?

Very likely Likely Neutral Unlikely Very unlikely

70 years o o o o o

75 years o o o o o

80 years o o o o o

≥85 years o o o o o

2. Consider the rate of severe (G3 to 5) toxicity of this chemotherapy as 40%. How likely are

you to recommend this chemotherapy if the patient were aged?

Very likely Likely Neutral Unlikely Very unlikely

70 years o o o o o

75 years o o o o o

80 years o o o o o

≥85 years o o o o o

319

SURVEY COMPLETE

Thank you for your time.

You have reached the end of the survey.

If you would like to make any comments about the survey or treatment decision making for older

adults with cancer, please do so in the space provided below.

__________________________________________________________________________________

__________________________________________________________________________________

__________________________________________________________________________________

__________________________________________________________________________________

_________________________________________________________________________________

320

Appendix B. Geriatric assessment used in this thesis

INVESTIGATOR ASSESSMENT – GERIATRIC ASSESSMENT

Participant number: _____________

This Geriatric Assessment form is to be completed by the Principal Investigator. Some sections of the

form are completed through direct questioning of the participant; other sections of the form are

completed through direct observation and measurement, or through review of the clinical record.

Explanatory notes are provided with each section. Text in italics and quotation marks is the suggested

wording presented to participants (patients).

INTRODUCTION TO PATIENT

“Part of this study is to better describe the level of independence and function of the patients 65 years

of age and older that we treat with chemotherapy. In order to do this, I will be taking you through a

series of questions about you, including specific questions about your level of independence and

available supports, your nutrition, your memory, and your physical abilities.”

Section 1: DEMOGRAPHICS

“The first series of questions is to gather basic information about yourself.”

How old are you? Age: _________ years

Sex: male female

Are you currently working (paid employment)?

Yes no

Are you currently married, widowed, separated / divorced, or single?

Married widowed divorced / separated single

Are you presently living alone, or with others (partner, family, or friends)?

Lives alone lives with others

What language do you mainly speak at home?

English non-English

Do you receive any community services to help you at home?

Yes no

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Section 2: GENERAL HEALTH

“In general, how would you rate your health today? Would you give it a rating of excellent, very

good, good, fair, or poor?”

Excellent very good good fair poor

“In comparison with other people of the same age, how would you rate your health status?”

Not as good as good better does not know

Section 3: FUNCTIONAL STATUS

The Timed Up and Go

The Timed Up and Go is performed by the patient and observed by the Principal Investigator. It may

be performed at the end of the assessment if logistically easier.

“One way of looking at a person’s general health is to watch them walk. Would it be okay if I watched

you walking up and down the corridor?”

Test instructions:

The participant is asked to sit in an armed chair. A mark on the floor is made 3 metres away.

The participant is instructed: “On the word GO, you will stand up, walk to the line on the floor, turn

around and walk back to the chair and sit down. Please walk at your own pace, there is no hurry.”

(The patient’s usual mobility aids should be used.)

The time to complete the task is recorded in seconds.

Timed Up and Go: __________ seconds

Timed Up and Go ≥ 14 seconds?

Yes No

322

Activities of Daily Living – The Katz Index

This section is completed after direct questioning of the patient about their activities of daily living.

The Katz Index of ADLs is provided as a guide for clarifying the level of assistance needed.

“Now I would like to ask you some questions about things we all need to do every day.”

Are you able to bathe (wash / shower) without help, or do you need some help?

Independent Dependent

Are you able to get dressed without help, or do you need some help?

Independent Dependent

Are you able to go to the toilet without help, or do you need some help?

Independent Dependent

Are you able to get in and out of bed by yourself, or do you need help?

Independent Dependent

Do you have any trouble with losing control of your bladder or bowels, or need to use catheters or

regular enemas?

Independent Dependent

If you have a meal in front of you, are you able to feed yourself, or do you need some help?

Independent Dependent

ACTIVITIES INDEPENDENCE (0 point)

No supervision, direction or personal

assistance

DEPENDENCE (1 points)

With supervision, direction, personal

assistance or total care

Bathing Assistance only in bathing a single body

part or bathes self completely

Assistance in bathing more than one

part, assistance getting in or out of tub,

does not bathe self

Dressing Gets clothes, puts them on, manages

buttons etc; help with shoe laces allowed

Does not dress self or remains partly

undressed

Toileting Gets to toilet, gets on and off, cleans,

manages clothing

Uses bedpan or commode or receives

assistance getting to and using toilet

Transferring Moves in and out of bed independently

and in and out of chair independently

(may have aids)

Needs assistance

Continence Urination/defaecation entirely self-

controlled

Partial or total incontinence, partial or

total control by enemas/pans/catheters

Feeding Gets food from plate into mouth

(assistance with cutting up food

allowed)

Assistance in act of feeding

Total points (0 - 6) = _____________ (0 = independent in all tasks 6 = dependent in all tasks)

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Instrumental Activities of Daily Living – OARS Functional Assessment

“Now I’d like to ask you about some of the activities of daily living, things that we all need to do as

part of our daily lives. I would like to know if you can do these activities without any help at all, or if

you need some help to do them, or if you can’t do them at all.”

1. Can you use the telephone…

Without help, including looking up numbers and dialling

With some help (can answer phone or dial in an emergency, but need a special phone or help

getting the number or dialling)

Or are you completely unable to use the telephone?

2. Can you get to places out of walking distance…

Without help (can travel alone on buses, taxis, or drive your own car)

With some help (need someone to help you or go with you when travelling)

Or are you unable to travel unless emergency arrangements are made like in an ambulance?

3. Can you go shopping for groceries or clothes (assuming you have transport)…

Without help (taking care of all shopping needs yourself, assuming you had transportation)

With some help (need someone to go with you on all shopping trips)

Or are you completely unable to do any shopping?

4. Can you prepare your own meals…

Without help (plan and cook full meals yourself)

With some help (can prepare some things but unable to cook full meals yourself)

Or are you completely unable to prepare any meals?

5. Can you do your housework…

Without help

With some help (can do light housework but need help with heavy work)

Or are you completely unable to do any housework?

6. Can you take your own medicine…

Without help (in the right doses at the right time)

With some help (able to take medicine if someone prepares it and / or reminds you)

Or are you completely unable to take your medicine?

7. Can you handle your own money…

Without help (write cheques, pay bills)

With some help (manage day to day buying but need help with bills)

Or are you completely unable to handle money?

Score 2 for without help; Score 1 for with some help; Score 0 for answers with unable

Total score = __________ (range 0 to 14)

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MOS-physical functioning measure

“Now I would like to ask you a few more questions about daily activities. The following are activities

you might do during a typical day. I would like to know if your health limits you a lot, a little or not at

all in doing these activities.

Does your health limit you in…”

1. Vigorous activities, such as running, lifting heavy objects, participating in strenuous sports?

Yes, limited a lot Yes, limited a little No, not limited at all

2. Moderate activities, such as moving a table, pushing a vacuum cleaner, bowling or playing golf?

Yes, limited a lot Yes, limited a little No, not limited at all

3. Lifting or carrying groceries?

Yes, limited a lot Yes, limited a little No, not limited at all

4. Climbing several flights of stairs?

Yes, limited a lot Yes, limited a little No, not limited at all

5. Climbing one flight of stairs?

Yes, limited a lot Yes, limited a little No, not limited at all

6. Bending, kneeling or stooping?

Yes, limited a lot Yes, limited a little No, not limited at all

7. Walking more than one mile?

Yes, limited a lot Yes, limited a little No, not limited at all

8. Walking several blocks?

Yes, limited a lot Yes, limited a little No, not limited at all

9. Walking one block?

Yes, limited a lot Yes, limited a little No, not limited at all

10. Bathing or dressing yourself?

Yes, limited a lot Yes, limited a little No, not limited at all

Score 1 for limited a lot; Score 2 for limited a little; Score 3 for not limited at all

Total Score: ____________ (range 10 to 30)

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Section 4: COMORBIDITY AND POLYPHARMACY

“Do you have any medical problems in addition to a diagnosis of cancer?”

__________________________________________________________________________________

__________________________________________________________________________________

__________________________________________________________________________________

__________________________________________________________________________________

The Cumulative Illness Rating Scale – Geriatrics (CIRS-G)

The Investigator will complete the CIRS-G through review of the EMR and clarification, where

necessary, with the participant based on the above question.

Refer to the CIRS-G manual for further instructions

Rating of comorbidities:

0 – No problem

1 – Current mild problem or past significant problem

2 – Moderate disability or morbidity / requires “first line” therapy

3 – Severe / constant significant disability / “uncontrollable” chronic problems

4 – Extremely severe / immediate treatment required / end organ failure / severe impairment in

function

Score

Heart

Vascular

Haematopoietic

Respiratory

Eyes, ears, nose and throat

Upper gastrointestinal

Lower gastrointestinal

Liver

Renal

Genitourinary

Musculoskeletal

Neurological

Endocrine / metabolic and breast

Psychiatric illness

Total number categories endorsed = ___________

Total score = ___________

CIRS-G Severity Index Score (range 0-4) = ___________

(total score / number of categories endorsed)

Polypharmacy

“How many medications are you taking at home?” (Includes prescription and over-the-counter)

Medication count: ____________

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Section 5: COGNITION

The Short Blessed Test (Orientation Memory Concentration)

“Now I would like to ask you some questions to check your memory and concentration. Some of them

may be easy and some of them may be hard.”

1. What year is it now? __________

Correct (0) Incorrect (1)

2. What month is it now? __________

Correct (0) Incorrect (1)

Please repeat this name and address after me (repeat until learnt):

John Brown, 42 Market Street, Chicago

Good, now remember that name and address for a few minutes.

3. Without looking at your watch or clock, tell me about what time it is. (within one hour)

Correct (0) Incorrect (1)

4. Count aloud backwards from 20 to 1

20 19 18 17 16 15 14 13 12 11

10 9 8 7 6 5 4 3 2 1

(Mark correctly sequenced numbers)

0 errors 1 error 2 errors

5. Say the months of the year in reverse order. Start with the last month of the year. The last month

of the year is…

D N O S A JL JN MY AP MR F J

0 errors 1 error 2 errors

6. Repeat the name and address I asked you to remember. (John, Brown, 42, Market, Chicago)

0 errors 1 error 2 errors 3 errors 4 errors 5 errors

Item Errors (0 - 5) Weighting factor Item score

1 X 4

2 X 3

3 X 3

4 X 2

5 X 2

6 X 2

TOTAL SCORE = ___________ (range 0 to 28)

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Section 6: PSYCHOSOCIAL FUNCTION

“The next few questions relate to how you are feeling.”

The Geriatric Depression Scale 5-Item Short Form

Item Score

Are you basically satisfied with your life? Yes No

Do you often get bored? Yes No

Do you often feel helpless? Yes No

Do you prefer to stay home rather than going out and doing new things? Yes No

Do you feel pretty worthless the way you are now? Yes No

“No” to question 1 is scored as 1.

“Yes” to questions 2,3,4,5 are scored as 1.

Total score = _______________ (A score of 2 or higher indicates possible depression.)

The Modified MOS Social Support Survey – tangible subscale

“People sometimes look to others for companionship, assistance, or other types of support. How often

is each of the following kinds of support available to you if you need it?” (May use answer prompt

sheet 3)

1. Someone to help you if you were confined to bed?

None of the time A little of the

time

Some of the time Most of the time All of the time

1 2 3 4 5

2. Someone to take you to the doctor if you need it?

None of the time A little of the

time

Some of the time Most of the time All of the time

1 2 3 4 5

3. Someone to prepare your meals if you are unable to do it yourself?

None of the time A little of the

time

Some of the time Most of the time All of the time

1 2 3 4 5

4. Someone to help with daily chores if you were sick?

None of the time A little of the

time

Some of the time Most of the time All of the time

1 2 3 4 5

Total score: __________ (range 4 to 20)

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Section 7: NUTRITION

Height: _______________m

Weight: _______________kg

Body Mass Index: _______________kg/m^2

“Have you lost any weight (without trying) in the last 6 months?”

YES NO

Mini Nutritional Assessment – Short Form

Item Scoring Score

A Has food intake declined over the past 3

months due to loss of appetite, digestive

problems, chewing or swallowing

difficulties?

0 = severe decrease in food intake

1 = moderate decrease in food

intake

2 = no decrease in food intake

B Weight loss during the last 3 months 0 = weight loss greater than 3kg

1 = does not know

2 = weight loss between 1 and 3kg

3 = no weight loss

C Mobility 0 = bed or chair bound

1 = able to get out of bed / chair but

does not go out

2 = goes out

D Has suffered psychological stress or acute

disease in the past 3 months?

0 = yes

2 = no

E Neuropsychological problems 0 = severe dementia or depression

1 = mild dementia

2 = no psychological problems

F Body mass index 0 = BMI <19

1 = BMI 19 to < 21

2 = BMI 21 to < 23

3 = BMI 23 or greater

Screening score = ___________ (maximum 14 points)

12 to 14 points: normal nutritional status

8 to 11 points: at risk of malnutrition

0 to 7 points: malnourished

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THE G8 SCREENING QUESTIONNAIRE

The G8 Screening Questionnaire can be completed in its entirety using data obtained within the

Geriatric Assessment. Location of data is indicated in parentheses under each Item description.

Item Answer Score

Has food intake declined over the

past 3 months due to loss of

appetite, digestive problems, or

chewing or swallowing difficulties?

(answer within MNA short form)

Severe decrease in food intake 0

Moderate decrease in food intake 1

No decrease in food intake 2

Weight loss during the last 3 months

(answer within MNA short form)

Weight loss > 3kg 0

Does not know 1

Weight loss between 1 and 3kg 2

No weight loss 3

Mobility

(answer within MNA short form)

Bed or chair bound 0

Able to get out of bed/chair but does not go out 1

Goes out 2

Neuropsychological problems

(from Comorbidity section of GA)

Severe dementia or depression 0

Mild dementia or depression 1

No psychological problems 2

Body Mass Index

(calculated by investigator)

BMI < 18.5 0

BMI 18.5 to <21 1

BMI 21 to <23 2

BMI 23 to >23 3

Takes more than 3 prescription

drugs per day

(from Comorbidity section of GA)

Yes 0

No 1

In comparison with other people of

the same age, how do they consider

their health status?

(from General Health section of

GA)

Not as good 0

Does not know 0.5

As good 1

Better 2

Age >85yrs 0

80-85yrs 1

<80yrs 2

Total Score: __________ (range 0 – 17)

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Appendix C. Geriatric Assessment Results Summary

GERIATRIC ASSESSMENT RESULTS SUMMARY FOR TREATING ONCOLOGIST

Functional Status

No / one or more dependencies in basic activities of daily living (ADLs):

bathing dressing toileting transfers continence feeding

No / one or more dependencies in instrumental activities of daily living (IADLs):

telephone transport shopping meals housework medicines finances

Limited / not limited by their health in their physical functioning:

Score of: ________ (range 10 – 30; where 10 is limited a lot in all tasks, 30 is not limited in any task)

History of falls in the last 6 months: yes no

Your patient took _______ seconds to walk 3m and back from a seated position.

Where ≥14s is associated with impaired mobility & risk of falls.

Comorbidity

Your patient reported having ______ mild comorbidities, ______ moderate comorbidities, _____

severe comorbidities, ______ life threatening comorbidities.

Polypharmacy

Your patient takes ______ different medications at home.

Cognition

On a screening test for cognitive impairment, score of _____ (range 0 to 28) indicating:

normal cognition (0-4) questionable impairment for evaluation (5-9) likely dementia (≥10)

Psychological health

On a screening test for depression, score of ______ (range 0 to 5) indicating:

≥2 possible depression no evidence of depression

Social supports

Score of ________ (range 4 to 20) on a social support scale, where a higher score indicates

greater availability of social supports.

Nutrition

BMI: _________ Unintentional weight loss in last 6 months? yes no

On a screening test for nutrition, score of ______ (range 0 to 14), indicating:

normal nutrition (≥12) at risk of malnutrition (8 to 11) malnutrition (0 to 7)

331

CARG TOXICITY SCORE RESULTS

CARG Toxicity Score* of _________ (range 0 to 23), where a higher score reflects a greater risk

of grade 3 to 5 treatment related toxicity.

This places them in the following risk group:

Low risk (score 0-5), where the rate of G3-5 toxicity in this group approximates 30%

Intermediate risk (score 6-9), where the rate of G3-5 toxicity in this group approximates 52%

High risk (score ≥10), where the rate of G3-5 toxicity in this group approximates 83%

*The CARG Toxicity Score uses 11 clinical and geriatric variables to provide a risk score that has

been shown to be moderately predictive of the occurrence of grade 3 to 5 treatment toxicity (includes

haematological toxicity) in older adults starting chemotherapy.

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Appendix D. Treating oncologist’s questionnaire

TREATING ONCOLOGIST’S STUDY SPECIFIC QUESTIONNAIRE

Cancer type: __________ Treatment intent: _________ Chemotherapy: _____________

Initial dose plan: _________

The following are to be answered after review of the patient’s Geriatric Assessment results and

CARG Score

1. Consider your existing chemotherapy treatment plan for this patient. How does your

chemotherapy plan compare to what would be considered standard treatment for a

younger, fitter patient with this type and stage of cancer?

No different

Using the same regimen but at a reduced dose

Using a less intensive regimen (eg. single agent over combination, or fractionated schedule), at a

standard dose

Using a less intensive regimen, at a reduced dose

2. Was the information contained in the Geriatric Assessment consistent with your clinical

impression of this patient?

Yes No

3. Did the Geriatric Assessment provide you with any new information about your patient?

Yes No

4. If you answered “YES” to ‘3’ above, in what areas did the Geriatric Assessment provide you

with new information about your patient? Please tick all fields that apply.

Functional status Comorbidity Polypharmacy

Cognition Nutrition

Psychological health Social supports

5. Based on the results of the Geriatric Assessment, would you consider any of the following

interventions? (I.e. interventions that would not otherwise have been done)

Referral to a geriatrician

Referral to a social worker

Referral to a physiotherapist

Referral to an occupational therapist

Referral to a dietician

Referral to a psychologist or psychiatric service

Clinical evaluation for cognitive impairment

Review of medications

Provision of community services or supports

Other:___________________________

333

6. If you had known the results of this patient’s geriatric assessment prior to making a

treatment decision, would you have modified your existing chemotherapy recommendation

on the basis of any of the information gained from this Geriatric Assessment?

Yes

No

7. If “YES” to the above, how would you have modified your chemotherapy recommendation?

I would have no longer recommended chemotherapy

I would have reduced the planned chemotherapy dose

I would have changed to a less intensive regimen

I would have increased the planned chemotherapy dose

I would have changed to a more intensive regimen

8. If you had known this patient’s CARG Toxicity Score prior to making a treatment decision,

would you have modified your existing chemotherapy recommendation on the basis of this

patient’s CARG Toxicity Score?

Yes

No

9. If “YES” to the above, how would you have modified your chemotherapy recommendation?

I would have no longer recommended chemotherapy

I would have reduce the planned chemotherapy dose

I would have changed to a less intensive regimen

I would have increased the planned chemotherapy dose

I would have changed to a more intensive regimen

10. Select the response that applies to you.

a. I found the following results useful.

Strongly

disagree

Disagree Neither

agree nor

disagree

Agree Strongly

agree

The Geriatric Assessment

The CARG Toxicity Score

b. I found the following results easy to interpret.

Strongly

disagree

Disagree Neither

agree nor

disagree

Agree Strongly

agree

The Geriatric Assessment

The CARG Toxicity Score

334

Appendix E Participant questionnaire

CHEMOTHERAPY DECISION-MAKING IN OLDER ADULTS

Questionnaire

Thank you for agreeing to participate in this study.

You have recently seen a cancer specialist to discuss and make a decision about treatment for your

cancer. We are interested to know more about your role in the treatment decision-making process and

what was important to you when the treatment decision was made.

This survey should take you up to 20 minutes to complete.

Please complete this survey within 12 weeks (3 months) of having discussed chemotherapy with your

cancer specialist.

When you have completed the survey, use the following options to return it:

BY POST

Please place it in the reply-paid envelope provided and post it:

At your nearest post-box

At your nearest Australia Post outlet

At the hospital you attend when seeing your cancer specialist:

OR…

RETURN TO YOUR CANCER CENTRE

Place the survey in the reply paid envelope provided and place it in the survey box at your

cancer centre.

This study has been approved by the Human Research Ethics Committee – CRGH of the Sydney Local

Health District. If you have any concerns or complaints about the conduct of the research study, you

may contact the Executive Officer of the Ethics Committee, on (02) 9767 5622.

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SECTION 1 Information about yourself

We would like to ask some general questions about you. For each question, please write your answer

or tick one of the possible responses.

1. How old are you? _______________ years

2. Are you?

Male

Female

Prefer not to answer

3. What language do you speak at home?

English

Other: _____________________

4. Who do you live with?

I live alone

I live with a partner or spouse

I live with other family

I live with friend(s)

5. Do you have any children?

Yes No

6. Do you have any children who are dependent on your support?

Yes No

7. What is the highest level of education you have completed?

Primary school

High school

Trade or technical qualification

University or college degree

8. What is your marital status?

Married / de facto

Separated / divorced

Widowed

Single

9. Just before your diagnosis of cancer, what was your employment status?

Employed

Unemployed (not retired or on pension)

Retired or on a pension

336

10. Has a close friend or relative of yours died from cancer?

Yes No

11. How much of the time would relatives / friends be available to care for you if you needed it?

None of the time

Some of the time

Most of the time

All of the time

12. What type of cancer do you have? (That is, where your cancer started)

Breast cancer

Bowel (colon or rectal) cancer

Stomach (gastric) cancer

Pancreas cancer

Prostate cancer

Ovarian cancer

Bladder cancer

Uterus (endometrial) cancer

Brain cancer

Lung cancer

Head and neck cancer

I am not sure

Other: _________________________________

13. Are you going to have (or are currently having) chemotherapy?

Yes

No

I am not sure

14. How long does it take you to travel to see your cancer specialist?

Less than 20 minutes

20 minutes to 1 hour

More than 1 hour

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SECTION 2 Your general health

1. How would you rate your current overall quality of life?

Make a mark on the line below to indicate how you rate your present overall quality of life,

ranging from “worst possible” on the far left to “best possible” on the far right.

2. Which of the following statements best describes your current ability to do things?

Normal with no limitations

Not my normal self, but able to be up and about with fairly normal activities

Not feeling up to most things, but in bed or a chair less than half the day

Able to do little activity and spend most of the day in bed or a chair

Pretty much bedridden, rarely out of bed

3. Compared to other people your age, would you say that your health is:

Poor

Fair

Good

Very good

Excellent

4. How much difficulty, on average, do you have with the following physical activities?

No

difficulty

A little

difficulty

Some

difficulty

A lot of

difficulty

Unable

to do

a. Stooping, crouching or kneeling?

b. Lifting, or carrying objects as heavy

as 5kg?

c. Reaching or extending arms above

shoulder level?

d. Writing, or handling and grasping

small objects?

e. Walking about 400 metres?

f. Heavy housework such as scrubbing

floors or washing windows?

5. Because of your health or a physical condition, do you have any difficulty in doing the

following?

a. Shopping for personal items (like toilet items or medicines)?

YES Do you get help with shopping? YES NO

NO

DON’T DO Is that because of your health? YES NO

Worst possible

quality of life

Best possible

quality of life

338

b. Managing money (like keeping track of expenses or paying bills)?

YES Do you get help with managing money? YES NO

NO

DON’T DO Is that because of your health? YES NO

c. Walking across the room (using a cane or walker is okay)?

YES Do you get help with walking? YES NO

NO

DON’T DO Is that because of your health? YES NO

d. Doing light housework (like washing dishes, straightening up, or light cleaning)?

YES Do you get help with light housework? YES NO

NO

DON’T DO Is that because of your health? YES NO

e. Bathing or showering?

YES Do you get help with bathing or showering? YES NO

NO

DON’T DO Is that because of your health? YES NO

339

SECTION 3 Decision-making about chemotherapy

You have recently discussed with your cancer specialist treatment options for your cancer including

chemotherapy. People make decisions about cancer treatments in different ways.

1. Consider how you prefer to make decisions about treatments for your cancer, such as

whether or not to have chemotherapy.

Which of the following statements best describes how you prefer to make decisions about

chemotherapy? (Tick one of the following)

I prefer to make the final selection about which treatment I will receive.

I prefer to make the final selection of my treatment after seriously considering my doctor’s

opinion.

I prefer that my doctor and I share responsibility for deciding which treatment is best for me.

I prefer that my doctor make the final decision about which treatment will be used but

seriously consider my opinion.

I prefer to leave all decisions regarding my treatment to my doctor.

2. Think back to the decision you recently made with your cancer specialist about

chemotherapy.

Which of the following statements best describes the role you actually played in making the

decision about chemotherapy? (Tick one of the following)

I made the final selection about which treatment I would receive.

I made the final selection of my treatment after seriously considering my doctor’s opinion.

My doctor and I shared responsibility for deciding which treatment was best for me.

My doctor made the final decision about which treatment would be used but seriously

considered my opinion.

My doctor made all the decisions regarding my treatment.

340

3. In making a decision about chemotherapy, you may have considered the opinions of others.

The opinion of others may or may not be important to you.

In making a decision about chemotherapy, how much did you take into account the opinion

of the following persons? (Tick one box per row)

I do not take

their opinion

into account

at all

I care little

about their

opinion

I care

somewhat

about their

opinion

I take their

opinion

seriously

I take their

opinion

very

seriously

Does

not

apply

Your partner

Your children

Your other

family

Your friends

Your colleagues

Your cancer

specialist

Your local

doctor (GP)

4. During the most recent consultation that you discussed chemotherapy with your cancer

specialist, who was also present? (Tick all that apply)

I was on my own

My partner or spouse

My children

Other family members

A friend

A carer

341

5. When making a decision with your doctor about chemotherapy, you may have thought

about a number of things that are important to you.

a. HOW important were the following to you in making a decision about chemotherapy?

(Rate importance from “not at all important” to “very important.” Tick one response per row.)

Not at all

important

A little

important

Moderately

important

Very

important

My doctor’s recommendation

Living longer

My quality of life

Doing everything possible to fight

the cancer

The side effects of chemotherapy

The benefits of chemotherapy

My other health problems

How old I am

Having someone to look after me

during treatment

How far I would need to travel for

treatment

Maintaining my independence

Being able to look after my partner

/ spouse or family

b. What was THE MOST important thing influencing your decision about chemotherapy?

(Tick one of the following)

My doctor’s recommendation

Living longer

My quality of life

Doing everything possible to fight the cancer

The side effects of chemotherapy

The benefits of chemotherapy

My other health problems

How old I am

Having someone to look after me during treatment

How far I would need to travel for treatment

Maintaining my independence

Being able to look after my partner / spouse or family

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SECTION 4 Satisfaction with your decision

1. Consider again the final decision that was made about chemotherapy for your cancer.

Please indicate to what extent each statement is true for you at this time.

(Tick one box per row)

Strongly

disagree

Disagree Neither

agree

nor

disagree

Agree Strongly

agree

a. I am satisfied that I am adequately

informed about the issues important

to my decision

b. The decision I made was the best

decision possible for me personally

c. I am satisfied that my decision was

consistent with my personal values

d. I expect to successfully carry out (or

continue to carry out) the decision I

made

e. I am satisfied that this was my

decision to make

f. I am satisfied with my decision

343

SECTION 5 Information about your cancer

1. Based on the information your cancer doctor gave you, in your situation do you expect

chemotherapy to:

a. Cure your cancer?

Yes No Unsure

b. Control the growth or spread of your cancer?

Yes No Unsure

c. Make you feel better?

Yes No Unsure

d. Make you feel worse?

Yes No Unsure

e. Live longer?

Yes No Unsure

2. Did your cancer doctor discuss the following with you?

a. If your cancer is able to be cured?

Yes No I do not recall

Did you want them to discuss this? Yes No

b. If your cancer is expected to shorten your life?

Yes No I do not recall

Did you want them to discuss this? Yes No

c. The length of time you may be expected to live?

Yes No I do not recall

Did you want them to discuss this? Yes No

d. The expected side effects and risks of chemotherapy?

Yes No I do not recall

Did you want them to discuss this? Yes No

e. The expected benefits of chemotherapy?

Yes No I do not recall

Did you want them to discuss this? Yes No

344

3. When making a decision about having treatment with chemotherapy, a person may be

influenced by their understanding of how long they might have to live.

What is your understanding of how long you have to live?

I prefer not to answer this question

I don’t know

__________ (number) of months

__________ (number) of years

Do you have any additional comments or suggestions?

__________________________________________________________________________________

__________________________________________________________________________________

__________________________________________________________________________________

__________________________________________________________________________________

__________________________________________________________________________________

__________________________________________________________________________________

__________________________________________________________________________________

__________________________________________________________________________________

__________________________________________________________________________________

_______________________________________________________

END OF SURVEY

Thank you for taking the time to complete this questionnaire.

See the instructions on the front page for how to return your survey.

345

Appendix F Publications

REVIEW

Decision-making in geriatric oncology: systemic treatment considerations for olderadults with colon cancerErin B. Motha,b, Janette Vardya,b and Prunella Blinmana,b

aConcord Cancer Centre, Concord Repatriation General Hospital, Sydney, Australia; bSydney Medical School, University of Sydney, Sydney, Australia

ABSTRACTIntroduction: Colon cancer is common and can be considered a disease of older adults with more thanhalf of cases diagnosed in patients aged over 70 years. Decision-making about treatment with che-motherapy for older adults may be complicated by age-related physiological changes, impaired func-tional status, limited social supports, concerns regarding the occurrence of and ability to toleratetreatment toxicity, and the presence of comorbidities. This is compounded by a lack of high qualityevidence guiding cancer treatment decisions for older adults.Areas covered: This narrative review evaluates the evidence for adjuvant and palliative systemictherapy in older adults with colon cancer. The value of an adequate assessment prior to making atreatment decision is addressed, with emphasis on the geriatric assessment. Guidance in making atreatment decision is provided.Expert commentary: Treatment decisions should consider goals of care, a patient’s treatment prefer-ences, and weigh up relative benefits and harms.

ARTICLE HISTORYReceived 27 July 2016Accepted 29 September 2016

KEYWORDSColon cancer;chemotherapy; decision-making; older adult

1. Introduction

Colon cancer is the second most common cancer in Australia,with an estimated incidence rate of 62 new cases per 100,000persons in 2016 [1]. Colon cancer is a disease of older adults witha median age at diagnosis of 70 years and an increasing inci-dence with increasing age, highest in patients aged >80 years(430 per 100,000 compared to 97 per 100,000 in those aged55–59 years) [1]. As the population ages, there will be increasingnumbers of older adults with colon cancer who will requiretreatment. As such, specialists involved in their care need to beadept at the management of older adults with colon cancer. Formedical oncologists, this means the clinical assessment of olderadults, decision-making about treatment, and management ofolder adults with regard to systemic therapy.

Systemic therapy for colon cancer includes adjuvant che-motherapy, palliative chemotherapy, and palliative targetedtherapy. Adjuvant chemotherapy is given following resectionof a high-risk stage II or stage III colon cancer to improvedisease-free survival (DFS) and overall survival (OS), with thegoal of treatment being cure. Palliative chemotherapy andtargeted therapy are given for metastatic, incurable coloncancer to prolong progression-free survival (PFS) and OS andto relieve cancer-related symptoms and improve health-related quality of life (HRQOL) but is generally not curative.

Older adults with colon cancer, compared with younger adults,receive less chemotherapy inboth the adjuvant [2–4] andpalliativesettings [5]. Published rates of adjuvant chemotherapy use forstage III colon cancer in adults aged ≥70 years range from 35% inTheNetherlands [6] to 42–52% in the United States [2]. Reasons for

this are multifactorial and include the presence of comorbidities[7], age-related physiological changes [8], and geriatric problemssuch as impaired functional status, frailty, limited social supports,cognitive impairment, mood disturbance, polypharmacy, andmal-nutrition [9]. These factors affect patients’ suitability for systemictherapy, the ability to tolerate, and deliver the treatment andhighlight the complexity of decision-making about systemic ther-apy for older adults with cancer.

Decision-making about systemic therapy in patients of allages requires a trade-off between the benefits and harms ofthe treatment. In older adults, this trade-off is more tightlybalanced and skewed towards smaller survival benefits for agreater risk of treatment toxicity. This decision-making com-plexity is compounded by the limited evidence guiding treat-ment recommendations in this population due to theunderrepresentation of older adults in cancer clinical trials[10] and the paucity of randomized, prospective data for theuse of systemic therapy in this population [11].

The aim of this narrative review is to explore key issuessurrounding decision-making for systemic therapy for olderadults with colon cancer. Areas covered include the use ofchemotherapy in older adults in both adjuvant and palliativesettings, balancing benefits with harms, the appropriate assess-ment of older adults to better inform treatment decision-making,and consideration of patient preferences. Given the differencesin the approach to the curative treatment of colon and rectalcancers, here we review the evidence for the adjuvant treatmentof colon cancer only. As the palliative systemic treatment ofthese cancers is similar, the review of palliative treatment appliesto both colon and rectal (‘colorectal’) cancers.

CONTACT Prunella Blinman prunella.blinman@sswahs.nsw.gov.au Concord Cancer Centre, Concord Repatriation General Hospital, Building 76, Hospital Rd,Concord, NSW 2139, Australia

EXPERT REVIEW OF GASTROENTEROLOGY & HEPATOLOGY, 2016VOL. 10, NO. 12, 1321–1340http://dx.doi.org/10.1080/17474124.2016.1244003

© 2016 Informa UK Limited, trading as Taylor & Francis Group

2. Adjuvant chemotherapy considerations

Adjuvant chemotherapy is considered for patients withresected stage III (node-positive) or high-risk stage II coloncancer [12,13]. Single-agent chemotherapy with 5-fluorouracil(5-FU) plus a cofactor of leucovorin (LV) or levamisole reducesthe relative risk of recurrence of colon cancer by 30% at5 years (absolute improvement in DFS of 12% from 55% to67%, hazard ratio [HR]: 0.70, 95% confidence interval [CI]:0.63–0.78, p < .0001) and the relative risk of death by 26% at5 years (absolute improvement in OS of 7%, from 64% to 71%,HR: 0.74, 95% CI: 0.66–0.83, p < .0001) [14]. Capecitabine, anoral fluoropyrimidine, is at least as effective as bolus 5-FU/LV[15,16], but it differs in its toxicity profile as later discussed.

Combination chemotherapy with FOLFOX (oxaliplatin andinfusional 5-FU/LV), compared with infusional 5-FU/LV alone,had superior DFS and OS in the landmark MulticenterInternational Study of Oxaliplatin/5-Fluorouracil/Leucovorinin the Adjuvant Treatment of Colon Cancer (MOSAIC) trial,reducing the relative risk of recurrence by 20% (absoluteimprovement in DFS of 5.9% at 5 years from 67.4% to 73.3%,HR: 0.80, 95% CI: 0.68–0.93, p = .003) and the relative risk ofdeath by 16% (absolute improvement in OS of 2.5% at 6 yearsfrom 76% to 78.5%, HR: 0.84, 95% CI: 0.71–1.00, p = .046) [17].The DFS and OS benefits were limited to patients with stage IIIdisease, whereas patients with high-risk stage II disease had aninsignificant absolute improvement in 5-year DFS (82.3% ver-sus 74.6%, HR: 0.72, 95% CI: 0.50–1.02, p = not reported) [17].The National Surgical Adjuvant Breast and Bowel Project C-07(NSABP-07) trial of FLOX (oxaliplatin and bolus 5-FU/LV) [18]and the XELOXA (NO16968) trial of XELOX (oxaliplatin andcapecitabine) [19] also showed significant improvements inDFS and added further support for oxaliplatin-containing com-bination regimens. Six months of FOLFOX or XELOX che-motherapy is now the recommended standard of care for fitpatients with resected stage III colon cancer [13].

2.1. The evidence for adjuvant chemotherapy in olderadults

The inclusion of older adults in trials of adjuvant chemother-apy for colon cancer has been limited. The median age ofpatients participating in phase III adjuvant chemotherapy trialsis 61 years (interquartile range: 53–68 years) [20] with patientsaged ≥70 years comprising only 18% of patients [21]. Manytrials excluded patients based on age alone with an upperlimit of 75 years [17]. As such, evidence for the use of adjuvantchemotherapy in older adults comes largely from pooled sub-group analyses [14,21–23], subgroup analyses from individualrandomized trials [15,18,19,24,25], and large population-basedstudies [2–4,26] as summarized in Table 1.

2.2. Adjuvant chemotherapy with 5-FU

Two pooled analyses of seven phase III randomized trialscompared adjuvant 5-FU/LV to surgery alone in patients withstage III and selected stage II colon cancer [14,23]. Sargentet al. found similar efficacy of 5-FU/LV across 10-year agegroups, but increasing age was associated with higher rates

of grade 3 leucopenia [23]. Gill et al. [14] evaluated for pre-dictive and prognostic factors, including age, and found thatincreasing age was a negative prognostic factor for OS (likelydue to competing causes of mortality), but treatment benefitswere consistent across age groups.

Results of large population-based studies also support theuse of adjuvant 5-FU in older adults with colon cancer andreduce some of the selection bias of the pooled analyses ofonly including fitter patients suitable for clinical trials. Threelarge population-based studies, two accessing patient datafrom the Medicare/Surveillance, Epidemiology and EndResults Program (SEER) database [3,4] and one from theNational Cancer Database [26], of patients with stage IIIcolon cancer who had adjuvant 5-FU showed that older adultswere less likely to receive adjuvant chemotherapy [26], butobtained an OS benefit from the treatment [3,4,26], the mag-nitude of which may diminish with increasing age [3].

2.3. Capecitabine as an alternative to 5-FU

Oral capecitabine is a convenient alternative to intravenousschedules of 5-FU/LV and is considered an equivalent alter-native to 5-FU/LV as adjuvant and palliative chemotherapy forcolon cancer. Data supporting its use in older adults comefrom the Xeloda in Adjuvant Colon Cancer Therapy (X-ACT)trial that compared adjuvant capecitabine with bolus 5-FU/LVin patients with stage III colon cancer and included a sub-group analysis by age with 396 patients aged ≥70 years [15].Capecitabine, compared with bolus 5-FU/LV, had similar DFSand OS, which was maintained across all age groups (HR forDFS: 0.88, 95% CI: 0.77–1.01, p-value for non-inferiority <.0001;HR for OS: 0.86, 95% CI: 0.74–1.01, p-value for non-inferiority<.001). Of note, dose reductions were required more fre-quently in older patients (51% in those aged ≥70 years and39% in those aged <70 years), without an impact on efficacy.Capecitabine caused significantly less febrile neutropenia,neutropenia, stomatitis, alopecia, diarrhea, and nausea, butmore hand–foot syndrome, and this toxicity profile did notdiffer according to age group (<65 years and ≥65 years)[16,28].

2.4. Combination of oxaliplatin and 5-FU/LV orcapecitabine adjuvant chemotherapy

The benefit of the addition of oxaliplatin to adjuvant 5-FU/LVor capecitabine for older adults with colon cancer is unclear.Subgroup analyses of randomized trials have produced con-flicting results [18,19,24], and two of these trials excludedpatients over the age of 75 years [17,24]. Tournigand et al.analyzed outcomes of the 315 patients aged 70–75 years inthe MOSAIC trial of FOLFOX chemotherapy [24] and found noDFS or OS benefit in this subgroup (in those aged >70 years,HR for DFS: 0.93, 95% CI: 0.64–1.35, p = .710; HR for OS: 1.10,95% CI: 0.73–1.65, p = .661). However, this exploratory analysislacked power due to small numbers, and a large proportion ofthe older patients had stage II disease where there was noproven benefit of oxaliplatin in the trial. Tolerance of FOLFOXwas similar to younger patients. Yothers et al. [18] included ananalysis by age in the final results of the NSABP-C07 trial of

1322 E. B. MOTH ET AL.

Table1.

Keyelderly-specific

coloncancer

chem

otherapy

trials,sub

grou

panalyses,and

largepo

pulatio

n-basedstud

iesin

theadjuvant

setting.

Stud

yDesignandmetho

dsParticipants

Results

andcomments

Schm

olle

tal.[19]

Exploratorysubg

roup

analysisby

ageof

phaseIII

RCTXELO

Xvs.b

olus

5-FU

/LVin

stageIII

CC(NO16968trial);

todeterm

ine

treatm

enteffect

inthoseaged

<70

yearsvs.

≥70

years

N=1886

(total)

N=409(≥70

years)

N=1477

(<70

years)

DFS:X

ELOXimproved

DFS

comparedwith

bolus5-FU

/LV;

HR:

0.80,9

5%CI:0

.69–0.93,p

=0.004

≥70

years,HR:

0.86,9

5%CI:0

.64–1.16,p

=NR;

<70

years,HR:

0.80,9

5%CI:0

.67–0.94,p

=NR

OS:XELO

Ximproved

OScomparedwith

bolus5-FU

/LV;

HR:

0.83,9

5%CI:0

.70–0.99,p

=0.04

≥70

years,HR:

0.91,9

5%CI:0

.66–1.26,p

=NR;

<70

years,HR:

0.82,9

5%CI:0

.67–1.01,p

=NR

Note:improved

DFS

andOSwith

XELO

Xcomparedto

5-FU

/LV;

smallereffect

size

inolderpatients

Halleret

al.[22]

Pooled

analysisof

four

RCTs

instageIII

CC(XELOXA

/AVA

NT/X-

ACT/NSA

BP-C08)of

theadditio

nof

oxaliplatin

to5-FU

orcapecitabine;to

compare

efficacy

(OSandDFS)and

safety

(AE)

ofXELO

X/FO

LFOXvs.

5-FU

/LVaccording

toageand

comorbidity

N=4819

(total)

XELO

X/FO

LFOX:

N=2418

(<70

years)

N=480(≥70

years)

5-FU

/LV:

N=1497

(<70

years)

N=424(≥70

years)

DFS:X

ELOX/FO

LFOXimproved

DFS

comparedwith

5-FU

/LV;

HR:

0.69,9

5%CI:0

.63–0.76,p

<0.001

Forthoseaged

≥70

years,HR:

0.77,9

5%CI:0

.62–0.95,p

=0.014

Forthoseaged

<70

years,HR:

0.68,9

5%CI:0

.61–0.76,p

<0.0001

OS:XELO

X/FO

LFOXimproved

OScomparedwith

5-FU

/LV;

HR:

0.65,9

5%CI:0

.57–0.73,p

<0.001

Forthoseaged

≥70

years,HR:

0.78,9

5%CI:0

.61–0.99,p

=0.045

Forthoseaged

<70

years,HR:

0.62,9

5%CI:0

.54–0.72,p

<0.0001

AE:few

erserio

usG3/G4AE

sin

thoseaged

<70

years;oxaliplatin

-related

G3/G4AE

scomparableacross

ages

Note:benefit

inDFS

andOSfortheadditio

nof

oxaliplatin

regardless

ofageandmedicalcomorbidity;smallereffect

size

inolderpatients

McClearyet

al.[21]

Pooled

subg

roup

analysisfrom

seven

adjuvant

chem

otherapy

trials

instageII/III

CC(ACC

ENTdatabase)

comparin

gIV

5-FU

with

oralor

combinatio

nregimens;to

assess

impactof

ageon

CCrecurrence

and

mortality

N=14,528

(total)

N=11,953

(<70

years)

N=2575

(≥70

years)

DFS:Inthoseaged

≥70

years;HR:

0.94,9

5%CI:0

.78–1.13

Inthoseaged

<70

years;HR:

0.78,9

5%CI:0

.71–0.86;p

valueforage-treatm

entinteraction=0.09

TTR:

Inthoseaged

≥70

years;HR:

0.86,9

5%CI:0

.69–1.06

Inthoseaged

<70

years;HR:

0.77,9

5%CI:0

.69–0.85;p

valueforage-treatm

entinteraction=0.36

OS:In

thoseaged

≥70

years;HR:

1.04,9

5%CI:0

.85–1.27

Inthoseaged

<70

years;HR:

0.83,9

5%CI:0

.74–0.92;p

valueforage-treatm

entinteraction=0.05

Note:survival

benefit

ofoxaliplatin

redu

cedin

patientsaged

≥70

years.Oralfluorop

yrimidines

noninferiorto

IV5-FU

/LVacross

allage

grou

ps

Tournigand

etal.

[24]

Subg

roup

analysisof

MOSA

ICtrial(ph

ase

IIIRC

Tof

5-FU

/LV±

oxaliplatin

[FOLFOX4]in

stage

II/III

CC);to

determ

ine

treatm

enteffect

inpatientsaged

70–

75years

N=2246

(total)

N=315(70–75

years)

DFS:FOLFOX4

improved

DFS

comparedwith

5-FU

/LV;

HR:

0.80,9

5%CI:0

.68–0.93,p

=0.003

Forthoseaged

70–75years,HR:

0.93,9

5%CI:0

.64–1.35,p

=0.710

Forthoseaged

<70

years,HR:

0.78,9

5%CI:0

.66–0.92,p

=0.003

OS:FO

LFOX4

improved

OScomparedwith

5-FU

/LV;

HR:

0.84,9

5%CI:0

.71–1.00,p

=0.046

Forthoseaged

70–75years,HR:

1.10,9

5%CI:0

.73–1.65,p

=0.661

Forthoseaged

<70

years,HR:

0.80,9

5%CI:0

.66–0.97,p

=0.020

Note:no

sign

ificant

benefit

inDFS

orOSwith

theadditio

nof

oxaliplatin

to5-FU

inpatientsaged

70–75years

(Con

tinued)

EXPERT REVIEW OF GASTROENTEROLOGY & HEPATOLOGY 1323

Table1.

(Con

tinued).

Stud

yDesignandmetho

dsParticipants

Results

andcomments

Changet

al.[27]

Prospectiveph

aseII

feasibility

stud

yof

capecitabine

dose

escalatio

nin

patientsaged

≥70

years;

2000

mg/m

2 /day

(D1–D14),escalated

to2500

mg/m

2 /day

atcycle2

N=82

Intensity:d

oseescalatio

npo

ssible

in56

patients

41(50%

)completed

treatm

entwith

RDIo

f≥80%;4

1(50%

)completed

treatm

entwith

RDIo

f<80%

ordidno

tcompleteallcycles

Toxicity:G

3HFS

21(25.6%

)QOL:no

sign

ificant

change

inQOLover

time

Twelveset

al.[15]

Subg

roup

analysisof

phaseIII

RCT(non

-inferio

rity)

ofcapecitabine

vs.

bolus5-FU

/LV,stage

IIICC

(the

X-AC

Ttrial);

tocompare

efficacyacross

age

grou

ps(prim

aryend

pointDFS)

N=1987

(total)

N=396(≥70

years)

N=1591

(<70

years)

DFS:capecitabine

equivalent

to5-FU

,HR:

0.88,9

5%CI:0

.77–1.01,p

<0.0001;n

oage–treatm

entinteraction(p

=0.50)

Forthoseaged

≥70

years,HR:

0.97,9

5%CI:0

.72–1.31;for

thoseaged

40–69years,HR:

0.87,9

5%CI:0

.75–1.01

OS:capecitabine

equivalent

to5-FU

,HR:

0.86,9

5%CI:0

.74–1.01,p

=0.000116;n

oage–treatm

entinteraction(p

=0.78)

Forthoseaged

≥70

years,HR:

0.91,9

5%CI:0

.65–1.26;for

thoseaged

40–69years,HR:

0.87,9

5%CI:0

.73–1.04

Note:capecitabine

isan

equivalent

alternativeto

5-FU

;effe

ctmaintainedin

olderpatients.

Sano

ffet

al.[2]

Retrospectivecoho

rtstud

yof

patients

aged

≥75

yearswith

stageIII

CCfrom

four

databases:

SEER/M

edicare,

NYSCR

,CanCO

RS,

andNCC

N;to

determ

ineeffect

ofadjuvant

chem

otherapy

onsurvival

N=5489

(≥75

years)

Receiptof

anyadjuvant

chem

otherapy:

SEER:H

Rfordeath:

0.60,9

5%CI:0

.53–0.68,p

=NR

NYSCR

:HRfordeath:

0.76,9

5%CI:0

.58–1.01,p

=NR

CanC

ORS:H

Rfordeath:

0.48,9

5%CI:0

.19–1.21,p

=NR

NCC

N:H

Rfordeath:

0.42,9

5%CI:0

.17–1.03,p

=NR

Additio

nof

oxaliplatin

:SEER:H

Rfordeath:

0.84,9

5%CI:0

.69–1.04,p

=NR

NYSCR

:HRfordeath:

0.82,9

5%CI:0

.51–1.33,p

=NR

NCC

N:H

Rfordeath:

1.84,9

5%CI:0

.48–7.05,p

=NR

Note:patientsaged

≥75

yearshave

improved

survivalfrom

adjuvant

chem

otherapy,tho

ughtheincrem

entalb

enefitof

theadditio

nof

oxaliplatin

issm

all.

Yotherset

al.[18]

Exploratorysubg

roup

analysisof

phaseIII

RCTof

bolus5-FU

/LV

±oxaliplatin

(FLO

X)in

stageIIor

IIICC

(NSA

BP-C07

trial);

todeterm

ine

efficacy(DFS

and

OS)

inpatientsaged

≥70

years

N=2409

(total)

N=396(≥70

years)

N=2013

(<70

years)

DFS:FLO

Ximproved

DFS,H

R:0.82,9

5%CI:0

.72–0.93,p

=0.002

Forthoseaged

<70

years,HR:

0.76,9

5%CI:0

.66–0.88,p

<0.001

Forthoseaged

≥70

years,HR:

1.03,9

5%CI:0

.77–1.36,p

=0.87

OS:FLOXdidno

tsign

ificantlyimproveOS,HR:

0.88,9

5%CI:0

.75–1.02,p

=0.08

Forthoseaged

<70

years,HR:

0.80,9

5%CI:0

.68–0.95,p

=0.013

Forthoseaged

≥70

years,HR:

1.18,9

5%CI:0

.86–1.62,p

=0.3

Theeffect

ofoxaliplatin

onOSvariedsign

ificantlyby

age(p

interaction=0.039)

Toxicity:for

thoseaged

<70

years,G4or

G5toxicity

rate:9

%(5-FU)and10%

(FLO

X)Forthoseaged

≥70

years,G4or

G5toxicity

rate

13%

(5-FU)and20%

(FLO

X)Note:theadditio

nof

oxaliplatin

tobo

lus5-FU

/LVimproved

OSin

patientsaged

<70

years,bu

tno

tin

aged

≥70

years

Zuckerman

etal.[3]

Retrospectivecoho

rtstud

yof

patients

aged

≥66

yearswith

stageIII

CCusing

SEER-M

edicare

database;to

exam

inetheeffect

ofageon

benefit

from

adjuvant

chem

otherapy

N=7182

(≥66

years)

Treatm

ent:51.1%

received

chem

otherapy

aftersurgery;66–69years:19%;70–74

years:29.8%;75–79

years:29.7%;80–84

years:16.5%;≥

85years:

5.1%

Cancer

death:

66–69years:HR:

0.47,9

5%CI:0

.33–0.65,p

<0.001

70–74years:HR:

0.32,9

5%CI:0

.25–0.40,p

<0.001

75–79years:HR:

0.41,9

5%CI:0

.34–0.50,p

<0.001

80–84years:HR:

0.59,9

5%CI:0

.49–0.72,p

<0.001

≥85

years:HR:

0.54,9

5%CI:0

.41–0.71,p

<0.001

Note:agemod

ified

thesurvival

benefit

ofchem

otherapy;m

agnitude

ofbenefit

decliningwith

age

(Con

tinued)

1324 E. B. MOTH ET AL.

Table1.

(Con

tinued).

Stud

yDesignandmetho

dsParticipants

Results

andcomments

Lembersky

etal.[25]

Subg

roup

analysisin

phaseIIIRC

TUFT/LV

vs.b

olus

5-FU

/LV,

stageII/III

CC(NSA

BPC06),n

on-

inferio

ritystud

y

N=1551

(total)

N=939(≥60

years)

N=612(<60

years)

OS:OralU

FT/LVequivalent

tobo

lus5-FU

/LV;

HR:

1.010,

95%

CI:0

.822–1.242,p

=0.92

Forthoseaged

≥60

years,HR:

1.40,9

5%CI:1

.12–1.74,p

=0.03

(age

<60

yearsreferent)

DFS:o

ralU

FT/LVequivalent

tobo

lus5-FU

/LV;

HR:

1.005,

95%

CI:0

.848–1.192,p

=0.95

Forthoseaged

≥60

years,HR:

1.41,9

5%CI:1

.18–1.69,p

=0.002(age

<60

yearsreferent)

Jessup

,etal.[26]

Retrospectivecoho

rtstud

yof

patients

with

stageIII

CCusingNational

Cancer

Database

(US);todeterm

ine

prevalence

ofchem

otherapy

use

and5YS

N=85,934

(total)

Prevalence

ofadjuvant

chem

otherapy

use:

<60

years:82%,6

0–69

years:77.2%,7

0–79

years:69%,≥

80years:39.2%

5YSwas

similaracross

agegrou

psElderly

patientsderived

thesamebenefitsfrom

chem

otherapy,b

utwereless

likelyto

receivetreatm

ent

Gillet

al.[14]

Pooled

analysisof

data

from

sevenRC

Tsof

adjuvant

chem

otherapy

(5-FU

vs.surgery

alon

e),

stageII/III

CC;to

determ

inethe

benefit

(DFS

and

OS)

ofadjuvant

chem

otherapy

byage

N=3302

(total)

N=1438

(<60

years)

N=1864

(≥60

years)

DFS:5

-FUimproved

DFS,H

R:0.70,9

5%CI:0

.63–0.78

Inthoseaged

<60

years,5Y-DFS

69%

vs.5

6%,p

<0.001;

inthoseaged

≥60

years,5Y-DFS

63%

vs.5

5%,p

=0.001

OS:5-FU

improved

OS,HR:

0.74,9

5%CI:0

.66–0.83

Inthoseaged

<60

years,5YS74%

vs.6

7%,p

=0.0002;inthoseaged

≥60

years,5YS69%

vs.6

2%,p

=0.0005

Note:benefit

(5YR

OSandDFS)foradjuvant

chem

otherapy

across

agegrou

ps;age

prog

nosticforOS

Sund

ararajan

etal.

[4]

Retrospectivecoho

rtstud

yof

patients

aged

≥65

years,

stageIII

CC,u

sing

SEER/M

edicare

database

N=4768

OS:5-FU

improved

OS,HR:

0.66,9

5%CI:0

.6–0.73

Note:adjuvant

chem

otherapy

with

5-FU

improved

OSin

patients≥65

years

Sargentet

al.[23]

Pooled

analysisof

data

from

sevenRC

Tscomparin

g5-FU

tosurgeryalon

e,stage

II/III

CC,w

ithanalysisof

efficacy

andtoxicity

by10-

year

agegrou

ps

N=3351

(total)

N=564(≤50

years)

N=1012

(51-

60years)

N=1269

(61-

70years)

N=506(>70

years)

DFS:5

-FUimproved

DFS,H

R:0.68,9

5%CI:0

.60–0.76,p

<0.001

OS:5-FU

improved

OS,HR:

0.76,9

5%CI:0

.68–0.85,p

<0.001

Efficacy(OSandDFS)didno

tdiffe

racross

agegrou

ps;p

valueforinteractionforOSof

0.61

andTTRof

0.33

Toxicity:age

was

associated

with

high

erratesof

greaterthan

orequaltoG3leucop

enia

RCT:rand

omized

controlledtrial;XELO

X:capecitabine

+oxaliplatin

;FU/LV:

fluorou

racil/leucovorin

;CC:

coloncancer;N

R:no

trepo

rted;FOLFOX:

infusion

al5-FU

/LV+oxaliplatin

;OS:overallsurvival;DFS:d

isease-freesurvival;

TTR:

timeto

recurrence;R

DI:relativedo

seintensity;Q

OL:qu

ality

oflife;SEER:Surveillance,Epidemiology

andEndResults

Prog

ram;C

anCO

RS:C

ancerCare

Outcomes

Research

andSurveillanceCo

nsortiu

m;N

CCN:N

ational

Comprehensive

Cancer

Network;NYSCR

:New

York

StateCancer

Registry;U

FT/LV:oraltegafur-uracil/leucovorin;5YS:5-yearsurvival;H

R:hazard

ratio

;CI:confidence

interval;IV:intravenou

s;MOSA

IC:M

ulticenterInternational

Stud

yof

Oxaliplatin

/5-Fluorou

racil/Leucovorin

intheAd

juvant

Treatm

entof

ColonCancer;AE

:adverseevent;AC

CENT:

Adjuvant

ColonCancer

EndPoints;AV

ANT:

Bevacizumab

plus

oxaliplatin

-based

chem

otherapy

asadjuvant

treatm

entforcoloncancer;X

-ACT:X

elod

ain

Adjuvant

ColonCancer

TherapyTrial;NSA

BP:N

ationalS

urgicalA

djuvantBreast

andBo

wel

Project.

EXPERT REVIEW OF GASTROENTEROLOGY & HEPATOLOGY 1325

FLOX versus bolus 5-FU/LV. It should be noted that the FLOXregimen is not commonly used due to high rates of severe andfatal toxicities (grade 4 or 5 toxicity in all ages 11.8%, thoseaged ≥70 years 19.3%; rates of death in all ages 1.3%, andthose aged ≥70 years 3.6%). The addition of oxaliplatin sig-nificantly improved DFS but not OS although an analysis byage demonstrated a significant OS benefit in younger patientswhich is not seen in older patients (OS in those aged<70 years: HR: 0.80, 95% CI: 0.68–0.95, p = .013; ≥70 yearsHR: 1.18, 95% CI: 0.86–1.62, p = .3). The exploratory age sub-group analysis of the XELOXA (NO16968) trial of XELOX versusbolus 5-FU/LV [19] showed improved DFS and OS in patientsaged ≥70 years, albeit with a smaller effect size than inyounger patients (in those aged ≥70 years: HR for DFS: 0.86,95% CI: 0.64–1.16, HR for OS: 0.91, 95% CI: 0.66–1.20, p-valuesnot reported; in those aged <70 years: HR for DFS: 0.80, 95%CI: 0.67–0.94, and HR for OS: 0.82, 95% CI: 0.67–1.01, p-valuesnot reported). The comparatively better point estimates ofbenefit for oxaliplatin in the XELOXA trial were likely due tothe inclusion of patients with stage III disease only, whereasthe MOSAIQ and NSABP-C07 trials included patients withstage II disease where there was no benefit of oxaliplatin.

A pooled analysis of the three aforementioned oxaliplatintrials (MOSAIC, XELOXA, and NSABP-C07) [21] showed that theOS benefit of oxaliplatin was in patients aged <70 years (HRfor death: 0.83, 95% CI: 0.74–0.92), but not in those aged≥70 years (HR for death: 1.04, 95% CI: 0.85–1.27, p-value forage–treatment interaction .05). A pooled analysis by Halleret al. included more contemporary trials of FOLFOX orXELOX [22]. Data from four randomized trials (XELOXA,AVANT, X-ACT, and NSABP-C08) included more than 400patients aged ≥70 years in each treatment arm. The additionof oxaliplatin improved DFS and OS in patients aged <70 and≥70 years and across levels of comorbidity. The effect size wassmaller in older patients; for example, in patients aged<70 years, the HR for death was 0.62 (95% CI: 0.54–0.72,p < .0001) but for patients aged ≥70 years, the HR for deathwas 0.78 (95% CI: 0.61–0.99, p = .045). Rates of serious adverseevents were higher in those aged ≥70 years across all treat-ment cohorts [22].

‘Real-world data’ from a large population-based cohortstudy further complicate the evidence for the addition ofoxaliplatin in older adults. Sanoff et al. [2] evaluated fourdatabases (SEER-Medicare, National Comprehensive CancerNetwork, New York State Cancer Registry, and Cancer CareOutcomes Research and Surveillance Consortium) to deter-mine the effect of adjuvant chemotherapy on OS in 5489patients aged ≥75 years with stage III colon cancer. Adjuvantchemotherapy significantly improved OS over no treatment(e.g. in the largest SEER-Medicare database, HR for death: 0.60,95% CI: 0.53–0.68), but out of the three databases evaluablefor the added benefit of oxaliplatin, only the two largestdatabases demonstrated a trend towards improved OS(SEER-Medicare data HR for death: 0.85, 95% CI: 0.69–1.04;NYSCR HR for death: 0.82, 95% CI: 0.51–1.33; in contrast toNCCN HR for death: 1.84, 95% CI: 0.48–7.05) [2].

The presently recruiting Randomised Study EvaluatingAdjuvant Chemotherapy After Resection of Stage III ColonicAdenocarcinoma in Patients of 70 and Over (ADAGE) trial [29]

will add to the evidence for adjuvant chemotherapy in olderadults with colon cancer. It will be the first elderly-specificrandomized phase III trial evaluating the efficacy of adjuvantchemotherapy in those aged ≥70 years with stage III disease.Using a factorial design, this trial aims to determine whetherthere is a benefit to adjuvant chemotherapy over observationand whether the addition of oxaliplatin confers more benefitover 5-FU or capecitabine alone. Results are not expected until2025.

In summary, the available evidence best supports the useof 5-FU/LV or capecitabine as adjuvant chemotherapy optionsin older adults with resected stage III colon cancer. The addi-tional benefit of oxaliplatin in this population is unclear andshould only be considered for fitter older patients after a fulldiscussion about the incremental benefits and likely excesstoxicity with combination FOLFOX or XELOX.

3. Palliative chemotherapy considerations

Palliative chemotherapy and targeted therapy for advanced,incurable colon cancer improve DFS and OS over best suppor-tive care alone. Contemporary trials of combination first-linechemotherapy and targeted therapy report a median OS ofaround 30 months [30,31]. A variety of chemotherapy andtargeted therapy agents have activity in advanced colon can-cer and may be used in combination or as single agents. Thechoice of regimen is tailored to the individual patient anddepends on the goals and timing of treatment, toxicity ofthe individual regimens, and the molecular profiles of thecancer such as Ras status.

Commonly used first-line options for treatment includesingle-agent fluoropyrimidines [32] of infusional 5-FU/LVrather than bolus schedules [33,34] or oral capecitabine asan equivalent alternative [35] or combination treatment withFOLFOX [36,37], FOLFIRI (5-FU/LV + irinotecan) [38], CapeOx(capecitabine + oxaliplatin) [39], or FOLFOXIRI (5-FU/LV + oxaliplatin + irinotecan) [40,41]. Targeted agents can beused alone or in combination with chemotherapy and includebevacizumab [42], cetuximab, panitumumab [43], aflibercept[44], ramucirumab [45], and regorafenib [46]. Detailed discus-sion of the evidence supporting these regimens and theirvarious combinations and sequences is beyond the scope ofthis review.

3.1. The evidence for palliative chemotherapy in olderadults

Older adults have been traditionally underrepresented in trialsof palliative chemotherapy in colon cancer [47] with key trialsexcluding participants based on age alone [36–38]. Morerecently, however, there have been an increasing number ofelderly-specific prospective trials to better guide treatment deci-sions for older adults with advanced, incurable disease as perTable 2. Newer trials are using broader inclusion criteria to allowfor the enrollment of older adults who would not otherwise beconsidered for standard treatment [48–53], using modified dos-ing schedules to improve toxicity in older adults [48,54,55], andincorporating baseline geriatric assessments (GAs) to betteridentify older adults who are more (or less) likely to tolerate

1326 E. B. MOTH ET AL.

and benefit from treatment [54]. The choice of chemotherapyregimen in the palliative setting should pay particular attentionto the goals of care and consider the balance between treat-ment-related toxicities and quality of life.

3.2. Single-agent chemotherapy with 5-FU orcapecitabine

Single-agent first-line chemotherapy with 5-FU/LV or oralcapecitabine is a reasonable approach for older adults whoare not fit for combination therapy or who wish to avoidadditional toxicities that impair quality of life. The equivalentefficacy of 5-FU in older patients (≥70 years), compared withyounger patients, was demonstrated in a large retrospectivepooled analysis of data from 22 trials of 5-FU (infusional orbolus), where 629 of 3825 (16.4%) patients were aged≥70 years [33]. Response rate (RR), PFS, and OS were equiva-lent in older and younger patients, suggesting that patientswho fulfill traditional clinical trial criteria, regardless of age,have the same chance of benefiting from treatment. Toxicitydata were not reported in this study. Older age has beenreported as a risk factor for 5-FU toxicity [77], with older adultsexperiencing higher rates of leukopenia, diarrhea, and stoma-titis [23,73] although this is, in part, mitigated by the prefer-ential use of infusional over bolus 5-FU regimens [34,78].

Oral capecitabine is at least as efficacious as intravenous 5-FU in the palliative setting [35] with higher RR and equivalenttime to progression and OS [79–82]. It is generally well toler-ated in the elderly [53,56], with its most common severe(grade 3 or 4) side effects being hand–foot syndrome (up to21%) and diarrhea (up to 18%) [83]. The main advantage overintravenous 5-FU is its oral administration and convenience oftreatment although no differences were seen in a study com-paring QOL outcomes between administration schedules [52].The oral administration poses some risk if follow-up is not asfrequent, as the treatment requires self-monitoring and iden-tification of toxicities needing treatment interruption betweenscheduled visits, which may be difficult for older adults with,for example, limited social supports or impaired cognition.

3.3. Combination chemotherapy with 5-FU orcapecitabine and oxaliplatin or irinotecan

Subgroup and pooled analyses of key phase III trials suggestthat fit older adults who meet traditional clinical trial inclusioncriteria are likely to experience similar benefits of combinationoxaliplatin chemotherapy to younger patients in the first-linesetting [66,69–71]. The modest RR and PFS benefits from theaddition of oxaliplatin [36] must be balanced against toxicitiesattributable to oxaliplatin, including higher rates of neutrope-nia, nausea, and neuropathy [84]. Older adults also experiencehigher rates of gastrointestinal toxicity from oxaliplatin com-bination chemotherapy [66,69], with the rate of severe (grade3/4) diarrhea about 25% in those aged ≥70 years [66,72].

The benefit of oxaliplatin combination chemotherapy isquestionable in less fit older adults, as suggested by theFluorouracil, Oxaliplatin, CPT11 [irinotecan]: Use andSequencing trial [52]. This trial was designed for elderly orfrail patients considered unsuitable for standard dose

combination chemotherapy in the first-line setting andaddressed the questions of efficacy and safety of first-linecombination versus single-agent chemotherapy and oral ver-sus intravenous 5-FU-based chemotherapy. The median age ofthe 459 randomized patients was 74 years, and 29% had aperformance status of Eastern Cooperative Oncology Group(ECOG) 2. The addition of oxaliplatin resulted in a nonsignifi-cant improvement in PFS (HR: 0.84, 95% CI: 0.69–1.01, p = .07),and there was no difference in global QOL between thecapecitabine and FU/LV arms or OS across all four arms. Thehighest rates of severe (≥grade 3) toxicity occurred in theoxaliplatin/capecitabine arm (43%) and lowest in the 5-FUarm (27%). Capecitabine increased the rate of ≥grade 3 eventsand was associated with higher rates of nausea, vomiting,diarrhea, anorexia, and hand–foot syndrome. Oxaliplatin regi-mens were associated with higher rates of diarrhea, neurosen-sory toxicity, nausea, vomiting, and neutropenia.

Combination irinotecan and 5-FU/LV chemotherapy wasevaluated in a recent elderly-specific randomized phase IIItrial by Aparicio et al. [54]. Elderly patients (≥75 years) wererandomized to one of the two variations of 5-FU/LV adminis-tration (classic or simplified), with or without irinotecan, in thefirst-line setting using a 2 × 2 factorial design. In total, 282patients were randomized, with a median age of 80 years.Despite an improvement in RR with the addition of irinotecan(41.7% vs. 21.1%, p = .0003), there was no significant differ-ence in the primary outcome of PFS (HR: 0.84, 95% CI: 0.66–1.07, p = .15). Rates of any grade ≥3 toxicity were increasedwith combination therapy (76.3% vs. 52.2%), notably neutro-penia (38.5% vs. 5.2%) and diarrhea (22.2% vs. 5.2%). A sub-study of GA showed that severe chemotherapy toxicity waspredicted by impairments in cognition (by Mini–Mental StateExamination) and instrumental activities of daily living(IADL) [85].

Results of the Aparicio trial [54] are in contrast with thosefrom a pooled analysis of four phase III randomized trials of 5-FU ± irinotecan, which showed that patients aged ≥70 yearsobtained similar PFS benefits to younger patients (HR for PFSin those aged ≥70 years: 0.75, 95% CI: 0.61–0.90, p = .0026; forthose aged <70 years HR for PFS: 0.77, 95% CI: 0.70–0.85,p < .0001) [47]. This discrepancy is likely due to differencesin patient selection; patients aged >80 years, the median agein the Aparicio trial, made up <1% of those in the pooledanalysis, and patients included in the pooled analysis were ofbetter performance status (91% ECOG 0 to 1).

3.4. Bevacizumab in older adults

Bevacizumab is a humanized monoclonal antibody that inhi-bits vascular endothelial growth factor. Bevacizumab improvesPFS and OS when added to first- and second-line chemother-apy for metastatic colorectal cancer [86–88] and when contin-ued beyond disease progression [89]. The toxicities ofbevacizumab include arterial and venous thromboembolicevents, hemorrhage, hypertension, proteinuria, wound healingcomplications, and bowel perforation [90]. Bevacizumab isrecommended in addition to first-line combination che-motherapy for fit patients and in addition to 5-FU or capeci-tabine alone in those not fit for combination treatment [13].

EXPERT REVIEW OF GASTROENTEROLOGY & HEPATOLOGY 1327

Table2.

Elderly-specific

prospectivecoloncancer

trialsandlargesubg

roup

analyses

inthepalliativesetting.

Stud

yDesign

Participants

Results

andcomments

Steinet

al.

[56]

Non

-interventio

nal,ob

servationalstudy

ofpatientswith

mCR

Creceivingcapecitabine

aspartof

first-line

chem

otherapy,w

ithanalysisby

agegrou

pN=1249

(total)

N=>75

years

N=≤75

years

ORR:3

8%(≤75

years)vs.3

2%(>75

years)(p

=0.019)

PFS:9.7mon

ths(≤75

years)vs.8

.2mon

ths(>75

years)(p

=0.00021)

OS:31.0

mon

ths(≤75

years)vs.2

2.6mon

ths(>75

years)(p

<0.0001)

Toxicity:n

osign

ificant

diffe

rencein

toxicity

betweenagegrou

psNote:ORR,P

FS,and

OSdiffe

redbetweenagegrou

ps;o

lder

patientsless

likelyto

receivecombinatio

ntherapy

Aparicioet

al.

[54]

PhaseIII

RCT(2

×2factoriald

esign)

inpatientsaged

≥75

years,classicLV5FU2or

simplified

LV5FU2,

±irino

tecanfirstline(FFCD2001

to2002)

Primaryendpo

intPFS

Second

aryendpo

ints

RRandOS

N=282(total)

N=71

(LV5FU

2)N=71

(simplified

LV5FU2)

N=70

(LV5FU

-irino

tecan)

N=70

(FOLFIRI)

RR:improved

RRwith

irino

tecan

21.1%

vs.4

1.7%

,p=0.0003

PFS:Nodiffe

rencein

PFSwith

additio

nof

irino

tecan

PFS5.2mon

thsvs.7

.3mon

ths(HR:

0.84,9

5%CI:0

.66–1.07,p

=0.15)

OS:Nodiffe

rencein

OSwith

additio

nof

irino

tecan

14.2

mon

thsvs.1

3.3mon

ths(HR:

0.96,9

5%CI:0

.75–1.24,p

=0.77)

Toxicity:Increased

G3/G4toxicity

with

irino

tecan(76.3%

vs.5

2.2%

)Note:Theadditio

nof

irino

tecanto

5-FU

didno

timprovePFSor

OSin

patientsaged

≥75

years

Sastre

etal.

[51]

PhaseIIsing

le-arm

stud

y,panitumum

abfirstlineforthose

aged

≥70

yearswith

KRAS

WTmCR

C,frailo

run

suitableforchem

otherapy

N=33

(≥70

years)

PFSrate:3

6.4%

(95%

CI:2

0.0–52.8)

PFS:4.3mon

ths,95%

CI:2

.8–6.4

mon

ths

OS:7.1mon

ths,95%

CI:5

–12.3mon

ths

Toxicity:G

3rash

15.2%

Pietranton

ioet

al.[50]

PhaseIIsing

le-arm

stud

y,panitumum

abin

patientsaged

≥75

years

with

KRAS

WTmCR

C,firstor

second

line,thoseno

tsuitableforchem

otherapy

N=40

ORR:3

2.5%

PFS:6.4mon

ths,95%

CI:4

.9–8.0

mon

ths

OS:14.3

mon

ths,95%

CI:1

0.9–17.7

mon

ths

Toxicity:G

3rash

20%

Feliu

etal.

[57]

Sing

le-arm

phaseIIstud

yof

bevacizumab

+CA

POXin

patients

aged

≥70

yearswith

mCR

CN=68

ORR:4

6%TTP:

11.1

mon

ths,95%

CI:8

.1–14.1mon

ths

OS:20.4

mon

ths,95%

CI:1

3.2–27.6

mon

ths

Toxicity:G

3/G4diarrhea

18%,G

3/G4DVT

6%,G

3/G4PE

4%Hofheinz

etal.[58]

Observatio

nalcoh

ortstud

yof

patientsreceivingbevacizumab

+chem

otherapy

firstline

formCR

C,analysisby

age

N=1777

(total)

N=480(≥70

years)

N=213(≥75

years)

ORR:<

70vs.≥

70years:62%

vs.5

5%,p

=0.0046

PFS:<70

vs.≥

70years:10.5

vs.9

.5mon

ths,p=0.074

<75

vs.≥

75years:10.5

vs.8

.9mon

ths,p=0.00019

Dou

blet

vs.singleagentin

aged

≥70

years:9.7vs.9

.2mon

ths,p=0.52

OS:<70

vs.≥

70years:25.8

vs.2

2.7mon

ths,p<0.0008

<75

vs.≥

75years:25.8

vs.2

0.8mon

ths,p<0.0001

Note:PFSandOSwereshorterin

oldercomparedwith

youn

gerpatientsreceiving

bevacizumab

incombinatio

nwith

chem

otherapy

Cunn

ingh

amet

al.[48]

PhaseIIIRC

Tinpatientsaged

≥70

years,capecitabine

±bevacizumab

firstlineinmCR

Cin

thoseno

tfit

fordo

ubletchem

otherapy,p

rimaryendpo

intPFS

N=280(m

edianage

76years)

PFS:9.1vs.5

.1mon

ths,HR:

0.53,9

5%CI:0

.41–0.69,p

<0.0001

Toxicity:G

3–G5treatm

ent-relatedAE

rate:capecitabine

+bevacizumab

40%,

capecitabine

alon

e22%;G

3–G5events:h

and-foot

synd

rome(16%

vs.7

%),diarrhea

(7%

vs.7

%),andVTE(8%

vs.4

%)

Note:Bevacizumab

improves

PFSwhenaddedto

cetuximab

first-line

treatm

entin

patientsaged

≥70

years

Hurwitz

etal.

[59]

Pooled

analysisfrom

sevenRC

Tsof

chem

otherapy

±bevacizumab;todeterm

inethe

efficacyandsafety

ofbevacizumab,analysisby

age

N=3763

(total)

N=2269

(<65

years)

N=1492

(≥65

years)

N=426(≥75

years)

PFS:Ad

ditio

nof

bevacizumab

improved

PFS,HR:

0.57,9

5%CI:0

.46–0.71,p

<0.0001

Forthoseaged

<65

years,HR:

0.68,9

5%CI:0

.62–0.75,p

<0.0001

Forthoseaged

≥65

years,HR:

0.66,9

5%CI:0

.59–0.75,p

<0.0001

Forthoseaged

≥75

years,HR:

0.55,9

5%CI:0

.44–0.70,p

<0.0001

OS:Ad

ditio

nof

bevacizumab

improved

OS,HR:

0.80,9

5%CI:0

.71–0.90,p

=0.0003

Forthoseaged

<65

years,HR:

0.80,9

5%CI:0

.73–0.88,p

<0.0001

Forthoseaged

≥65

years,HR:

0.87,9

5%CI:0

.77–0.97,p

=0.0156

Forthoseaged

≥75

years,HR:

0.76,9

5%CI:0

.62–0.94,p

=0.0118

Note:Theadditio

nof

bevacizumab

tochem

otherapy

improved

OSandPFSacross

all

agegrou

ps

(Con

tinued)

1328 E. B. MOTH ET AL.

Table2.

(Con

tinued).

Stud

yDesign

Participants

Results

andcomments

Abdelwahab

etal.[60]

PhaseIIstud

yof

cetuximab

+irino

tecangreaterthan

orequaltosecond

linein

patients

aged

≥65

years

N=46

PFS:4mon

ths,95%

CI:3

5.6mon

ths

OS:7mon

ths,95%

CI:5

.9–8

mon

ths

Toxicity:G

3/G4rash

20%,G

3/G4diarrhea

18%

Jehn

etal.

[61]

Observatio

nalstudy

ofcetuximab

incombinatio

nwith

chem

otherapy

inpatientswith

pretreated

mCR

C,redu

cedperformance

status

andage>65

years

N=657(total)

N=309(≤65

years)

N=305(>65

years)

PFS:Nodiffe

rencein

PFSbetweenagegrou

ps,p

=0.12

Forthoseaged

≤65

vs.>

65years,6.6vs.7

mon

ths

Toxicity:N

odiffe

rencein

rate

ofG3/G4toxicitiesbetweenagegrou

ps,tho

ughmedian

duratio

nof

toxicitieslong

erforthoseaged

>65

years

Note:Thesafety

andefficacyprofile

ofcetuximab

was

similaracross

agegrou

ps(≤65

and>65

years)

Sastre

etal.

[55]

PhaseIIsing

le-arm

stud

y,first-line

cetuximab

+capecitabine

inpatientsaged

≥70

years

N=66

(≥70

years)

ORR:3

1.8%

,95%

CI:2

0.9–44.4

PFS:7.1mon

ths,95%

CI:5

.3–8.4

mon

ths

OS:16.1

mon

ths,95%

CI:1

2.0–18.8

mon

ths

Toxicity:highratesof

severe

paronychialedto

protocoldo

seredu

ctionof

capecitabine

from

1250

mg/m

2BD

to1000

mg/m

2BD

;G3/G4rash

28%,G

3/G4HFS

20%,G

3/G4

diarrhea

12%

Seym

our

etal.[52]

Elderly/frailspecific,ph

aseIII

RCT,first-line,2

×2factoriald

esign;

FU/LVor

capecitabine

±redu

ceddo

seoxaliplatin

(25%

DR),inthoseno

tfit

forfull-do

sechem

otherapy

(citedas

dueto

frailty

in71%,age

in68%)

Primaryendpo

int:PFSforadditio

nof

oxaliplatin

;chang

einglob

alQOLforsub

stitu

tion

ofcapecitabine

forFU

/LV

N=459

PFS:Ad

ditio

nof

oxaliplatin

:HR:

0.84,9

5%CI:0

.69–1.01,p

=0.07

Capecitabine

vs.FU:H

R:0.99,9

5%CI:0

.82–1.20,p

=0.93

OS:Ad

ditio

nof

oxaliplatin

:HR:

0.99,9

5%CI:0

.81–1.18,p

=0.91

Capecitabine

vs.FU:H

R:0.96,9

5%CI:0

.79–1.17,p

=0.71

QOL:Nodiffe

rencein

glob

alQOLbetweenFU

andcapecitabine

arms

Toxicity:H

ighestratesof

greaterthan

orequaltoG3toxicity

with

OxCap

(43%

),lowest

with

FU(27%

);no

increase

intoxicity

with

additio

nof

oxaliplatin

;increased

riskof

greaterthan

orequaltoG3toxicity

with

capecitabine

vs.FU

Note:Theadditio

nof

oxaliplatin

to5-FU

orcapecitabine

didno

timprovePFSinelderly

frailp

atients;substitutionof

5-FU

with

capecitabine

didno

timproveQOL.

Priceet

al.

[62]

Subg

roup

analysisof

patientsaged

≥75

yearsin

RCTof

capecitabine

vs.b

evacizum

ab/

capecitabine

vs.b

evacizum

ab/capecitabine/m

itomycin

C(AGITGMAX

stud

y)N=99

(≥75

years)

Foradditio

nof

bevacizumab

(CB/CB

Mvs.C

)PFS:In

thoseaged

<75

years,HR:

0.65,9

5%CI:0

.52–0.82,p

<0.001

Inthoseaged

≥75

years,HR:

0.45,9

5%CI:0

.29–0.69,p

<0.001;

p-interaction=0.19

OS:In

thoseaged

<75

years,HR:

0.99,9

5%CI:0

.77–1.27,p

=0.94

Inthose≥aged

75years,HR:

0.79,9

5%CI:0

.50–1.24,p

=0.31;p

-interaction=0.29

Note:Ad

ditio

nof

bevacizumab

tochem

otherapy

improved

PFSin

youn

gerandolder

agegrou

psCassidyet

al.

[63]

Retrospectivepo

oled

analysisof

olderpatientsenrolledin

four

phaseII/III

RCTs

ofbevacizumab

plus

chem

otherapy,todeterm

ineefficacyandtoxicity

ofbevacizumab

addedto

chem

otherapy

inolderpatients

N=3007

(total)

N=1864

(<65

years)

N=1142

(≥65

years)

N=712(≥70

years)

PFS:<65

years,PFS:9.5vs.6

.7mon

ths,HR:

0.59

(95%

CI:0

.52–0.66,p

<0.0001)

≥65

years,PFS:9.3vs.6

.9mon

ths,HR:

0.58

(95%

CI:0

.49–0.68,p

<0.0001)

≥70

years,PFS:9.2vs.6

.4mon

ths,HR:

0.54

(95%

CI:0

.44–0.66,p

<0.0001)

OS:<65

years,OS:19.9

vs.1

6.5mon

ths,HR:

0.77

(95%

CI:0

.69–0.86,p

<0.0001)

≥65

years,OS:17.9

vs.1

5.0mon

ths,HR:

0.85

(95%

CI:0

.74–0.97,p

=0.015)

≥70

years,OS:17.4

vs.1

4.1mon

ths,HR:

0.79

(95%

CI:0

.66–0.93,p

=0.005)

Toxicity:Increased

thrombo

embo

liceventswith

bevacizumab

(arterial)inpatientsaged

≥65

and≥70

years

Note:Thebenefit

ofbevacizumab

tochem

otherapy

inOSandPFSwas

seen

across

all

agegrou

ps,w

ithincreasedarterialthrom

boem

bolic

eventsin

olderpatients.

Kozloffet

al.

[64]

Observatio

nalstudy

subg

roup

analysisof

olderpatientstreatedwith

bevacizumab-based

treatm

entfirst-line

(BRiTE

stud

y)to

determ

inesafety,PFS,O

S,andSBP,as

assessed

byagegrou

p

N=1953

(total)

N=896(≥65

years)

PFS:Ag

e<65

years9.8mon

ths,95%

CI:9

.2–10.3mon

ths

Age65–74years9.6mon

ths,95%

CI:9

.0–10.2mon

ths

Age75–79years10

mon

ths,95%

CI:8

.5–10.5mon

ths

Age≥80

years8.6mon

ths,95%

CI:7

.5–9.9

mon

ths

OS:Ag

e<65

years26

mon

ths,95%

CI:2

4.5–27.6

mon

ths

Age65–74years21.1

mon

ths,95%

CI:1

8.6–23.9

mon

ths

Age75–79years20.3

mon

ths,95%

CI:1

6.8–22.5

mon

ths

Age≥80

years16.2

mon

ths,95%

CI:1

3.4–20.4

mon

ths

Safety:Increased

arterialthrom

boem

bolic

eventswith

age

(Con

tinued)

EXPERT REVIEW OF GASTROENTEROLOGY & HEPATOLOGY 1329

Table2.

(Con

tinued).

Stud

yDesign

Participants

Results

andcomments

Feliu

etal.

[49]

Sing

le-arm

phaseIItrialo

fcapecitabine

+bevacizumab

inpatientsaged

≥70

years

considered

unsuitableforoxaliplatin

oririno

tecando

ublet

N=59

(≥70

years)

RR:3

4%(95%

CI:2

2.4–47.5

mon

ths)

PFS:10.8

mon

ths(95%

CI:7

.6–14.1mon

ths)

OS:18

mon

ths(95%

CI:9

.6–26.3mon

ths)

Toxicity:G

3/G4toxicity

in54%;G

3/G4PPE19%;G

3/G4diarrhea

9%VanCu

tsem

etal.[65]

Expand

edaccess

trialo

fbevacizumab

addedto

chem

otherapy

first

lineformCR

C(the

BEAT

stud

y)N=1914

(total)

N=1286

(<65

years)

N=499(65–74

years)

N=129(≥75

years)

Toxicity:Similartoxicity

across

agegrou

psPFS:<65

yearsvs.6

5–74

yearsvs.≥

75years:10.8

vs.1

1.2vs.1

0.0mon

ths,p=NR

OS:<65

yearsvs.6

5–74

yearsvs.≥

75years:23.5

vs.2

2.8vs.1

6.6mon

ths,p=NR

Sastre

etal.

[66]

Subg

roup

analysisby

ageof

phaseIII

RCTof

continuo

usinfusion

al5-FU

+oxaliplatin

(FUOX)

vs.X

ELOXfirst

line

N=348(total)

N=109(≥70

years)

N=233(<70

years)

RR:D

idno

tdiffe

rbetweenagegrou

ps(p

=0.081)

≥70

yearsvs.<

70years,34.9%

vs.4

4.7%

TTP:

Did

notdiffe

rbetweenagegrou

ps(p

=0.114)

≥70

vs.<

70years,8.3vs.9

.6mon

ths

OS:Did

notdiffe

rbetweenagegrou

ps(p

=0.74)

≥70

vs.<

70years,16.8

vs.2

0.5mon

ths

Toxicity:G

3/G4diarrhea

high

erin

aged

≥70

yearsreceivingXELO

X(25.0%

vs.8

.1%,

p=0.005);h

igherratesof

treatm

entdiscon

tinuatio

ndu

eto

toxicity

inolderpatients

(37%

vs.2

1%)

Kabb

inavar

etal.[67]

Pooled

analysisfrom

twoRC

Tsof

patientsaged

≥65

yearsreceivingfirst-line

chem

otherapy

±bevacizumab

N=439(≥65

years)

ORR:3

4.4%

vs.2

9%(p

=NS)

PFS:9.2vs.6

.2mon

ths,HR:

0.52,9

5%CI:0

.40–0.67,p

<0.001

OS:19.3

vs.1

4.3mon

ths,HR:

0.70,9

5%CI:0

.55–0.90,p

=0.006

Note:Patientsaged

≥65

yearsbenefit

from

theadditio

nof

bevacizumab

tochem

otherapy

first

line

Folprecht

etal.[47]

Pooled

analysisfrom

four

phaseIII

RCTs

ofFU

/FA±irino

tecanas

first-line

treatm

entin

mCR

CN=2092

(total)

N=599(≥70

years)

RR:For

thoseaged

≥70

years:50.5%

vs.3

0.3%

(p<0.0001)

Forthoseaged

<70

years:46.5%

vs.2

9%(p

<0.0001)

PFS:Forthoseaged

≥70

years:HR:

0.75,9

5%CI:0

.61–0.90,p

=0.0026

Forthoseaged

<70

years:HR:

0.77,9

5%CI:0

.70–0.85,p

<0.0001

OS:Forthoseaged

≥70

years:HR:

0.87,9

5%CI:0

.72–1.05,p

=0.15

Forthoseaged

<70

years:HR:

0.83,9

5%CI:0

.75–0.92,p

=0.0003

Toxicity:Similaracross

agegrou

ps;significantly

moreneutropenia,leucop

enia,

diarrhea,n

ausea,andvomiting

with

irino

tecanin

both

agegrou

psFrançoiset

al.

[68]

Sing

le-arm

phaseIItrialo

fFO

LFIRIa

sfirst-line

treatm

entin

thoseaged

≥70

years

N=40

(≥70

years)

RR:4

0%(95%

CI:2

5–55)

PFS:8mon

ths(95%

CI:6

toun

reached)

OS:17.2

mon

ths(95%

CI:1

1.6–22.2

mon

ths)

Toxicity:G

3/G4diarrhea

15%

Arkenau

etal.[69]

Subg

roup

analysisof

phaseIII

RCTof

FUFO

Xvs.C

APOXfirstline

N=476(total)

N=140(≥70

years)

RR:A

ge≥70

vs.<

70years:49%

vs.5

2%(p

=NS)

PFS:Ag

e≥70

vs.<

70years:7.7vs.7

.5mon

ths(p

=NS)

OS:Ag

e≥70

vs.<

70years:18.8

vs.1

4.4mon

ths(p

=0.013)

Toxicity:G

reater

gastrointestinalside

effectsin

thoseaged

≥70

years

Figeret

al.

[70]

Subg

roup

analysisof

patientsaged

76–80yearsenrolledin

OPTIMOX1

trialo

ffirst-line

continuo

usFO

LFOXvs.sixcycles

followed

bymaintenance

5-FU

N=620(total)

N=37

(76–80

years)

ORR:A

ge<76

vs.7

6–80

years:59%

vs.5

9.4%

(p=NS)

PFS:Ag

e<76

vs.7

6–80

years:9vs.9

mon

ths(p

=0.63)

OS:Ag

e<76

vs.7

6–80

years:20.2

vs.2

0.7mon

ths(p

=0.57)

Toxicity:m

orethan

G3/G4toxicity

inolderpatients(65%

vs.4

8%);neutropenia(41%

vs.2

4%);andneurotoxicity

(22%

vs.1

1%)

Goldb

erg

etal.[71]

Pooled

analysisfrom

four

RCTs

ofFO

LFOXin

adjuvant,firstandsecond

line

N=3742

N=614(≥70

years)

Safety:≥

G3hematolog

icaltoxicity

morecommon

inthoseaged

≥70

years;no

diffe

rence

inGIo

rneurotoxicity

betweenagegrou

psOS:Forthoseaged

≥70

years(+oxaliplatin

),HR:

0.82,9

5%CI:0

.63–1.06

Forthoseaged

<70

years(+oxaliplatin

),HR:

0.77,9

5%CI:0

.67–0.88,p

interaction=0.79

Feliu

etal.

[72]

Sing

le-arm

phaseIIstud

yof

XELO

Xfirstlinein

patientsaged

≥70

yearswith

mCR

CN=50

(≥70

years)

RR:3

6%(95%

CI:2

8–49)

TTP:

5.8mon

ths(95%

CI:3

.9–7.8

mon

ths)

OS:13.2

mon

ths(95%

CI:7

.6–16.9mon

ths)

Toxicity:G

3/G4diarrhea

in22%

(Con

tinued)

1330 E. B. MOTH ET AL.

Table2.

(Con

tinued).

Stud

yDesign

Participants

Results

andcomments

Feliu

etal.

[53]

Sing

le-arm

phaseIIstud

yof

capecitabine

inpatientsaged

≥70

years,mCR

C,inapprop

riate

forcombinatio

nchem

otherapy

N=51

RR:2

4%(95%

CI:1

5–41%);clinicalbenefit

rate

of40%

(of35

patients)

OS:Median11

mon

ths(95%

CI:8

.6–13.3mon

ths)

PFS:Median7mon

ths(95%

CI:6

.4–9.5

mon

ths)

Toxicity:O

nlysixpatients(12%

)hadaG3to

G4toxicity

D’And

reet

al.

[73]

Pooled

analysisfrom

four

North

CentralC

ancerTreatm

entGroup

trialsof

5-FU

±leucovorin

foradvanced

CRC

N=371(≤55

years)

N=450(56–65

years)

N=354(66–70

years)

N=483(>70

years)

RR:D

idno

tdiffe

rby

age(p

=0.90)

TTP:

Did

notdiffe

rby

age(p

=0.25)

OS:Did

notdiffe

rby

age(p

=0.42)

Toxicity:Severetoxicity

46%

vs.5

3%(>65

vs.≤

65years,p=0.01);diarrhea

16%

vs.

21%,stomatitis13%

vs.1

7%,infectio

n2%

vs.4

%Co

mellaet

al.

[74]

Sing

le-arm

phaseIIstud

yof

XELO

Xfirstlinein

patientsaged

≥70

years

N=76

(≥70

years)

RR:4

1%(95%

CI:3

0–53%)

PFS:8.5mon

ths(95%

CI:6

.7–10.3mon

ths)

OS:14.4

mon

ths(95%

CI:1

1.9–16.9

mon

ths)

Toxicity:G

3/G4PPE13%

Soug

lakos

etal.[75]

Sing

le-arm

,phase

IIstud

yof

FOLFIRIfirstlinein

patientsaged

≥70

years

N=30

(≥70

years)

RR:3

6.5%

(95%

CI:2

6.6–48.4%)

TTP:

7mon

ths

OS:14.5

mon

ths

Toxicity:G

3/G4neutropenia20%;G

3/G4diarrhea

17%

Sastre

etal.

[76]

Sing

le-arm

phaseIItrialo

firin

otecan

+5-FU

firstlinein

patientsaged

≥72

years,of

good

performance

status

andwith

outgeriatricsynd

romes

N=85

(≥72

years)

RR:3

5%(95%

CI:2

5–46%)

TTP:

8mon

ths(95%

CI:6

.0–10.0mon

ths)

OS:15.3

mon

ths(95%

CI:1

3.8–16.9

mon

ths)

Toxicity:G

3/G4neutropenia21%;G

3/G4diarrhea

17%

Folprecht

etal.[33]

Retrospectivepo

oled

analysisof

data

from

22palliativechem

otherapy

trialsusing5-FU

-basedchem

otherapy

N=3825

N=629(≥70

years)

OS:Nodiffe

rencebetweenagegrou

ps(p

=0.31);≥70

vs.<

70years,10.8

vs.1

1.3mths

PFS:Better

PFSin

olderpatients(p

=0.01);≥70

yearsvs.<

70years,5.5vs.5.3mon

ths

RR:Equ

ivalentacross

agegrou

ps(≥70

years23.9%,2

1.1%

<70

years)

mCR

C:metastatic

colorectalcancer;O

RR:o

verallrespon

serate;PFS:p

rogression

-freesurvival;O

S:overallsurvival;RR:respo

nserate;H

FS:h

and–

foot

synd

rome;BR

iTE:bevacizumab

regimens:investigationof

treatm

enteffect

andsafety;S

BP:survival

beyond

prog

ression;

TTP:

timeto

prog

ression;

FOLFOX:

5-FU

/LV+oxaliplatin

;XELOX:

capecitabine

+oxaliplatin

;FUFO

X:5-FU

/LV+oxaliplatin

;CAP

OX:

capecitabine

+oxaliplatin

;FOLFIRI:5-FU

/LV

+irino

tecan;NR:no

trepo

rted;RCT:rando

mized

controlledtrial;QOL:qu

ality

oflife;CR

C:colorectalcancer;H

R:hazard

ratio

;CI:confidence

interval;FU:fluorou

racil;LV:leucovorin

;LV5FU

2:leucovorin/5-fluorou

racil;FFCD

:French

Francoph

onede

Cancérolog

ieDigestive;KR

as:K

irsten-ras;WT:wild

type;D

R:do

seredu

ction;

AGITGMAX

:AustralasianGastro-IntestinalTrialsGroup

Mito

mycin,A

vastin,X

elod

aStud

y;BEAT

:Bevacizum

abExpand

edAccess

Trial;OPTIMOX1:Optimized

Leucovorin

[LV]-Fluorou

racil[FU]-Oxaliplatin

1;DVT:deep

veno

usthrombo

sis;

PE:pu

lmon

aryem

bolus;

AE:adverseevent;VTE:

veno

usthrombo

embo

lism;BD

:twicedaily;OxCap:

oxaliplatin

/capecitabine;C

B/CB

M:P

PE:p

almar-plantar

erythrod

ysaesthesia;NS:no

tsign

ificant;G

I:gastrointestinal.

EXPERT REVIEW OF GASTROENTEROLOGY & HEPATOLOGY 1331

Evidence for the efficacy of bevacizumab in older adultsfirst came from pooled analyses of randomized trials[59,63,67] and large observational cohort studies [58,64,65].The largest pooled analysis was by Hurwitz et al. [59] andincluded data from 3763 patients (1492 ≥65 years) enrolledin seven randomized trials of chemotherapy ± bevacizumabin the first- or second-line setting with a subgroup analysisby age (<65, ≥65, and ≥75 years). The addition of bevacizu-mab improved both PFS and OS across all age subgroupswith a similar effect size. Toxicity was not analyzed by age,though the bevacizumab group had higher rates of severeproteinuria (1.7% vs. 0.2%), hypertension (7.7% vs. 1.6%),bleeding (4.0% vs. 1.9%), arterial thromboembolic events(3.3% vs. 1.6%), and any grade gastrointestinal perforation(2.2% vs. 0.7%). An earlier pooled analysis of four of theseven included trials [63] showed patients aged ≥65 yearshad significantly higher rates of thromboembolic (particu-larly arterial) events.

Three large observational cohort studies, one from the bev-acizumab regimens: investigation of treatment effect and safety(BriTE) registry of 1953 patients (896 ≥65 years) receiving first-line bevacizumab [64], another of 1914 patients (628 ≥65 years)treated on an expanded access program of first-line bevacizu-mab added to physician’s choice chemotherapy [65], and aGerman study of 1777 patients (206 ≥75 years) receiving first-line bevacizumab-based therapy [58], provide further supportfor the benefit of bevacizumab in older adults. In the BriTEregistry [64], PFS was similar across age groups (<65, 65–74,75–79, and ≥80 years); however, median OS declined with age(median OS 26 months for those aged <65 years and16.2 months for those aged ≥80 years), with age being a sig-nificant predictor of survival even after adjustment for baselinecovariates. Age was also a predictor for arterial thromboembolicevents (adjusted incidence rate ratio of 2.01 for patients aged75–79 years and 1.67 for patients aged ≥80 years compared withpatients aged <65 years). In the expanded access program [65],PFS and OS in patients treated with bevacizumab did not sig-nificantly differ across age groups (<65, 65–74, and ≥75 years). Incontrast, the German observational study [58] found both PFSand OS to be significantly shorter in patients aged ≥70 yearstreated with bevacizumab plus chemotherapy compared tothose aged <70 years, possibly explained by less intensive che-motherapy backbones received by older patients in this study,and competing causes of mortality.

Recognizing that not all elderly patients were suitable toreceive standard combination chemotherapy as a backboneto bevacizumab, Cunningham et al. [48] designed the inno-vative AVastin in the Elderly with Xeloda trial, an elderly-specific randomized phase III trial evaluating the efficacy ofbevacizumab when added to first-line capecitabine forpatients aged ≥70 years with Eastern CooperativeOncology Group Performance Status ≤2. This trial specifi-cally included patients deemed by investigators to be unsui-table for first-line oxaliplatin or irinotecan-basedcombination chemotherapy. The median age of the 280randomized patients was 76 years. The addition of bevaci-zumab to capecitabine was found to improve PFS (9.1 ver-sus 5.1 months; HR: 0.53; 95% CI: 0.41–0.69; p < .0001), with

a nonsignificant trend towards improved OS. The rate ofgrade 3–5 treatment-related adverse events was 40% inthe combination arm compared to 22% with capecitabinealone, with rates of grade ≥3 venous thromboembolism of8% versus 4%, hand–foot syndrome 16% versus 7%, and anygrade hemorrhage 25% versus 7%. The results of this trialwere consistent with prior single-arm phase II studies thatdemonstrated acceptable safety and efficacy of bevacizu-mab in addition to capecitabine in older patients [49,57].This has been adopted by clinicians as an acceptable first-line regimen for older adults.

3.5. Antibodies against the epidermal growth factorreceptor

Cetuximab and panitumumab are monoclonal antibodies direc-ted against the epidermal growth factor receptor (EGFR). Theirbenefit in the first- and second-line settings in combination withchemotherapy and in the third-line setting as monotherapy forpatients with RAS wild-type advanced colorectal cancer wasconfirmed in a meta-analysis of 14 randomized trials (HR forPFS in first and second line combined = 0.83, 95% CI: 0.76–0.90, p < .0001) [43]. The main toxicity concerns with theseagents are fatigue, skin rash, and electrolyte abnormalities.

Retrospective [61,91] and small phase II studies [50,51,55] ofanti-EGFR therapy in older adults show that they derive similarbenefits to younger adults, with no indication of greater toxi-city. A large German observational study of 657 patients receiv-ing cetuximab in combination with irinotecan for pretreatedmetastatic colorectal cancer found no difference in RR or PFSbetween those aged <65 years and those aged ≥65 years. Therate of grade 3/4 toxicities did not differ significantly betweengroups, despite older patients in the study having more comor-bidities [61]. Acceptable toxicity profiles in older adults receiv-ing cetuximab or panitumumab have also been demonstratedin a number of single-arm phase II studies [50,51,55], even instudy populations deemed ‘unsuitable’ for chemotherapy[50,51].

3.6. Regorafenib

Regorafenib is an oral multi-kinase inhibitor with superior effi-cacy to placebo in patients with chemotherapy-refractory meta-static disease. In the Regorafenib monotherapy for previouslytreated metastatic colorectal cancer (CORRECT) trial [46], regor-afenib improved median OS by a modest 1.4 months (HR: 0.77,95% CI: 0.64–0.94, p = 0.0052); however, 67% of patients in theregorafenib arm required dose modification due to toxicity.Toxicities included any-grade fatigue in 47%, any-grade diar-rhea in 34%, and grade ≥3 hand–foot skin reactions in 17%.Two separate subgroup analyses by age, one from theCORRECT trial [92] and another from the Regorafenib inSubjects With Metastatic Colorectal Cancer (CRC) Who HaveProgressed After Standard Therapy phase IIIb continued accesstrial [93], have shown that the efficacy and tolerability of regor-afenib do not differ significantly by age.

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4. The assessment of older adults with cancer forchemotherapy

The clinical assessment of older adults for chemotherapy shoulddetect patient factors that may impact treatment tolerability andoutcomes and include assessment of geriatric domains such asfunctional status, social supports, comorbidities, nutrition,mood, medications, and cognition. Treating physicians shouldalso make an assessment of a patient’s preference for the treat-ment being considered and how much they wish to be involvedin the treatment decision to foster shared decision-making,where desired, and patient-centered care [94].

4.1. Broadening the concept of ‘fitness forchemotherapy’

Patients included in clinical trials are generally younger andfitter than patients seen in routine clinical practice, whichmakes translation of clinical trial results to patients whowould not meet traditional trial inclusion criteria difficult. Astrong emphasis is often placed on chronological age andperformance status when selecting older adults who are ‘fitfor chemotherapy.’

Population studies addressing receipt of chemotherapy forcolon cancer in both the adjuvant [2–4,26,95,96] and thepalliative settings [5] have revealed that increasing age isassociated with a decline in receipt of chemotherapy, evenwhen adjusted for comorbidity [95,97]. Additionally, studiesexploring chemotherapy decision-making by oncologists inthe adjuvant setting have revealed that age is an importantdriver of treatment choice [98–102]. Chronological age doesnot correlate well with physiological age [103] and should notbe used in isolation to assess a patient’s fitness for chemother-apy. Problems with the use of performance status, such as theECOG score [104], are that it is only a crude measure of apatient’s functional status and do not capture more subtlechanges in physical function, comorbidities, nutrition, socialsupports, cognition, or the presence of geriatric syndromes, allof which are relevant to treatment decision-making for theolder adult [105]. It is widely accepted that patients with apoor performance status (ECOG 3 or 4) have a worse prognosis[106], may tolerate treatment poorly [107], and generallyshould not have chemotherapy.

Methods to improve the assessment and selection of olderadults for chemotherapy include the use of GAs, geriatricscreening tools, and risk-predicting tools. A comprehensivegeriatric assessment (CGA) is a formal assessment of keyhealth domains in an older adult. It is commonly used ingeriatric medicine and usually performed by a multidisciplin-ary team. Given that it is resource intensive, abbreviatedforms, more simply called GAs, are feasible alternatives foruse in oncologic practice [108,109]. Alternatives to a GA arebrief screening tools that identify vulnerable older adults whomay benefit from a full CGA, such as the G8-Questionnaire orVulnerable Elders Survey-13 [110].

Themost consistently identified benefit of a GA in oncology isin identifying the presence of geriatric problems that would nototherwise have been detected and which may benefit fromsupportive interventions or further evaluation [111–113]. For

example, in a large prospective study of 1967 patients aged≥70 years for whom treatment with chemotherapy was beingconsidered, 71% were considered to be ‘at risk’ by use of ascreening tool (G8-questionnaire) of whom 51% had geriatricproblems found on CGA [114]. This was despite 70% of patientsbeing rated as good performance status (ECOG 0/1). Otherpotential benefits of a GA are its ability to predict for treatmentoutcomes such as all-cause mortality and toxicity although theavailable data are inconsistent among studies [111,115].Impaired IADLs, poor performance status, and number of GAdeficits have been found to be the most consistent predictorsfrom a GA for mortality [116]. There are no consistent individualpredictors from a GA for toxicity [116,117].

Despite the uncertainty as to how best to use the GA toinform treatment decision-making, it can positively impact onclinical care. For example, a systematic review by Hamakeret al. [105] included six studies assessing the impact of a GAon treatment decisions and found that interventions (oncolo-gical and non-oncological) were recommended for the major-ity of patients who had a GA. The initial chemotherapytreatment plan was modified in a median of 39% of patients,often to a less intensive treatment. A prospective study of 375patients with cancer found that impaired activities of dailyliving and malnutrition were factors independently associatedwith a change in treatment [118].

Due to the valuable information gained from a more holisticassessment of the older adult with cancer, incorporation of a GAcovering key health domains is now recommended as part ofroutine oncological practice [112]. The next step is to evaluatethe value of interventions performed on the basis of a GA, withearly studies suggesting that patients who receive GA-promptedinterventions are more likely to complete chemotherapy asplanned [119]. This is the primary outcome being assessed inthe presently recruiting Effect of Geriatric Intervention in FrailElderly Patients Receiving Chemotherapy for Colorectal Cancertrial [120], a randomized GA intervention trial specifically for frailolder adults receiving chemotherapy for colon cancer. Furtherintervention trials in mixed tumor populations are also recruit-ing [121].

4.2. Tools to aid in predicting chemotherapy toxicity

There has been a recent focus on the development of geria-tric-specific prediction models to estimate the risk of severechemotherapy-related toxicity. The Chemotherapy RiskAssessment Scale for High-Age Patients (CRASH) score [122]uses treatment and patient-related factors to estimate thelikelihood of severe chemotherapy-related toxicity. Two sepa-rate predictive models were developed, one for hematologicaland one for non-hematological toxicity. The Cancer and AgingResearch Group’s (CARG) toxicity score [123] was developed ina cohort of 500 patients aged ≥65 years commencing che-motherapy for any tumor type or stage. An 11-item predictivemodel uses cancer and treatment variables, laboratory values,and GA variables to classify patients as low, intermediate, orhigh risk of experiencing a grade 3–5 treatment-related toxi-city over the course of treatment. The CARG score has recentlybeen validated in an external cohort [124] and in a non-small-cell lung cancer population [125]. There are no data about the

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impact of the CRASH or CARG scores on treatment decisions,but they may be useful in identifying older adults at particularrisk for treatment toxicity for whom supportive measures ordose modifications may be considered.

4.3. Other tools to guide chemotherapy decision-making

There are an increasing number of other tools that may assistin treatment decision-making and the assessment of olderadults. Mortality risk calculators such as ePrognosis [126]have been widely used in general geriatric medicine andmay be useful in decision-making about adjuvant chemother-apy where consideration of competing causes of mortality isparamount.

5. Making a decision about treatment

Decisions about chemotherapy are a complex interplaybetween patient and clinician factors (Figure 1).Considerations include the goals of treatment, the baselineprognosis of the patient’s cancer, the likelihood of benefitsand harms, and how patients trade off these benefits andharms, otherwise known as their preferences for thetreatment.

5.1. Balancing the benefits and harms of adjuvantchemotherapy

The goal of adjuvant chemotherapy is cure. The benefits ofadjuvant chemotherapy are usually a reduction in the risk ofcancer recurrence and improved OS and, as such, are intangi-ble and only realized in time. The harms of adjuvant che-motherapy, however, are real, experienced by most patients,and carry the risk of long-term side effects such as peripheralneuropathy that impair quality of life in patients who areotherwise cured of cancer.

For older adults having adjuvant chemotherapy, competingcauses of mortality may diminish the survival benefits of

treatment, and physiological and functional impairmentsmay increase the risk of harm. Hence, the balance betweentreatment benefits and harms is more finely balanced than inyounger patients who have more to gain from long-termsurvival benefits and generally tolerate treatment better.Clinicians should be mindful, however, not to underestimatethe life expectancy of an older adult based on chronologicalage alone, as an 80-year-old woman in excellent health maybe expected to live up to a further 13 years, compared to a 75-year-old in relatively poor health, who may be expected to liveanother 7 years [127].

With regard to adjuvant chemotherapy in older adults withcolon cancer, the discussed evidence supports the use ofsingle-agent 5-FU/LV or capecitabine, but less so oxaliplatincombinations in patients aged ≥70 years. The benefits ofadjuvant 5-FU/LV or capecitabine chemotherapy for stage IIIcolon cancer are a 30% relative reduction in the risk of recur-rence and a 26% relative reduction in the risk of death at5 years. The additional benefit of oxaliplatin is uncertain,possibly decreases with increasing age, and occurs at thecost of more frequent and severe toxicity. The harms of adju-vant chemotherapy include the toxicities and inconveniencesof the treatment. Older adults who have adjuvant chemother-apy for colon cancer outside of clinical trials have reduceddose intensities due to early cessation and dose modificationsfor toxicity [6,96,128,129]. Even highly selected older adultstreated within the trial setting experience higher rates oftreatment toxicity and early treatment discontinuation[15,18,22], with advanced age an identified predictor of earlymortality [20].

The risks and benefits of adjuvant chemotherapy also needto be weighed against the risk and consequences of recurrentdisease. Stage III colon cancer has a relatively poor baselineprognosis with overall recurrence rates about 50% at 5 yearsand 5-year OS rates of 40–80% [130] without adjuvant treat-ment. Careful discussion of the possible outcomes of all treat-ment options, including observation, should be part of thedecision-making process.

Figure 1. Key factors influencing systemic treatment decisions for older adults with colon cancer.

1334 E. B. MOTH ET AL.

5.2. Balancing the benefits and harms of palliativechemotherapy

The goals of palliative chemotherapy are to prolong survival(PFS and OS) and improve disease-related symptoms andHRQOL, but generally not to cure the cancer. The proviso onthe palliative intent of treatment is the small subset of patientswith metastatic colorectal cancer that have resectable diseasefor whom chemotherapy is given along with surgery for cure.

The balance between the benefits and harms of palliativechemotherapy differs to the adjuvant setting because patientswith advanced, incurable cancer will have shorter survivalwithout chemotherapy and usually have cancer-related symp-toms which may improve with treatment. Chronic toxicitiesare also less of a concern. The balance for palliative che-motherapy is also dynamic and changes over time and isgenerally characterized by more intensive chemotherapywith bigger survival benefits when a patient is first diagnosedand moves towards less intensive chemotherapy and themaintenance of HRQOL towards their end-of-life.

As discussed, there is reasonably good evidence supportingthe use of palliative chemotherapy and targeted therapy inolder adults with colorectal cancer. There is much more het-erogeneity of the benefits and harms of palliative chemother-apy, compared with adjuvant chemotherapy, due to the widespectrum of metastatic colorectal cancer from asymptomatic,oligometastatic disease to widespread metastases with a hightumor burden and substantial cancer-related symptoms. Inolder adults, the additional wide spectrum of premorbid phy-siological and functional reserve necessitates individualizedtreatment decisions.

5.3. Patients’ preferences for chemotherapy

Patients’ evaluations of the relative benefits and harms ofchemotherapy, compared with a given alternative or alterna-tives, are known as their preferences for the treatment [131].Consideration of patients’ preferences is a key feature ofshared decision-making, and clinicians should endeavor toelicit patients’ preferences for chemotherapy in order to per-sonalize treatment decisions.

Studies on patients’ preferences can be used by cliniciansas a guide and useful starting point in discussions abouttreatment. Preference studies quantitate the trade-offbetween the benefits and harms of chemotherapy by deter-mining the minimal survival benefit that makes the harms andinconveniences worthwhile. In general, patients judge smallsurvival benefits to make adjuvant chemotherapy worthwhile,but preferences are inherently individual and vary widely.Some patients require very small benefits and others verylarge benefits to make treatment worthwhile, and othersnever want chemotherapy at all.

A study on patients’ preferences for adjuvant chemother-apy for colon cancer included 123 patients with a median ageof 65 years (range: 19–86 years) who had all previously hadadjuvant treatment [132]. Most patients judged small survivalbenefits sufficient to make adjuvant chemotherapy worth-while, for example, an extra 1-month survival time (beyond abaseline of 5 years) or an extra 1% to 2% survival rate (beyond

a 5-year baseline of 65% or 85%). Older age was associatedwith needing larger survival benefits to make adjuvant che-motherapy worthwhile [132]. A recent study of patients’ pre-ferences for palliative chemotherapy for colon cancer included107 patients with a mean age of 57 years. Patients varied intheir willingness to tolerate different treatment-relatedadverse events, and older age was associated with a reducedwillingness to tolerate any adverse events [133]. Overall,patients were less willing to tolerate non-acute, adverseevents affecting quality of life, such as depression, fatigue,and pain.

To our knowledge, there have been no studies specificallyevaluating the treatment preferences of older adults withcolon cancer. One study on cancer patients’ preferences forchemotherapy in all cancer types suggested that older adultswere less willing to accept major toxicity for the same survivalbenefit as younger patients [134]. These limited data suggestthat older adults having chemotherapy for colorectal cancerwill likely need larger survival benefits to make the treatmentworthwhile.

6. Expert commentary

Evidence for the use of systemic therapy for older adultswith colon cancer has been historically limited by trialdesign, leading to the inclusion of only small numbers offitter older adults who are not representative of the generalgeriatric population. This has prompted age-based analysesof existing trial and population data sets and, more recently,elderly-specific prospective clinical trials. In the adjuvantsetting, the evidence supports the use of single-agent 5-FU/LV or capecitabine for stage III colon cancer, but less sooxaliplatin-containing combinations in patients aged≥70 years. In the palliative setting, there is good evidenceto suggest that older adults experience similar benefits toyounger adults from single-agent infusional fluorouracil orcapecitabine, though their toxicity profiles differ. Fitter olderadults are likely to experience similar benefits to youngeradults from combination chemotherapy regimens, but mod-est benefits in RR and PFS must be balanced with additivetoxicity where quality of life is a priority. The use of combi-nation chemotherapy regimens in less fit or frail olderadults is not supported by the available evidence. GAshould be incorporated into the routine assessment of theolder adult with cancer, both to provide information thatmay affect treatment decisions and to identify areas wheresupportive care interventions may be needed. Treatmentdecisions should consider the goals of care, an older adult’streatment preferences, and weigh up the relative benefitsand harms.

7. Five-year view

Colorectal cancer is an age-related and common cancer inwhich there is an established role for adjuvant chemotherapyand palliative chemotherapy and targeted therapy. Over thenext 5 years, there will be increasing numbers of older adultswith colorectal cancer who see oncologists regarding thesetreatments. This will help drive the development and

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recognition of geriatric oncology as a subspecialty and theneed for medical oncologists to have the skills to optimallytreat these patients.

The next major change in the treatment landscape forcolorectal cancer is the introduction of immunotherapy, anappealing option for older adults because of its generallybetter toxicity profile compared to chemotherapy. At present,immunotherapy seems most effective in the subset of patientswith microsatellite-unstable colorectal cancers, but currentresearch efforts are also focused on how to broaden its effi-cacy to the much larger cohort of patients with microsatellite-stable colorectal cancer.

The next 5 years will see an increase in elderly-specifictreatment trials in colorectal cancer with inclusion criteriabroadened to include older adults with medical comorbiditiesand reduced performance status. Research on the optimalintegration of GAs and risk-predicting tools will continue toimprove our understanding of ‘fitness for chemotherapy’ andhow best to use these assessments to guide treatment deci-sion-making, and studies of elderly-specific supportive careinterventions will improve holistic care. The increased aware-ness of decision-making priorities in the era of patient-cen-tered care will drive further research in this field.

Key issues

● Decision-making about chemotherapy for older adults withcolon cancer, in both the adjuvant and palliative settings, iscomplex.

● Patient factors affecting chemotherapy treatment decisionsinclude comorbidity, functional status, social supports, cog-nition, nutrition, ability to tolerate treatment toxicity, andpatient preference for treatment.

● Older adults are under-represented in trials of both adju-vant and palliative chemotherapy.

● An increasing number of elderly-specific trials, with broaderinclusion criteria and modified treatment regimens, arehelping to guide treatment in the palliative setting.

● The assessment of older adults prior to a decision abouttreatment with chemotherapy should evaluate factors thatmay impact upon treatment deliverability and outcomes,and focus on identifying areas for supportive careinterventions.

● Being mindful of the goals of care, balancing treatmentbenefits with harms, and incorporating patients’ preferencefor treatment is key to successful treatment decision-mak-ing for this population.

Funding

This paper was not funded.

Declaration of interest

E. B. Moth is supported by a University of Sydney Australian PostgraduateAward (APA) and funding support from Sydney Catalyst: The TranslationalCancer Research Centre of Central Sydney and regional NSW, University ofSydney, NSW, Australia and Cancer Institute NSW. The authors have no otherrelevant affiliations or financial involvement with any organization or entity

with a financial interest in or financial conflict with the subject matter ormaterials discussed in the manuscript apart from those disclosed.

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ORIGINAL ARTICLE

How do oncologists make decisions about chemotherapyfor their older patients with cancer? A surveyof Australian oncologists

E. B. Moth1,2& B. E. Kiely1,3 & V. Naganathan2,4

& A. Martin3& P. Blinman1,2

Received: 20 April 2017 /Accepted: 24 July 2017 /Published online: 3 August 2017# Springer-Verlag GmbH Germany 2017

AbstractPurpose Oncologists are making treatment decisions on in-creasing numbers of older patients with cancer. Due to comor-bidities and frailty that increase with age, such decisions areoften complex. We determined factors influencing oncolo-gists’ decisions to prescribe chemotherapy for older adults.Methods Members of the Medical Oncology Group ofAustralia (MOGA) were invited to complete an online surveyin February to April 2016.Results Ninety-three oncologists completed the survey ofwhich 69 (74%) were consultants and 24 (26%) were trainees,with most (72, 77%) working predominantly in a publichospital-associated practice. The three highest ranked factorsinfluencing decisions about (a) adjuvant chemotherapy wereperformance status, survival benefit of treatment, and life ex-pectancy in the absence of cancer and about (b) palliativechemotherapy were performance status, patient preference,and quality of life. Most geriatric health domains are

reportedly assessed routinely by the majority of respondents,though few routinely use geriatric screening tools (14%) orgeriatric assessments (5%). In hypothetical patient scenarios,oncologists were less likely to prescribe palliative and adju-vant chemotherapy as age and rates of severe toxicityincreased.Conclusion Performance status was the most influential factorfor oncologists when making a decision about chemotherapyfor their older patients, and the importance of other factorsdiffered according to treatment intent. Oncologists were lesslikely to recommend chemotherapy as patient age and treat-ment toxicity increased. The low uptake of geriatric assess-ments or screening tools provides scope for improved clinicalassessment of older adults in treatment decision-making.

Keywords Decision-making . Chemotherapy . Older adult .

Elderly . Oncologist

Introduction

Themost consistent determinant of an older adult’s decision toaccept or decline chemotherapy is their oncologist’s recom-mendation [1]. Little is known, however, about how oncolo-gists make decisions about chemotherapy for their older pa-tients. Treatment decisions in this setting are complex, withthe need to consider comorbidities, frailty, benefits and toxic-ity of treatment, and patients’ treatment preferences. There isalso limited clinical trial evidence in this population to guidetreatment as many trials have excluded older patients [2, 3].Chemotherapy use declines with increasing age in a numberof cancer types and treatment settings [4–11] providing goodrationale to explore how oncologists make decisions aboutchemotherapy for their older patients.

Electronic supplementary material The online version of this article(doi:10.1007/s00520-017-3843-0) contains supplementary material,which is available to authorized users.

* P. Blinmanprunella.blinman@sswahs.nsw.gov.au

1 Concord Cancer Centre, Concord Repatriation General Hospital,Building 76, Hospital Rd, Concord, NSW 2139, Australia

2 Concord Clinical School, University of Sydney, Sydney, Australia3 NHMRC Clinical Trials Centre, University of Sydney,

Sydney, Australia4 Centre for Education and Research on Ageing, University of Sydney

and Ageing and Alzheimers Institute, Concord Repatriation GeneralHospital, Sydney, Australia

Support Care Cancer (2018) 26:451–460DOI 10.1007/s00520-017-3843-0

Published studies, varying in methodology, have sought todetermine factors considered by oncologists when makingdecisions about chemotherapy for their older patients.Observational studies in early- [12] and late-stage [10] breastcancer have asked oncologists for reasons for their recommen-dation either for or against chemotherapy, and for regimenchoice. Factors frequently considered were perceived limitedbenefits, comorbidity, frailty, quality of life [12], age, andhealth status [10]. Studies that asked oncologists which factorsmost influence their chemotherapy recommendation havefound patient performance status the most frequently influen-tial factor [13–16]. Retrospective database reviews [5–7, 17,18], predominantly in the setting of early-stage colon cancer[5, 6, 17], have evaluated predictors of treatment choice andhave found that patient age and comorbidity predict whetherpatients received chemotherapy, though the scope of factorsevaluable retrospectively is a major limitation. Survey studiesusing hypothetical patient scenarios [16, 19–26], predomi-nantly in early breast cancer [16, 19, 20, 23, 25], have identi-fied advancing patient age [16, 20–26], comorbidity [20,22–25], and patient preference [26, 27] as predictors of receiptof chemotherapy and regimen type. Only one of these studiesaddressed palliative treatment decisions [26]. There is littledata about the utilisation of comprehensive geriatric assess-ments or geriatric assessment tools and the influence of che-motherapy toxicity on oncologists’ treatment decisions, de-spite the recommendation for the use of geriatric assessmentin routine practice [28] and the development of predictivetools that estimate the likelihood of chemotherapy toxicityfor older adults [29–31].

We aimed to determine factors influencing oncologists’decisions about chemotherapy for their older patients in bothadjuvant and palliative settings. We sought to determine thefollowing: (a) the importance of patient and clinical factors indecision-making; (b) methods used to assess older patients’suitability for chemotherapy; (c) oncologists’ attitudes to-wards decision-making; and (d) using hypothetical scenarios,the effect of age and expected rates of chemotherapy toxicityon the likelihood of recommending treatment.

Methods

Survey distribution

The Medical Oncology Group of Australia (MOGA) mem-bers (450 consultants, 174 trainees) were invited to completean online survey in February 2016. A reminder email was sent4 weeks later; the survey remaining open for 3 months.Completion of the anonymous survey constituted consent toparticipate. Surveys returned by oncologists whose clinicalpractice comprised at least 10% of patients ≥ 65 years wereincluded. The study was approved by the Sydney Local

Health District’s Human Research Ethics Committee ofConcord Repatriation General Hospital (CH62/6/2015-226).

Survey

The survey was designed to collect information about oncol-ogists’ approaches to decision-making (Supplementary 1).The importance of 14 pre-specified clinical factors in makinga decision about treating an older adult with (a) palliativechemotherapy for an advanced cancer and with (b) adjuvantchemotherapy for an early cancer was assessed on a 5-pointLikert scale. Respondents then ranked the three most influen-tial factors. Attitudes towards decision-making were evaluatedby asking respondents to what extent they agreed (on a 5-pointLikert scale) with six statements about the decision-makingprocess. Respondents were asked how they defined Ban olderadult with cancer^, and whether they thought there was an ageabove which adjuvant or palliative chemotherapy should gen-erally not be considered. Respondents were asked which clin-ical assessments they routinely perform (> 50% of the time)and how they assess individual health domains: formallyusing validated tools or informally. An informal assessmentwas defined as an evaluation using clinical judgement basedon history and examination, without using an objective clini-cal tool.

Hypothetical scenarios were used to determine the likeli-hood of oncologists recommending palliative and adjuvantchemotherapy according to age and risk of severe (grades 3–5) chemotherapy toxicity. The advanced cancer scenario de-scribed a patient with a symptomatic incurable cancer forwhich palliative chemotherapy had a response rate of 40%and a 3-month absolute improvement in median overall sur-vival (OS) (from 6 to 9 months). The early cancer scenariodescribed the patient as having a resected early-stage cancerfor which the 5-year OS rate was 70%, with adjuvant chemo-therapy reducing the risk of recurrence by 25% (from 40 to30%), giving an absolute improvement in 5-year OS of 5%(from 70 to 75%). Patient factors common to both scenarioswere as follows: Eastern Cooperative Oncology GroupPerformance Status of 1, independence in basic and instru-mental activities of daily living, adequate social supports, mi-nor comorbidity, and willingness to be guided by their oncol-ogist regarding treatment. For each scenario, respondents wereasked to rate on a 5-point Likert scale how likely they wouldbe to recommend chemotherapy if the patient were aged 70,75, 80, and ≥ 85 years if the probability of severe toxicity were(a) 10 and (b) 40% (16 decision-making scenarios in total, 8 ineach treatment setting).

Statistical analysis

Amean importance score for each pre-specified clinical factorwas derived based on Likert scale values (0 being Bnot at all

452 Support Care Cancer (2018) 26:451–460

important^, 5 being Bvery important^). The frequency of eachfactor being ranked first, second, and third was calculated.These frequencies were multiplied by a weight (wi = 4 − i,where i = rank position 1, 2, or 3) then summed to form a rankscore.

The proportion of oncologists Blikely^ or Bvery likely^ torecommend chemotherapy across the 16 hypothetical scenar-ios was summarised by patient age groupings, risk of toxicity,and setting. The frequency of selecting a given likelihoodcategory was presented using diverging stacked bar charts(each bar centred at the midpoint of the Bneutral^ category).The association between patient age and toxicity risk on thelikelihood to recommend chemotherapy was quantified foreach setting using logistic regression fitted using generalisedestimating equations to account for correlations among re-sponses from an individual respondent. This approach wasalso used to test the following physician factors as predictorsfor chemotherapy recommendation: physician age, consultantor trainee, sex, years of experience, proportion of practice≥ 65 years or ≥ 80 years, and how they defined an Bolder adultwith cancer .̂ Odds ratios for each factor represent the odds ofa chemotherapy recommendation (Blikely^/Bvery likely^ rela-tive to Bneutral^/Bunlikely^/Bvery unlikely^).

We sought 125 completed surveys to provide reasonableprecision of estimated proportions (i.e., 95% confidence inter-vals extending no more than +/−< 9% from point estimates).

Results

Ninety-three surveys were returned for a response rate of 15%,all meeting criterion for inclusion. Table 1 outlines the char-acteristics of respondents. About half were female (55%), andmost were consultants (74%) rather than trainees (26%) work-ing mainly in public hospital practices (77%). Most (53%)were aged 20–39 years, with few (8%) ≥ 60 years. The studysample was representative of the MOGA membership withrespect to available data on age, sex, and level of training.

Table 2 shows the importance ranking of chemotherapydecision-making factors. The three factors with the greatestoverall rank score in the adjuvant setting were as follows (inorder): performance status, survival benefit of treatment, andlife expectancy in the absence of cancer. In the palliative set-ting, these were the following: performance status, patientpreference, and quality of life.

Figure A (supplementary) shows the mean importance rat-ing of chemotherapy decision-making factors. Other than forage in the palliative setting, the mean importance rating of allfactors was ≥ 3 (at least moderately important). Performancestatus had the highest mean importance rating in both treat-ment settings (rated Bvery important^ by 94% of respondentsin the adjuvant setting and 97% in the palliative setting). Agehad the lowest mean importance rating in both settings (rated

Bimportant^ or Bvery important^ by 41% of respondents in theadjuvant setting and 26% in the palliative setting).

An Bolder adult with cancer^ was most frequently definedas ≥ 75 years (48%). Almost half of the oncologists (37, 45%)agreed with an upper age limit (an age above which chemo-therapy should generally not be considered) for adjuvant

Table 1 Participant demographics and clinical practice

Characteristic Surveyrespondentsn (%)

MOGAmembershipf

n (%)

Sexa Male 41 (44) 343 (55)Female 51 (55) 281 (45)

Position Consultant 69 (74) 450 (72)Trainee 24 (26) 174 (28)

Age 20–39 years 49 (53) 302 (50)40–59 years 37 (40) 236 (39)60 + years 7 (8) 61 (10)

Years of experienceb 1 to 5 years 31 (33)6 to 10 years 26 (28)10 to 20 years 14 (15)> 20 years 20 (22)

Practice type Mostly public 72 (77)Mostly private 7 (8)Equal public and

private11 (12)

Other (e.g., locumwork)

3 (3)

New patients seeneach year (mean)

158

Cancer types treatedc Breast 60 (65)Lung/thoracic 51 (55)Colorectal 53 (57)Genitourinary 39 (42)Upper GIT 36 (39)Neurological 13 (14)Gynaecological 25 (27)Head and neck 17 (18)Melanoma 17 (18)Sarcoma 7 (8)Other 3 (3)

Proportion of practice≥ 65 yearsd

< 10% 0 (0)10 to 25% 5 (5)26 to 50% 31 (33)51 to 75% 45 (48)> 75% 9 (10)

Proportion of practice≥ 80 yearse

< 10% 33 (35)10 to 25% 49 (53)26 to 50% 6 (6)51 to 75% 1 (1)> 75% 0 (0)

Definition of Ban olderadult with cancer^b

65 years and older 2 (2)70 years and older 33 (35)75 years and older 45 (48)80 years and older 11 (12)

a One missing responseb Two missing responsescMore than one cancer type could be selectedd Three missing responsese Four missing responsesf Demographic data accessed from the MOGA membership database at624 members, 2016; age missing for 25 members

Support Care Cancer (2018) 26:451–460 453

chemotherapy. Age limits proposed by these 37 oncologistswere > 85 (n = 20), > 80 (n = 14), and > 75 years (n = 3).Fewer oncologists (18, 22%) agreed with an upper age limitfor palliative chemotherapy, with proposed age limits being> 85 (n = 12), > 80 (n = 5), and > 70 years (n = 1).

Methods of assessment of older patients are presented inFig. 1. Only a minority of respondents routinely use geriatricscreening tools (14%) or a geriatric assessment (5%). Whenasked how they assess functional status, nutrition, cognition,and psychological state, most reported assessing these domains

Binformally^ using clinical judgement (functional status 87%,nutrition 70%, cognition 77%, psychological state 87%), ratherthan with specific assessment tools (functional status 12%,nutrition 13%, cognition 19%, psychological state 7%).

Figure 2 presents respondents’ attitudes to chemotherapydecision-making for older adults. Most (88%) agreed that theycould adequately assess which older adults were suitable forchemotherapy, but only half (52%) were confident in predictingwho was likely to experience chemotherapy-related toxicity.Most (93%) agreed that a clinical tool predictive of chemother-apy toxicity would be useful. A majority (71%) agreed thatthere is a role for geriatricians in themanagement of older adultswith cancer, but less than half (46%) agreed that geriatriciansshould play a role in cancer-specific treatment decision-making.

Figure B (supplementary) presents the likelihood of oncolo-gists recommending chemotherapy according to patient age andtreatment toxicity for each setting. The likelihood of a positivechemotherapy recommendation (Bvery likely^ or Blikely^) as afunction of patient age and toxicity risk for each setting is pre-sented in Fig. 3. In each setting, patient age was a significantpredictor (p < 0.001) of chemotherapy recommendationadjusting for toxicity risk. In the palliative setting, relative to a70-year-old patient, the adjusted odds of recommending chemo-therapy for a 75-year-old was 0.35 (95%CI 0.24–0.52), for an80-year-old 0.08 (95%CI 0.04–0.14), and for a patient≥ 85 years0.02 (95%CI 0.01–0.04). In the adjuvant setting, relative to a 70-

Table 2 Ranking of factors important in decision-making about chemotherapy

Adjuvant setting Palliative setting

Rankings by oncologistsa (number) Overall rankscoreb

Rankings by oncologistsa (number) Overall rankscoreb

Factor Ranked1st

Ranked2nd

Ranked3rd

Ranked1st

Ranked2nd

Ranked3rd

Performance status 28 14 8 120 (1) 47 13 8 175 (1)

Survival benefit with treatment 12 21 20 98 (2) 0 5 6 16

Life expectancy in the absence ofcancer

16 12 15 87 (3) 2 3 9 21

Patient preference 8 8 7 47 10 13 11 67 (2)

Quality of life 1 5 3 16 9 10 13 60 (3)

Functional status 4 4 5 25 12 8 7 59

Comorbidities 3 9 6 33 2 15 10 46

Cancer type 8 5 5 39 4 4 5 25

Cancer-related symptoms – – – – 3 7 8 31

Treatment toxicity 3 5 9 28 3 5 8 27

Cognition 1 5 4 17 1 5 6 19

Age 2 0 4 10 0 0 2 2

Burden of disease – – – – 0 2 1 5

Social supports 0 0 0 0 0 0 2 2

a Represents the number of oncologists ranking the nominated factor as 1st, 2nd, or 3rd in degree of importance in treatment decision-makingb Each rank was assigned a weight (a rank of 1st was assigned 3 points, 2nd = 2 points, 3rd = 1 point), with frequencies multiplied by these weights andthen summed to form an overall rank score for each factor

1

5

14

45

64

70

88

93

96

98

99

100

0 20 40 60 80 100

Consult with a geriatrician

Use a geriatric assessment

Use a geriatric screening tool

Enquire about falls

Assess nutri�on

Assess psychological state

Assess cogni�on

Assess number of medica�ons

Assess func�onal status

Assess social supports

Assess performance status

History and physical examina�on

% of oncologists rou�nely performing assessment

Fig. 1 Assessments routinely performed by oncologists for older adultswith cancer. Oncologists were asked which of the above clinicalassessments they performed routinely (> 50% of the time) in evaluatingan older adult with cancer

454 Support Care Cancer (2018) 26:451–460

year-old patient, the adjusted odds of recommending chemother-apy for a 75-year-old was 0.28 (95%CI 0.19–0.40), for an 80-year-old 0.06 (95%CI 0.03–0.10), and for a patient ≥ 85 years0.01 (95%CI 0.01–0.03). In each setting, toxicity risk was asignificant predictor (p < 0.001) of chemotherapy recommenda-tion adjusting for patient age. In the palliative setting, relative toa chemotherapy regimen with a toxicity risk of 10%, the adjust-ed odds of recommending chemotherapy with a toxicity risk of40% was 0.06 (95%CI 0.03–0.10). In the adjuvant setting, thisodds ratio was 0.14 (95%CI 0.09–0.22).

In the palliative setting, there were no respondent charac-teristics predictive of chemotherapy recommendation

(Table 3). In the adjuvant setting, oncologists with a higherproportion of their practice aged ≥ 80 years (≥ 10 vs < 10%)were more likely to recommend chemotherapy on univariateanalysis, and this remained predictive after adjusting for ageand toxicity risk (OR 2.43, 95%CI 1.09–5.42, p = 0.03).

Discussion

In our study, performance status was the most important con-sideration influencing oncologists’ decisions about chemo-therapy, whereas chronological age was one of the least

11

1

1

29

8

17

1

18

4

33

20

36

6

29

8

25

57

36

60

45

71

2

14

11

33

7

17

0% 20% 40% 60% 80% 100%

My clinical prac�ce has adequate access to a geriatricmedicine service

There is a role for geriatricians in the management of olderadults with cancer

There is a role for geriatricians in treatment decision-makingfor older adults with cancer

A clinical tool that predicts the likelihood of significantchemotherapy-related toxicity in older adults would be useful

I am able to predict which older pa�ents are likely toexperience toxicity from chemotherapy

I am able to assess an older pa�ent's suitability forchemotherapy

Propor�on of surveyed oncologists (%)

Strongly disagree Disagree Neither agree nor disagree Agree Strongly agree

Fig. 2 Attitudes towardschemotherapy decision-making.Oncologists were asked to rate ona 5-point Likert scale the extent towhich they agreed with the abovestatements concerning decision-making about chemotherapy forolder adults. The stacked bargraph shows the proportion ofrespondents answering in eachresponse category

0102030405060708090100

70 years 75 years 80 years ≥85 years%on

cologistsrecom

men

ding

chem

othe

rapy

Hypothe�cal pa�ent age

10% rate of severe toxicity

40% rate of severe toxicity

0102030405060708090

100

70 years 75 years 80 years ≥85 years%on

cologistsrecom

men

ding

chem

othe

rapy

Hypothe�cal pa�ent age

10% rate of severe toxicity

40% rate of severe toxicity

0

10

20

30

40

50

60

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40% expected rate ofsevere toxicityPr

opor�o

nof

oncologists(%)

recommen

ding

chem

othe

rapy

Adjuvant se�ng Pallia�ve se�ng

a

c

bFig. 3 Relationship betweenchemotherapy recommendation,age, treatment toxicity, andsetting. a Chemotherapyrecommendation according to ageand toxicity in the palliativescenario. b Chemotherapyrecommendation according to ageand toxicity in the adjuvantscenario. c Chemotherapyrecommendation for all ages bytreatment setting and toxicity

Support Care Cancer (2018) 26:451–460 455

important factors. An Bolder adult with cancer^ was mostcommonly defined as ≥ 75 years, with an upper age limitagreed on more frequently for adjuvant rather than palliativechemotherapy. Oncologists make an assessment of most geri-atric health domains but rarely use formal geriatric assess-ments or screening tools. Oncologists were less likely to rec-ommend chemotherapy as patient age and risk of treatmenttoxicity increased.

The emphasis on performance status to guide the treatmentdecisions of oncologists in our study is consistent with otherstudies of older adults [14, 16]. Reasons for this include on-cologists’ experience with performance status as a quick andeasy assessment, a commonly used clinical trial enrolment

criterion, and its association with patient outcomes [32].There is, however, a trend away from relying solely on per-formance status in older adults with cancer. It only provides asummative measure of patient function, failing to capture oth-er factors important in treatment decision-making, such ascomorbidity, nutrition, social supports, and more subtle defi-cits in physical functioning [33].

Almost half of the oncologists agreed with age limits foradjuvant chemotherapy, albeit fewer for palliative chemother-apy, despite age rating as the least important factor influencingchemotherapy decision-making. The reason for less oncolo-gists agreeing with an age limit for palliative chemotherapy islikely because palliative chemotherapy is predominantly

Table 3 Predictors of chemotherapy recommendation in hypothetical scenarios

Palliative scenario Adjuvant scenario

Univariate analysis Multivariate analysis Univariate analysis Multivariate analysis

Potential predictor Odds ratio p value Odds ratio p value Odds ratio p value Odds ratio p value

Age of patient

70 years 1 < 0.001 1 < 0.001 1 < 0.001 1 < 0.001

75 years 0.51 (0.41 to 0.64) 0.35 (0.24 to 0.52) 0.36 (0.27 to 0.47) 0.25 (0.17 to 0.37)

80 years 0.18 (0.13 to 0.25) 0.08 (0.04 to 0.14) 0.09 (0.06 to 0.14) 0.05 (0.02 to 0.09)

≥ 85 years 0.05 (0.03 to 0.09) 0.02 (0.01 to 0.04) 0.02 (0.01 to 0.05) 0.01 (0.005 to 0.03)

Toxicity of regimen

10% 1 < 0.001 1 < 0.001 1 < 0.001 1 < 0.001

40% 0.13 (0.09 to 0.18) 0.06 (0.03 to 0.10) 0.26 (0.20 to 0.34) 0.13 (0.08 to 0.20)

Sex

Male 1 0.4 – 1 0.9 –

Female 1.20 (0.82 to 1.77) 0.96 (0.61 to 1.51)

Position

Consultant 1 0.1 – 1 0.1 –

Trainee 1.47 (0.87 to 2.49) 0.66 (0.40 to 1.09)

Age of physician

20 to 39 years 1 0.3 – 1 0.7 –

40 + years 0.82 (0.55 to 1.22) 1.08 (0.68 to 1.71)

Years of experience

1 to 10 years 1 0.2 – 1 0.7 –

> 10 years 0.77 (0.52 to 1.14) 0.93 (0.58 to 1.47)

Proportion of practice ≥ 65 years

< 50% 1 0.1 – 1 0.1 –

≥ 50% 1.45 (0.93 to 2.24) 1.50 (0.93 to 2.41)

Proportion of practice ≥ 80 years

< 10% 1 0.2 – 1 0.04 1 0.03

≥ 10% 1.31 (0.84 to 2.05) 1.65 (1.02 to 2.67) 2.43 (1.09 to 5.42)

Definition of Ban older adult with cancer^

> 65 or > 70 years 1 0.6 – 1 0.3 –

> 75 or > 80 years 1.12 (0.73 to 1.71) 1.32 (0.80 to 2.18)

Odds ratio the odds of being likely or very likely to recommend chemotherapy

Multivariate analysis only predictors found to be significant on univariate analysis included in multivariate analysis

456 Support Care Cancer (2018) 26:451–460

given to improve symptoms and quality of life. Its benefits areimmediate, and so, it is often considered at any age. In con-trast, adjuvant chemotherapy reduces the risk of future recur-rence in a patient without cancer symptoms, at the cost oftoxicity and reduced quality of life. In this setting, patientsneed to have adequate estimated life expectancy to makeshort-term toxicities worthwhile. Chronological age is a pre-dictor of cancer treatment choice [5, 16, 17, 20–24, 34, 35]and treatments received [4, 6–10, 18], but it does not alwayscorrelate with physiological age [36], which is more informa-tive for treatment decision-making. Oncologists agreeing withage limits for chemotherapy may be making associations be-tween chronological age and other factors such as comorbid-ities or reduced physiological reserves, or limited life expec-tancy and unclear survival benefits from treatment. This maybe particularly the case for adjuvant chemotherapy, where lifeexpectancy and survival benefits from treatment were rankedas highly influential factors. Chronological age having thelowest importance rating for chemotherapy decision-makingin our study is reassuring and suggests that oncologists con-sider a range of factors other than age alone when assessingolder adults for chemotherapy.

Few oncologists reported routinely using geriatric assess-ments and screening tools, consistent with other studies [15,37]. Despite the potential benefits of a geriatric assessment[28, 38, 39] and its incorporation into international oncologyguidelines [28, 40], the best model for its implementation intolocal practice is unknown. Additionally, its role in decision-making about chemotherapy remains unclear, likelyexplaining the low uptake in our study. The strongest evidencefor the use of a geriatric assessment in oncology is in identi-fying geriatric problems not otherwise recognised [28, 41, 42].There is inconsistency across studies for the ability of thegeriatric assessment or its component parts to predict mortalityand treatment toxicity [42, 43]. Impaired instrumental activi-ties of daily living, poor performance status, and number ofdeficits are the most consistent predictors of mortality from ageriatric assessment [39]; however, consistent individual pre-dictors for toxicity are lacking [39, 42]. Further reasons for thelow use of geriatric assessments or tools in our study include alack of awareness, doubts about benefits to patient care, lackof time, and confidence in clinical acumen and care otherwiseprovided to patients. Most respondents (88%) in our studywere confident in assessing an older patient’s suitability forchemotherapy, and, as such, may not value additionalassessments.

Only 52% of the respondents were confident in predictingchemotherapy-related toxicity, and most (93%) agreed that aclinical tool predictive for toxicity would be useful. Such toolshave been developed in the research setting [29–31] but are notyet widely used in practice. The Cancer and Aging ResearchGroup’s (CARG) Toxicity Score [30, 31], for example, is an11-item validated predictive model using clinical and geriatric

assessment variables to classify a patient as at low-, medium-,or high-risk of experiencing severe (grades 3–5) chemotherapytoxicity. Another example, the CRASH Score [29], providesseparate models for haematological and non-haematologicaltoxicities. The results of our study suggest that oncologistsunderestimate the actual risk of severe chemotherapy toxicityin practice and may benefit from the use of predictive toxicitytools if these were validated locally. To illustrate, actual rates ofsevere chemotherapy toxicity in prospective studies of olderadults with solid organ cancers are about 50% [29, 31]. In ourstudy, however, few respondents were likely to recommendchemotherapy in a hypothetical situation when the rate of se-vere toxicity was lower than this (i.e., 40%). The proportion ofoncologists likely to recommend chemotherapy to a 75-year-old fell from 59 to 19% in the adjuvant scenario and from 76 to18% in the palliative scenario as the expected rate of severetoxicity increased from 10 to 40%.

The likelihood of recommending chemotherapy in both theadjuvant and palliative scenarios decreased with increasingpatient age as in other studies [16, 20–26, 44]. Naeim et al.[20], for example, surveyed 151 oncologists regarding thetreatment of older women with early-stage breast cancer usinghypothetical scenarios that varied by patient age (70, 75, 80,and 85 years) and health status (good, average, or poor). Bothage and health status predicted treatment recommendations(p < 0.0001 for both). Keating et al. [24] surveyed 1096 on-cologists about chemotherapy for stage III colon cancer usinghypothetical scenarios that varied by patient age (55 vs80 years) and comorbidity (none, moderate, or severe conges-tive cardiac failure). Both age and comorbidity were strongpredictors of treatment recommendation. It is important toconsider that patient age has been one of at most two factorsvaried and tested in previous studies using hypothetical sce-narios, as in our study, and so it is difficult to conclude thatrespondents in these types of studies would not consider otherpatient or clinical factors more important. For example, in ourstudy, oncologists may have related increasing chronologicalage with patient characteristics not described in the scenarios,such as reduced physiological reserves or altered pharmaco-kinetics, or placed greater significance on the described minorcomorbidities as the hypothetical patient’s age increased. Assuch, concluding that oncologists focus singularly on chrono-logical age from this and other similar studies may not beentirely reasonable.

The only respondent characteristic predictive of chemo-therapy recommendation was a clinical practice with a higherproportion of patients aged ≥ 80 years for the adjuvant scenar-io. Chemotherapy has previously been shown to be more like-ly recommended by oncologists with high-volume practices[20, 23] in teaching roles or large cancer centres [24]. It islikely, however, that there are oncologist factors that influencetreatment recommendations beyond demographics and thatare difficult to measure, such as comfort with risk, individual

Support Care Cancer (2018) 26:451–460 457

age-related bias, temperament, and the oncologist’s own pref-erence for chemotherapy.

Our study adds to existing knowledge on the complexitiesof chemotherapy decision-making for older adults. Strengthsinclude the novel data on how chemotherapy toxicity influ-ences oncologists’ recommendations. The use of hypotheticalscenarios with unspecified tumour types allowed for re-sponses not limited by subspecialty knowledge and focussedthe survey on the general principles of chemotherapy treat-ment decision-making to make it broadly applicable. It shouldbe emphasised that this study evaluated treatment decision-making only with regard to chemotherapy and that oncolo-gists’ attitudes towards recommending immunotherapies andtargeted agents may differ, particularly given their substantial-ly different toxicity profiles and potential to be costly. A lowerresponse rate than expected was a limitation of our study.Though comparable to other local surveys of oncologists[45] and fairly typical of non-incentivised physician surveys[46, 47], the low response rate reduced our power to detectsignificant predictors of chemotherapy recommendation. As isrepresentative of theMOGAmembership, most of our respon-dents were aged under 40. It is possible that older oncologistsmake decisions about older patients differently. Responderdrop-out was also evident, with 84 of the 93 respondents com-pleting all questions. Generalisability of the survey is likelylimited by the majority of respondents practicing in the publicsystem, meaning our study population was not entirely repre-sentative of all Australian oncologists [48]. We used pre-specified clinical factors for the rating and ranking questionsand evaluated only two factors in the hypothetical scenarios tomaintain focus and reduce respondent burden. In reality,decision-making is complex, dynamic, and individualised,and there are many more factors affecting treatment recom-mendation. Responses to hypothetical scenarios may also notbe truly reflective of actual clinical practice.

Clinical implications of our study are that decisions aboutchemotherapy for older adults are a complex interplay of var-ious factors, including patient age and risk of treatment toxic-ity. This raises the question about whether oncologists shouldconsider using a geriatric assessment and available validatedassessment tools to improve their evaluation of older patientsand optimise their decision-making, though definitive proofthat taking this approach improves patient outcomes isawaited. Research implications include the exploration ofolder adults’ decision-making priorities and preferences forchemotherapy, the evaluation of the impact of geriatric assess-ment measures over and above routine clinical evaluation ontreatment decision-making, and strategies to implement geri-atric assessment in routine clinical practice. Further work toincrease the representation of older adults in clinical trials isalso warranted, since variations in treatment for older adultswill likely continue so long as there is minimal evidence toguide optimal treatment.

Conclusion

In conclusion, performance status was the predominant factorinfluencing chemotherapy decision-making for older adultswith cancer of any stage, and recommendations for adjuvantand palliative chemotherapy decreased as age and toxicityincreased. The limited uptake of formal instruments or geriat-ric assessments provides scope for improvement in the routineclinical assessment of older adults with cancer.

Acknowledgements Dr. EM is supported by two PhD scholarships: aUniversity of Sydney Australian Postgraduate Award (APA) and PhDfunding support from the Sydney Catalyst: the Translational CancerResearch Centre of Central Sydney and regional NSW, University ofSydney, NSW, Australia and Cancer Institute NSW. Survey distributionwas via the Medical Oncology Group of Australia (MOGA).

Authors’ contributions Study concepts are from EB Moth, BE Kiely,and P Blinman.

Study design is by EB Moth, BE Kiely, V Naganathan, A Martin, andP Blinman.

Data acquisition was conducted by EB Moth.Quality control of data and algorithms was done by EB Moth and A

Martin.Data analysis and interpretation were performed by EB Moth, BE

Kiely, V Naganathan, A Martin, and P Blinman.Manuscript preparation was done by EB Moth.Manuscript editing was conducted by EB Moth, BE Kiely, V

Naganathan, A Martin, and P Blinman.Manuscript review was performed by EB Moth, BE Kiely, V

Naganathan, A Martin, and P Blinman.

Compliance with ethical standards

Disclosures The authors declare that they have no disclosures.

Ethical statement Completion of the anonymous survey constitutedconsent to participate. The study was approved by the Sydney LocalHealth District’s Human Research Ethics Committee of ConcordRepatriation General Hospital (CH62/6/2015-226).

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Predicting chemotherapy toxicity in older adults: Comparing thepredictive value of the CARG Toxicity Score with oncologists' estimatesof toxicity based on clinical judgement

Erin B. Moth a ,b ,⁎, Belinda E. Kiely a ,b , Natalie Stefanic b , Vasikaran Naganathan c ,d , Andrew Martin b ,Peter Grimison b ,e , Martin R. Stockler a ,b , Philip Beale a ,b , Prunella Blinman a ,b

a Concord Cancer Centre, Concord Repatriation General Hospital, Sydney, Australiab University of Sydney, Sydney, Australiac Centre for Education and Research on Ageing, Concord Clinical School, University of Sydney, Australiad Ageing and Alzheimers Institute, Concord Repatriation General Hospital, Sydney, Australiae Chris O'Brien Lifehouse, Sydney, Australia

a b s t r a c ta r t i c l e i n f o

Article history:Received 23 April 2018Received in revised form 20 July 2018Accepted 31 August 2018Available online 14 September 2018

Aim: The Cancer and Aging Research Group's (CARG) Toxicity Scorewas designed to predict grade ≥3 chemother-apy-related toxicity in adults aged ≥65 yrs. commencing chemotherapy for a solid organ cancer. We aimed toevaluate the CARG Score and compare it to oncologists' estimates for predicting severe chemotherapy toxicityin older adults.Methods: Patients aged ≥65 yrs. starting chemotherapy for a solid organ cancer had their CARG Score (range0–23) calculated. Their treating oncologist, blinded to these results, independently estimated each patient'srisk of severe chemotherapy toxicity (0–100%). Toxicities were captured prospectively. The predictive value ofthe CARG Score and oncologists' estimates was estimated using logistic regression and in terms of Area Underthe Receiver Operating Characteristic curve (AU-ROC).Results: 126 patients from ten oncologists at two sites participated. The median age was 72 yrs. (range 65–84).The median CARG Score was 7 (range 0–17); the median oncologist estimate of risk was 30% (range 3–80%),and these measures were not correlated (r =−0.01). 64 patients (52%) experienced grade ≥ 3 toxicity. Ratesof severe toxicity in low-, intermediate-, and high-risk groups by CARG Score were 58%, 47%, and 58% respec-tively, and 63%, 44%, and 67% by oncologist estimate. Severe chemotherapy toxicity was not predicted bythe CARG Score (OR 1.04, 95%CI 0.92–1.18, p = .54, AU-ROC 0.52), or oncologists' estimates (OR 1.00, 95%CI0.98–1.02, p= .82, AU-ROC 0.52).Conclusion: Neither the CARG Score, nor oncologists' estimates based on clinical judgement, predicted severechemotherapy-related toxicity in our population of older adults with cancer. Methods to improve risk predictionare needed.

© 2018 Elsevier Ltd. All rights reserved.

Keywords:Decision-makingChemotherapy toxicityOlder adultElderly

1. Introduction

Chemotherapy toxicity is an important consideration for olderadults with cancer [1,2] and their oncologist [3] whenmaking decisionsabout the treatment. About 50% of older adults having chemotherapyfor a solid organ cancer will experience a severe (grade 3 to 5) chemo-therapy-related toxicity over the course of treatment. [4–9] Such toxic-itiesmay impair quality of life, and lead to hospital admissions and earlycessation of treatment. Clinical prediction tools that help distinguishbetween older adults at low or high risk of chemotherapy-related

toxicity may help optimise treatment decisions and improve patientcare. Ideally, such tools are easily implemented, maintain predictivevalue in varied populations, and add to clinical judgement. [10,11]

The Cancer and Aging Research Group's (CARG) Toxicity Score is achemotherapy toxicity risk prediction tool that provided proof of con-cept for combining geriatric assessment variables with clinical charac-teristics to predict chemotherapy-related toxicity in older adults. [5,6]Eleven clinical, treatment, and geriatric assessment (GA) variables areused to classify patients as low- (score 0–5), intermediate- (score6–9), or high-risk (score N 10) for severe chemotherapy-related toxic-ity. It was developed in a cohort of 500 patients aged ≥65 years havingchemotherapy for a solid organ cancer of any type or stage. The scoreshowed moderate predictive performance [Area Under the ReceiverOperating Characteristic (AU-ROC), 0.72], with rates of severe toxicity

Journal of Geriatric Oncology 10 (2019) 202–209

⁎ Corresponding author at: Concord Cancer Centre, Concord Repatriation GeneralHospital, Hospital Rd, Concord, NSW 2139, Australia.

E-mail address: Erin.Moth@health.nsw.gov.au (E.B. Moth).

https://doi.org/10.1016/j.jgo.2018.08.0101879-4068/© 2018 Elsevier Ltd. All rights reserved.

Contents lists available at ScienceDirect

Journal of Geriatric Oncology

increasing across risk groups (30% for low-, 52% for intermediate-, and83% for high-risk). The score was validated in a similar external cohort(AU-ROC 0.65), [5] and has been tested in smaller populations withprostate [12] and lung [13] cancers with mixed results. Whether thescore is predictive of lower grade toxicities, often significant in olderadults, is unknown.

Prior to this study, the CARG Score had not been tested in a similarpopulation outside of the United States, and there was little data com-paring the CARG Score with clinical judgement. The aims of this studywere therefore to (i) evaluate the CARG Score for predicting severe che-motherapy-related toxicity in an external population of older adults,and (ii) compare it to the predictive value of oncologists' estimates ofthe risk of severe toxicity. Secondary aimswere to determine the abilityof the CARG Score to predict for all-grade toxicities and to identifypredictors of severe toxicity using a brief GA.

2. Methods

2.1. Design and Participants

A prospective observational study was conducted at two tertiaryreferral cancer centres in Sydney, Australia. Eligibility criteria were asused by Hurria et al. [6]: age ≥ 65 years, a solid organ cancer (any typeor stage), and starting an initial or new line of outpatient chemotherapy.Exclusion criteria included concurrent radiotherapy, treatment withimmunotherapy, or insufficient English to complete assessments.

Ethics approval was granted by the Sydney Local Health DistrictHuman Research Ethics Committee of Concord Repatriation GeneralHospital (HREC/15/CRGH/102). Signed, informed consent was obtainedfrom all participants.

2.2. Procedures

Prior to starting chemotherapy, each participant's CARG Score(range 0–23) was calculated by a study researcher with whom theycompleted an abbreviated GA covering standard health domains (com-ponents outlined with presentation of the results). The GA was to becompleted prior to the commencement of chemotherapy whilstminimising additional hospital visits. Treating oncologists were blindedto the results of the CARG Score and GA and asked to independently es-timate participants' risk of severe chemotherapy-related toxicity overthe course of planned treatment (nomination of a single number from0 to 100%). This was after they had assessed the patient and decidedto commence chemotherapy. Treating oncologists were asked to docu-ment details of the proposed chemotherapy treatment (regimen, treat-ment intent, and dose), and assess participants' performance status (byEastern Cooperative Oncology Group Score [14] & Karnofsky Perfor-mance Status, [15]) and level of frailty using the Canadian Study ofHealth and Aging (CSHA) Clinical Frailty Scale. [16]

Participants were followed prospectively until completion ofplanned chemotherapy, or cessation due to disease progression, patientpreference, death, or toxicity. Chemotherapy-related toxicities (all-grade)were captured prospectively, recorded by the treating oncologistat each clinical presentation (routine or otherwise, includinghospitalisations) in the presence of a trained study investigator (NS)or clinician (oncologist) researcher (EM) and graded as per the NCICTCAE version 4.0. [17] The electronic medical record for each patientwas also reviewed each cycle by the study's clinician researcher (EM)such that all clinical encounters and their toxicities were captured, in-cluding hospitalisations. Grade ≥ 3 haematological toxicities were re-corded if present on the day of the next cycle and led to a treatmentmodification or intervention, or if present upon presentation for toxicitybetween cycles. No GA-driven interventions were made, and any sup-portive interventions were as per usual practice for each oncologist.

2.3. Statistical Analysis

The populationwas described using frequencies and proportions (%)for categorical variables andmean andmedian for continuous variables.Correlation between the CARG Score and oncologists' estimates wastested using Spearman's correlation coefficient. The proportion ofpatients with severe toxicity in each risk group by (i) CARG Score and(ii) oncologists' estimates were determined. CARG Score risk groupswere per the derivation study [6]: low-risk (score 0–5), intermediate-risk (score 6–9), and high-risk (score ≥ 10). Risk groups by oncologists'estimates were determined by quartiles (middle two quartilescombined) and were as follows: low-risk (b30%), intermediate-risk(30–50%), and high-risk (N50%). Associations between risk groups andsevere toxicity were tested using Chi-tests of association.

Univariate and multivariate logistic regression were used to explorepotential associations between toxicity and covariates, with the CARGScore and oncologists' estimates here treated as continuous variables.AU-ROC curves were used to summarise the predictive performanceof (i) the CARG Score, (ii) oncologists' estimates, and (iii) a combinedmeasure of the two. The combined measure was the risk score esti-mated from a logistic regression model with the CARG Score and oncol-ogists' estimates fitted as covariates. Each of the 11 component items ofthe CARG Score were tested for association with toxicity, as were eightpre-specified patient measures: age, ECOG-PS, CSHA Clinical FrailtyScale, Orientation-Memory-Concentration (OMC) test, Mini-NutritionalAssessment Short-Form (MNA-SF), Timed Up andGo (TUG), G8 Vulner-ability Score, and summary GA score, with variables dichotomisedwhere appropriate.

As we were testing the CARG Score in a new population, we com-pared the proportions of participants scoring on each of the eleven com-ponent items of the CARG Score to those in the development population[6] using chi-squared statistics.

The planned sample size was N= 100 to provide 87% power at thetwo-sided 5% level of statistical significance to identify, using logisticregression modelling, a two-fold increase in the odds of a grade 3+toxicity for every standard deviation increase in CARG Score, assumingan underlying prevalence of grade 3+ toxicity of 50%.

3. Results

3.1. Participant Characteristics

Between August 2015 and November 2016, 126 patients were en-rolled and completed baseline assessments. Demographics and clinicalcharacteristics are shown in Table 1 and GA results in Table 2. Mostpatients were of good performance status (ECOG-PS 0 or 1, 110, 87%)and performed well on measures of functional status. Oncologistsrated most patients as ‘fit or well’ (93, 74%), and a minority ‘vulnerable’(28, 22%) or ‘frail’ (5, 4%). A small proportion scored outside of normalrange on screening for cognitive impairment (24, 20%) and depression(32, 25%). Over half (70, 55%) were ‘at-risk’ or ‘likely malnourished’ onnutritional assessment. Patients werewell supported socially, hadmod-erate comorbidity, and one third (41, 32%) rated their health as ‘excel-lent’ or ‘very good’. The median time from abbreviated GA to the startof chemotherapy was 0 days (range 0 to 41 days).

Supplementary Table 1 compares the study populationwithHurria'sderivation cohort [6] by components of the CARG Score. Our cohort hadsignificantly more participants with gastrointestinal malignancies andless with impaired ability to walk one block.

3.2. CARG Score and Oncologists' Estimates

Distributions of the CARG Score and oncologists' estimates areshown in Fig. 1 (A and B). The median CARG Score was 7 (range0–17), with 25 (20%) participants classified as low-, 77 (61%) as inter-mediate, and 24 (19%) as high-risk. The median oncologist estimate

203E.B. Moth et al. / Journal of Geriatric Oncology 10 (2019) 202–209

was 30% (interquartile range 30–50%), with 30 (24%) classified as low-(estimate b30%), 79 (63%) as intermediate- (estimate 30–50%), and 15(12%) as high-risk (estimate N50%). Oncologists' estimates and theCARG Score were not correlated, r =−0.03 (Fig. 2).

3.3. Chemotherapy Toxicity

Of the 126 patients who completed baseline assessments, one diedfrom rapidly progressing cancer prior to treatment, and one moved toanother centre, leaving 124 patients included in the outcome analysis.Patients received a median of six chemotherapy cycles (range one-sev-enteen). Sixty patients (48%) completed their planned chemotherapycourse. Reasons for discontinuation in the remaining 64 (52%)were dis-ease progression [31], patient preference [11], toxicity [11], miscella-neous [7], relocation to another cancer centre [2], and death [2].

Sixty-four patients (52%) experienced a grade ≥ 3 chemotherapy-re-lated toxicity over their treatment course, in 26 of 64 (21%) followingcycle 1. Forty-three patients (35%) experienced a grade ≥ 3 non-haema-tological toxicity, and 38 (31%) experienced a grade ≥ 3 haematologicaltoxicity. The type and frequency of severe and all-grade toxicities areoutlined in Supplementary Tables 2 and 3. The most common grade ≥3 non-haematological toxicities were non-neutropenic infection (12,10%) and fatigue (9, 7%); the most common all-grade toxicities were

fatigue (113, 91%) and nausea (67, 54%). Fifty-four participants (44%)were hospitalised during their treatment course.

Thirty-three patients (28%) commenced cycle 1 of chemotherapy ata reduced dose. The proportion of patients commencing cycle 1 at a re-duced dose according to their oncologist-rated ECOG-PS was ECOG-PS0/1 21% and ECOG-PS ≥2 75%. The proportion of patients planned tocommence cycle 1 at a reduced dose by their CARG Score risk groupwas low-risk 40%, intermediate risk 23%, and high risk 29%. Duringtheir chemotherapy, 49 patients (40%) had a reduction in dose/regimenintensity, 31 (25%) had a treatment delay, and fifteen (12%) had an in-crease in dose/regimen intensity.

3.4. Predictive Value of the CARG Score and Oncologists' Estimates

The CARG Score did not predict severe chemotherapy-related toxic-ity (OR 1.04, 95%CI 0.92–1.18, p = .5) and had low discriminatory value(AU-ROC 0.52, 95%CI 0.42–0.62). Oncologists' estimates also did notpredict severe chemotherapy-related toxicity (OR 1.00, 95%CI 0.98–1.02, p = .8) and had low discriminatory value (AU-ROC 0.52, 95%CI0.42–0.62). (Fig. 3) Rates of severe toxicity in low-, intermediate-, andhigh-risk groups by CARG Score were 58%, 47%, and 58% respectively,with no association between risk group and toxicity (p= .4). Rates ofsevere toxicity in low-, intermediate-, and high-risk groups by oncolo-gist estimatewere 63%, 44% and 67% respectively (p= .1). (Supplemen-tary Fig. 1) The addition of the CARG Score to oncologists' estimates in acombined model did not improve its predictive value (AUC-ROC 0.52).There was no relationship between the CARG Score and the burden ofgrade 2 toxicities, or a sum of all-grade toxicities (Supplementary Fig.2). In a post-hoc analysis, there was no significant difference in propor-tions of patients hospitalised or completing planned treatment acrossrisk groups by CARG Score or oncologist estimate (SupplementaryTable 4). Details of hospitalisations are in Supplementary Table 5.

3.5. Other Predictors

Univariate logistic regression of the eight pre-specified clinical vari-ables (Table 3) and the eleven items of the CARG Score (SupplementaryTable 6) revealed: (i) better functional status (by TUG) to be negativelyassociated with severe grade 3–5 toxicity (OR 0.24, 95%CI 0.06–0.92, p= .04); (ii) better cognition (by OMC test) to be negatively associatedwith severe grade 3–5 toxicity (OR 0.37, 95%CI 0.14–0.96, p = .04);and, (iii) impaired social activity due to health (assessed via theMedicalOutcomes Study item of the CARG Score) to be positively associatedwith severe grade 3–5 toxicity (OR 2.19, 95%CI 1.05–4.59, p = .04). Nosatisfactory multivariate model was obtained using either forward orbackward selection approaches.

4. Discussion

Our study's main findings were that neither the CARG Score nor on-cologists' estimates of likelihood of severe chemotherapy-related toxic-ity were useful predictors of severe chemotherapy-related toxicity.Patients classified as low-risk by CARG Score experienced equivalentrates of severe toxicity as those classified as high-risk. There was no re-lationship between patients' CARG Score and oncologists' estimates, andoncologists tended to underestimate risk. Prolonged TUG, impaired so-cial activity, and an abnormal OMC test were associated with severetoxicity.

Twopublished studies have reported on theutility of theCARG Scorein populations external to the derivation cohort. Nie et al. [13] tested theCARG Score in 120 patients receiving chemotherapy for lung cancer.Rates of toxicity significantly increased across low-, intermediate-, andhigh-risk groups (9%, 40%, and 60% respectively, p b .001). Alibhai etal. [12] tested the CARG Score in a cohort of 46 patients havingdocetaxelfor metastatic prostate cancer. There was a non-significant increase intoxicity across risk groups (rates of 0%, 17%, and 27% respectively; p

Table 1Participant characteristics (N= 126).

Characteristic N (%)*

Sex MaleFemale

75 (60)51 (40)

Cancer centre Concord Cancer CentreThe Chris O'Brien Lifehouse

88 (70)38 (30)

Age 65 to 69 years70 to 74 years75 to 79 years≥80 years

37 (29)41 (33)39 (31)9 (7)

Employment status Retired or not workingWorking

110 (87)16 (13)

Marital status Married/de factoWidowedDivorced/separatedSingle

89 (71)18 (14)7 (6)12 (10)

Living arrangements Lives with othersLives aloneCare facility

100 (79)26 (21)0 (0)

Language spoken at home EnglishNon-English

90 (71)36 (29)

Community services YesNo

11 (9)115 (91)

Cancer type ColorectalUpper gastrointestinal**

Lung/pleuraProstateBladderOvarianBreastOther

45 (36)23 (18)13 (10)10 (8)9 (7)9 (7)8 (6)9 (7)

Stage of cancer IIIIIIIV

0 (0)16 (13)32 (25)78 (62)

Line of treatment NeoadjuvantAdjuvant1st line palliativeSubsequent line palliative

11 (9)33 (26)55 (44)27 (21)

Chemotherapy regimen Single agentCombination chemotherapy

51 (40)75 (60)

Primary G-CSF YesNo

0 (0)126 (100)

Initial dose plan for cycle 1 Dose reducedStandard dose

35 (28)91 (72)

Abbreviations: G-CSF, granulocyte colony stimulating factor.* Due to rounding, not all percentages may= 100%.** Upper gastrointestinal includes pancreaticobiliary, gastric, and oesophageal cancers.

204 E.B. Moth et al. / Journal of Geriatric Oncology 10 (2019) 202–209

= .65), the study limited by small sample size and low event rate.Hurria et al. [5] published a validation study of 250 patients acrosseight centres in theUnited States, six ofwhich participated inmodel de-velopment. The predictive value of the CARG Score was insignificantlylower in the validation cohort (AU-ROC 0.65, 95%CI 0.58–0.71) than inthe development cohort (AU-ROC 0.72, 95%CI 0.68–0.77; p = .09) [6],with smaller differences in rates of toxicity between intermediate andhigh-risk groups (low, intermediate, and high-risk groups with ratesof 37%, 62%, and 70% respectively). In our study, there was no differencein toxicity rates across risk groups, and the model showed a predictive

ability close to chance (AU-ROC of 0.52), with a confidence interval(95%CI 0.42–0.62) entirely below that reported in the developmentstudy by Hurria et al. [6]

Differences in study population may explain the differences in AU-ROC estimates between our study and the development and validationstudies by Hurria et al. [5,6] Hurria et al.'s validation study [5] was con-ducted in similar centres in the United States (US) to the derivationstudy, [6] resulting in study populations with comparable characteris-tics. Our population had significantly more patients with gastrointesti-nal cancers and less with lung and breast cancer affecting (i)

Table 2Baseline geriatric assessment results (N= 126).

Characteristic Category N (%) Median Range Range of scores

Self-rated health Excellent or very good 41 (33)Good 54 (43)Fair or poor 31 (25)

CSHA Clinical Frailty Rating Fit, or well 92 (73)Vulnerable or frail 34 (27)

Performance statusECOG Performance Status 0 37 (29)

1 73 (58)2 15 (12)3 1 (1)4 0

Karnofsky Performance Rating Scale 90–100 53 (42)80 42 (33)≤70 31 (25)

Functional statusKatz Activities of Daily Living [25,26] Independent (score 6) 111 (88) 6 2–6 0–6

Dependent ≥1 task 15 (12)OARS Instrumental ADLs [27] Independent (score 14) 72 (57) 14 6–14 0–14

Dependent ≥1 task 54 (43)MOS Physical Functioning [28] No limitation 13 (10) 26 13–30 10–30

Some limitation 113 (90)Timed Up and Go [29,30] ≥14 s 14 (11) 11 s 6.7–55.7 secs

b14 s 108 (86)Falls in last 6 months Yes 18 (14)

ComorbiditiesCIRS-G [31] Total Score 4 0–12CIRS-G Index 1.55 0–3Number of category 3 (severe) 17 (13)Number of category 4 (life threatening) 6 (5)

PolypharmacyNumber of medications 4 0–16

Social SupportsSocial Support Survey [32] Complete social supports 75 (60) 20 4–20 0–20

Some deficit in support 51 (40)Mood and Cognition

5-Item Geriatric Depression Scale [33] Score 0 or 1 94 (75) 1 0–5 0–5Score ≥ 2 (abnormal) 32 (25)

Orientation-Memory-Concentration Test* [34] Score 0 to 4 (normal) 102 (81) 2 0–21 0–30Score 5 to 9 12 (10)Score ≥ 10 12 (10)

NutritionWeight loss in last 6 months Yes 75 (60)Mini-nutritional assessment short form [35] Normal nutrition (score 12 to 14) 56 (44) 11 6–14 0–14

At risk (score 8 to 11) 62 (49)Malnourished (score 0 to 7) 8 (6)

G8 [36] N14 45 (36)≤14 (at risk) 81 (64)

Geriatric assessment score** 0 or 1 62 (49)2 or 3 50 (40)≥4 14 (11)

Abbreviations: CSHA- Canadian Study of Health and Aging; ECOG- Eastern Cooperative Oncology Group; OARS- Older Americans Resources and Services; ADL- Activities of Daily Living;MOS- Medical Outcomes Study; CIRS-G- Cumulative Illness Rating Scale in Geriatrics; G8- Geriatric 8.*Categories reported under the Blessed OrientationMemory Concentration test are those suggested byMorris et al.'s Memory and Aging Project (1989), with normal cognition (score of 0to 4), requiring evaluation (score of 5 to 9), or likely consistent with dementia (score ≥ 10).**Geriatric assessment score (range 0 to 7) is a summary score for the geriatric assessment performed, where a point is scored for a deficit in a geriatric health domain as follows:- performance status ECOG 2 or more.- functional status: TUG N/= 14 s, any dependency in ADLs.- nutrition: MNA at risk or malnourished.- cognition: at risk or likely consistent with dementia.- social supports: b18 (lowest quartile).- psychological state: GDS N/= 2.- comorbidity: CIRS G score N 6 (highest quartile).

205E.B. Moth et al. / Journal of Geriatric Oncology 10 (2019) 202–209

algorithm scoring for ‘tumour type’ and (ii) planned chemotherapy reg-imens and consequent toxicity. Our population also had significantlyless patients with “impaired ability to walk one block” possiblyreflecting better overall fitness. Other considerations include differencesin local practices that affect the actual rates or grades of toxicity, for ex-ample, the use of prophylactic colony-stimulating factor support [0% inour study vs. 18% inHurria et al. [6]] and othermeans of supportive care.Further differences in practice patterns and healthcare systems that areless easily measured may also account for our findings. For example,having been developed in patients starting chemotherapy, the CARGScore is sensitive to what judgements have already been made aboutpatients deemed ‘fit for chemotherapy’. Australian oncologists may se-lect patients for chemotherapy differently to US oncologists, alteringthe score's performance.

Methodological differences potentially influencing results on theperformance of the CARG Score include the prospective recording oftoxicity in our study (traditional overestimation bias) versus Hurria etal.'s [6] retrospective design (underestimation bias). Opposing the di-rection of these traditional biases, the prospective nature of our studymay have introduced a bias to under-reporting or early supportive in-tervention for toxicity, as oncologists were aware these outcomes

were being scrutinised. Noted, however, are the comparable toxicityrates and rates of dose modification between the two studies.

Oncologists' estimates did not predict severe chemotherapy-relatedtoxicity. To our knowledge, the study by Alibhai et al. [12] is the onlyother published study that has evaluated the predictive value of oncol-ogists' estimates of chemotherapy-related toxicity in older adults, andshowed similar results. Oncologists' estimates on a 10-point scale (lowto high) were poorly correlated with the CARG Score (r = 0.109, p =.46) and were not predictive of toxicity (OR 1.04, 95%CI 0.71–1.52, p= .83). Reasons to explain this include: oncologists not being used toassigning a single numerical value to the overall likelihood of a severetoxicity; difficulty translating rates of toxicity published in clinical trialsto older, frailer adults in real-world practice; and, oncologists lackingconfidence in their ability to predict chemotherapy-related toxicity intheir older patients. [3]

Previous attempts at identifying clinical predictors of chemother-apy-related toxicity from GA in older adults with mixed tumour typeshave used heterogeneous methods and given varied results. [8,18] TheTUG has been associated with an increased risk of falls, [19] early mor-tality, [20] and functional decline [21] in older adults receiving chemo-therapy. It has been evaluated against chemotherapy toxicity in onlyone other study,where a cut off of N10 swas associatedwith severe tox-icity. [6] Impaired cognition has been associated with severe non-haematologic toxicity in one study of older adults with mixed tumourtypes [4] and with severe all-cause toxicity in those receiving chemo-therapy for colon cancer. [22] However, no association between cogni-tion and toxicity was found in studies of patients with mixed tumourtypes, [6,7] or in studies of patients with lung [23] or ovarian cancer.[24] Impaired social activity due to health (derived from the MedicalOutcomes Study social functioning item, and a component of theCARG Score), maintained predictive value for toxicity in our study.Thismeasure asks patients how often their health has limited their nor-mal social activities and so reflects global, and often subtle, impairmentsin multiple health domains, and so showed good discrimination withinour population selected for fitness for chemotherapy.

Strengths of our study include it being the first reported study test-ing the CARG Score in a heterogeneous cancer population outside ofthe United States and the largest study comparing oncologists' esti-mates of chemotherapy-related toxicity with a risk prediction tool. Pro-spective collection of toxicities at the point of care strengthens theoutcome data. Rather than use an ordinal scale, oncologists nominatedexpected toxicity rates (0–100%) for a group of similar patients toallow calibration between oncologists (what is low to one oncologistmay be considered moderate or high to another). Oncologists werealso blinded to patients' CARG Scores and GA results so that this

y = -0.033x + 35.983R² = 3E-05

0

10

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40

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60

70

80

90

0 2 4 6 8 10 12 14 16 18

Onc

olog

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stim

ate

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00%

)

CARG Toxicity Score (0 to 23)

Fig. 2. Relationship between Cancer and Aging Research Group's (CARG) toxicity score and oncologists' estimates of the likelihood of severe chemotherapy-related toxicity. Spearman’scorrelation coefficient r=−0.03.

30

25

20

15

10

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303540

2520151050

0 1 2 3 4 5 6 7 8

CARG Toxicity Score (0 to 23)

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atie

nts

Oncologists’ estimates (0 to 100%)

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0 3 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

A

B

Fig. 1. Distribution of the Cancer and Aging Research Group's (CARG) toxicity score (A)and oncologists' estimates (B) in study population (n= 126).

206 E.B. Moth et al. / Journal of Geriatric Oncology 10 (2019) 202–209

information did not influence their clinical assessments, planned che-motherapy, and study outcomes.

Limitations of our study include the modest sample size. A largersample sizewould have improved power to declaremodest associationsbetween measures of risk and toxicity as statistically significant, and togenerate a multivariate predictive model. The narrow 95% confidenceinterval surrounding our estimate of the AU-ROC for the CARG Scorefell entirely below that of Hurria et al.'s derivation study, [6] suggestingthat an increase in sample size would not significantly alter our conclu-sions regarding the predictive ability of the CARG Score in our popula-tion. The modest sample size and small number of centres (n = 2)and oncologists (n= 10) limits the wider generalisability of the results.We also recognise a lack of patient reported outcomes as a weakness ofthe study. Our primary outcome measure was the occurrence of anygrade 3 to 5 chemotherapy-related toxicity across the course of treat-ment which included haematological toxicities. Although haematologi-cal toxicitiesmay result in treatment delay ormodification, theymay beless important to patients. The prospective design of our studymayhaveinadvertently introduced bias to the testing of the CARG Score. Despiteblinding of oncologists to its results, learning of the CARG Score overtime by the involved clinicians and modification to planned treatmentor supportive interventions in line with expected risk category, cannot

be discounted. The proportion of high risk (by CARG Score) patients re-ceiving planned reduced dose chemotherapy was, however, low (25%),when compared to measures for which the oncologist was unblinded(rate of planned dose reduction in those ECOG-PS ≥2 of 75%, and inthose considered frail of 47%), suggesting that this was unlikely to bethe case.

The results of our study do not support the implementation of theCARG Score in routine practice in the local setting. The results do sug-gest a need for improved risk prediction, and education and support ofoncologists to improve their prediction of treatment toxicity for olderadults with cancer. The importance of validating geriatric assessmenttools in varied healthcare settings and geographic locations ishighlighted. Identifying strong and reproducible predictors of chemo-therapy toxicity in cohorts of older patients with varied cancer typestreated with varied chemotherapy regimens is problematic, so futurestudies could focus on single tumour types or treatment regimens.This would enable focus on the impact of regimen-specific lowergrade toxicities (such as capecitabine related diarrhoea, or taxane neu-ropathy). Additional research implications are the validity of other riskprediction tools in our population, and the optimal assessment ofolder adults who declined or were deemed unsuitable forchemotherapy.

1.00

0.75

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0.25 0.50 0.75 1.00

1- Specificity

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Area Under ROC Curve (CARG Score) = 0.5178 Area Under ROC Curve (Oncologists’ estimates) = 0.5210

Area Under ROC Curve (combined) = 0.5232

Combined model

CARG Score

A

C

B

Fig. 3. Predictive value for toxicity of the Cancer and Aging Research Group's (CARG) toxicity score (A), oncologists' estimates (B), and a combined model of the two (C) as modelled byReceiver operating characteristic (ROC) curves.

207E.B. Moth et al. / Journal of Geriatric Oncology 10 (2019) 202–209

5. Conclusion

Neither the CARG Score nor oncologists' estimates of toxicity basedon clinical judgement were significant predictors of severe chemother-apy-related toxicity in our cohort of older adults with a solid organ can-cer. Aspects of the GA, namely the Prolonged Timed Up and Go,impaired social activity due to health, and an abnormal OrientationMemory Concentration test, predicted severe chemotherapy-relatedtoxicity. Methods to improve risk prediction in the local setting areneeded.

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jgo.2018.08.010.

Disclaimers

Nil.

Authors' Contributions

Conception and design: EM, PB, BK, AM, and VN conceived thework. Allauthors were responsible for the subsequent design of the work.Data collection: EM and NS were responsible for data acquisition,Analysis and interpretation of data: EM and AM were responsible fordata analysis and initial interpretation of results. All authors were re-sponsible for final interpretation of results.Manuscript writing: EM prepared the first draft of the manuscript. Allauthors revised the manuscript.Approval of final manuscript: All authors approved the final version of themanuscript. All authors agreed to be accountable for all aspects of thework.

Ethics Approval and Consent to Participate

Ethics approval was granted by the Sydney Local Health DistrictHuman Research Ethics Committee of Concord Repatriation GeneralHospital (HREC/15/CRGH/102). Signed, informed consent was obtainedfrom all participants.

Conflict of Interest

A/Prof Beale receives compensation as a consultant forMerck Sharp andDohme (Aust) P/L, AstraZenica, and Roche Products P/L. The remainingauthors declare no conflict of interest.

Acknowledgement of Funding

This project was funded by a Sydney Local Health District CancerServices Research Grant (CIA Dr. EM, other investigators: BK, PLB, PG,VN). Dr. EMwas supported in thiswork by two PhD scholarships: a Uni-versity of Sydney Australian Postgraduate Award (APA), and PhDfunding support from Sydney Catalyst: the Translational CancerResearch Centre of Central Sydney and regional NSW, University of Syd-ney, NSW, Australia and Cancer Institute NSW.

Acknowledgements

This project was funded by a Sydney Local Health District CancerServices Research Grant (CIA Dr. EM). Dr. EM was supported in thiswork by two PhD scholarships: a University of Sydney Australian Post-graduate Award (APA), and PhD funding support from Sydney Catalyst:the Translational Cancer Research Centre of Central Sydney and regionalNSW, University of Sydney, NSW, Australia and Cancer Institute NSW.

References

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Table 3Predictors of severe chemotherapy-related toxicity.

Characteristic % with toxicity OR 95% CI P-value

Age group 65 to 69y70 to 74y75 to 79y≥80y

44%48%59%67%

0.400.450.72ref.

0.09–1.860.10–2.070.16–3.31–

0.45

ECOG PS 0 or 1≥2

52%50%

1.08 0.38–3.08 0.89

CSHA Frailty Fit to wellVulnerable to frail

51%55%

0.85 0.38–1.89 0.69

Cognition (OMC) Normal cognition (score 0 to 4)Needs evaluation (score ≥ 5)

47%71%

0.37 0.14–0.96 0.04

Nutrition (MNA-SF) Normal nutrition (score ≥ 12)At risk or malnourished (score b 12)

46%56%

0.69 0.34–1.40 0.30

Timed Up and Go b14 s≥14 s

47%79%

0.24 0.06–0.92 0.04a

G8 Not vulnerable (score N 11)Vulnerable (score ≤ 11)

43%56%

0.59 0.28–1.24 0.17

Geriatric assessment score Score 0 or 1Score 2 or 3Score ≥ 4

44%54%71%

0.330.47ref.

0.09–1.160.13–1.70–

0.20

Abbreviations: ECOG PS, Eastern Cooperative Oncology Group Performance Status; CSHA Frailty, Canadian Study of Health and Aging Frailty Index; OMC, Short Blessed OrientationMem-ory Concentration Test; MNA, Mini Nutritional Assessment Short Form.

a Maintained significance on multivariate analysis with forward selection.

208 E.B. Moth et al. / Journal of Geriatric Oncology 10 (2019) 202–209

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[27] Fillenbaum GG, Smyer MA. The development, validity, and reliability of the OARSmultidimensional functional assessment questionnaire. J Gerontol 1981;36(4):428–34.

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209E.B. Moth et al. / Journal of Geriatric Oncology 10 (2019) 202–209

Short communication

Oncologists' perceptions on the usefulness of geriatric assessmentmeasures and the CARG toxicity score when prescribing chemotherapyfor older patients with cancer

Erin B. Moth a,b,⁎, Belinda E. Kiely a,b, Natalie Stefanic b, Vasikaran Naganathan b,c, Andrew Martin b,Peter Grimison d, Martin R. Stockler a,b, Philip Beale a,b, Prunella Blinman a,b

a Concord Cancer Centre, Concord Repatriation General Hospital, Sydney, Australiab University of Sydney, Sydney, Australiac Centre for Education and Research on Ageing, Concord Repatriation General Hospital, Sydney, Australiad The Chris O'Brien Lifehouse, Sydney, Australia

a b s t r a c ta r t i c l e i n f o

Article history:Received 28 August 2018Received in revised form 5 November 2018Accepted 15 November 2018Available online 28 November 2018

Background: The use of geriatric assessment (GA) and the Cancer and Aging Research Group (CARG) ToxicityScore by Australian oncologists is low. We sought oncologists' views about the value of GA and the CARG Scorewhen making decisions about chemotherapy for their older patients.Methods: Patients aged ≥65 yrs. with a plan to start chemotherapy for a solid organ cancer underwent a GA andhad their CARG Score calculated. Results of the GA and CARG Score were provided to treating oncologists whothen completed a questionnaire on the value of these measures for each patient.Results: We enrolled 30 patients from eight oncologists. Patients had a median age of 76 years and most (77%)were ECOG performance status 0 or 1. Risk category for severe chemotherapy toxicity by CARG Score was lowin 7 patients (23%), intermediate in 18 (60%), and high in 5 (17%). The GA provided oncologists new informationfor 12 patients (40%), most frequently in the domains of function and nutrition. Knowledge of the GA promptedsupportive interventions for 7 patients (23%). Oncologists considered modifications to recommended chemo-therapy based on the CARG Score for 2 patients (7%) (one more intensive and one less intensive), and basedon GA for no patients. Oncologists judged the GA and CARG Score as useful in 26 (87%) and 25 (83%) patients,respectively.Conclusion: Although oncologists valued the GA and CARG Score, they rarely used them tomodify chemotherapy.The GA provided new information that prompted supportive interventions in one quarter of patients.

© 2018 Elsevier Ltd. All rights reserved.

Keywords:Decision-makingChemotherapy toxicityOlder adultElderly

1. Introduction

Determining the suitability of an older adult with cancer for chemo-therapy ideally involves geriatric assessment (GA) and the use of clinicalrisk prediction tools, both now recommended in international guide-lines. [1,2] GA identifies co-existent geriatric problems that can affecttolerance of anti-cancer therapies and prompt supportive interventions,[1,3] may alter treatment decisions, [4,5] and improve treatment toler-ance and completion. [5,6] The Cancer and Aging Research Group's(CARG) Toxicity Score [7,8] is a clinical risk prediction tool that mayaid decision-making about chemotherapy by estimating the likelihoodof severe chemotherapy toxicity in older adults. Eleven clinical and GAvariables are used to classify patients as low, intermediate, or high risk

of severe chemotherapy toxicity. [7,8] Despite their potential benefitsto patient care, use of the GA and CARG Score by Australian oncologistsis low. [9,10]

Barriers to the implementation of clinical tools are multifaceted.Accessibility, time burden, and resource availability were cited as themost frequent barriers to the use of a GA in a recent cross-sectional sur-vey of 69 Australian oncologists, [10] with most of these oncologistsagreeing that a GA would add to their clinical assessment (71%) andwould influence their clinical decision-making (65%). The CARG Scorehas been externally validated [7] and tested in a small number of exter-nal cohorts, [11–14] but there are no published studies on barriers to itsuse or its perceived value to oncologists and its potential to influencedecision-making.

We performed a prospective observational study evaluating theCARG Score and comparing it to oncologists' clinical judgement inpredicting for severe chemotherapy toxicity. [12] This parent study pro-vided the opportunity to prospectively determine the value of the GA

Journal of Geriatric Oncology 10 (2019) 210–215

⁎ Corresponding author at: Concord Cancer Centre, Building 76, Concord RepatriationGeneral Hospital, Hospital Rd, Concord, NSW 2139, Australia.

E-mail address: Erin.Moth@health.nsw.gov.au (E.B. Moth).

https://doi.org/10.1016/j.jgo.2018.11.0041879-4068/© 2018 Elsevier Ltd. All rights reserved.

Contents lists available at ScienceDirect

Journal of Geriatric Oncology

and CARG Score to oncologists prescribing chemotherapy for olderadults. Specific objectives of this substudy were to determine(i) oncologists' views on the usefulness of the GA and CARG Score, (ii)new information gained from the GA, and (iii) the potential impact ofthe GA and CARG Score on chemotherapy prescribing and patientmanagement.

2. Methods

2.1. Design and Participants

Participants of this substudywere the oncologists of a subset of olderadultswith cancer participating in the parent study described and refer-enced above. [12] Eligibility criteria for the parent study includedage ≥ 65 years, diagnosis of a solid organ malignancy (any type orstage), and plan to start an initial or new line of systemic cytotoxicchemotherapy.

Ethics approval for this study was provided by the Sydney LocalHealth District Human Research Ethics Committee of Concord Repatria-tion General Hospital (HREC/15/CRGH/102) and the study was open attwo cancer centres in Sydney, Australia.

2.2. Procedures

Oncologists determined plans for chemotherapy for each patient asper usual clinical practice. A trained study researcher (NS) or clinicianresearcher (EM) not involved in clinical care then completed a GA andcalculated the CARG Score for each patient. The GA was performed inthe outpatient clinic at an agreed time to minimise additional visits,and took b30 min. Geriatric health domains assessed were: functionalstatus by the Timed Up and Go, [15,16] Katz Index of Activities ofDaily Living, [17,18] OARS Multidimensional Functional Assessment In-strumental Activities of Daily Living, [19] the Medical Outcomes Study(MOS) Physical Functioning Scale, [20] and a self-reported history offalls; comorbidity by the Cumulative Illness Rating Scale in Geriatrics(CIRS-G); [21] cognition by the Short Blessed (Orientation MemoryConcentration) Test; [22] psychological health by the Geriatric Depres-sion Scale 5-Item Short Form (GDS-5); [23] social supports by themod-ified MOS Social Support Survey; [24] and nutrition by unintentionalweight loss and the Mini Nutritional Assessment Short Form (MNA-SF). [25]

For the first 126 patients, oncologists were blinded to the results ofthe GA and CARG Score and independently estimated the risk of severechemotherapy toxicity for each of their participating patients. Results ofthis first part have been published. [12] For the final 30 patients re-ported here, treating oncologists were provided with results of the GAand CARG Score as a pro forma written report (Supplementary 1) andthen invited to complete a study-specific questionnaire (Supplemen-tary 2). For all 156 patients, a chemotherapy treatment recommenda-tion had been made prior to the GA and CARG Score being performed.The GA and CARG Score results were presented to oncologists for thissubstudy prior to the commencement of chemotherapy or shortlyafter starting chemotherapy, as it was apparent from Part 1 that GAswere most feasibly performed on day 1 of treatment. [12]

2.3. Oncologist Questionnaire

The substudy questionnaire addressed themes of (i) new informa-tion gained from the GA; (ii) the impact of the GA and CARG Score onchemotherapy recommendations; (iii) GA-prompted interventions;and (iv) the usefulness and ease of interpretation of the GA and CARGScore (Supplementary 2). Of note, the impact of the GA and CARGScore on chemotherapy recommendations was evaluated retrospec-tively. Oncologists were first asked how their chemotherapy recom-mendation compared to standard treatment for a younger, fitterpatient with the same type and stage of cancer. Deviations from

standard treatment here were considered to have been made basedon usual clinical judgement. Oncologistswere then asked how their rec-ommendation would have been modified based on the (i) GA and (ii)CARG Score had they known these results prior to making a treatmentdecision. A post-hoc brief written survey asked oncologists to commenton the barriers to implementation of the GA and CARG Score in clinicalpractice.

2.4. Analysis

Questionnaire responses were described using frequencies and pro-portions (%). Proportions of responses within answer categories usingLikert scales were presented using stacked bar charts. A post-hoc analy-sis explored the relationship between a patient's CARG Score and theironcologist-rated CSHA Clinical Frailty Rating using a 2 × 3 contingencytable and Fisher's Exact Test. We aimed to enrol 50 patients to provide95% confidence intervals of estimated proportions of within +/− 15%.

3. Results

Between December 2016 and March 2017, eight oncologists pro-vided survey responses for 30 patients with a response rate of 100%.

Table 1Patient (n = 30) characteristics.

Characteristic Number (%)

Sex Male 19 (63)Female 11 (37)

Cancer centre Concord Cancer Centre 14 (47)The Chris O'Brien Lifehouse 16 (53)

Age 65 to 69 years 8 (27)70 to 74 years 4 (13)75 to 79 years 12 (40)≥80 years 6 (20)Median (years) 75.5 years

Employment status Retired or not working 28 (93)Working 2 (7)

Marital status Married/Common law (De-facto) 18 (60)Widowed 1 (3)Divorced/separated 7 (23)Single 4 (13)

Living arrangements Lives with others 7 (23)Lives alone 22 (73)Care facility 1 (3)

Language spoken at home English 22 (73)Non-English 8 (27)

Receiving community services Yes 5 (17)No 25 (83)

Cancer type Colorectal 14 (47)Ovarian 4 (13)Upper gastrointestinal⁎ 3 (10)Lung/pleura 3 (10)Prostate 3 (10)Bladder 1 (3)Other 2 (7)

Stage of cancer I 0 (0)II 1 (3)III 12 (40)IV 17 (57)

Line of treatment Neoadjuvant 2 (7)Adjuvant 8 (27)1st line palliative 13 (43)Subsequent line palliative 7 (23)

Chemotherapy regimen Single agent 13 (43)Combination chemotherapy 17 (57)

Primary G-CSF Yes 0 (0)No 30 (100)

Initial dose plan for cycle 1⁎⁎ Dose reduced 10 (33)Standard dose 20 (67)

⁎ Upper gastrointestinal includes pancreaticobiliary, gastric, and oesophageal cancers.⁎⁎ Initial dose plan for cycle 1 was defined as per the item of the Cancer and Aging Re-search Group's (CARG) Toxicity Score, as standard or reduced dose for that regimen ac-cording to NCCN guidelines.

211E.B. Moth et al. / Journal of Geriatric Oncology 10 (2019) 210–215

The sample size was smaller than planned due to resource availabilityand presentation of results for Part 1, in which the CARG Score did notpredict severe chemotherapy toxicity in our local population. [12]Given that this may have influenced oncologists' responses to the ques-tionnaire for the substudy, recruitment was stopped at 30 patients.

Of the eight oncologists, the median years in practice was 14 (range7–25 years),most (six of eight)workedmainly in the public setting, andnearly all (seven of eight) never used GA tools in their routine practice.Most (five of eight) reported patients ≥70 years comprising between50% and 75% of their practice.

Table 1 outlines patient characteristics. Table 2 outlines GA results.Oncologists rated most patients as fit or well (21, 70%), and a minority

as vulnerable or frail (9, 30%). Seven patients (23%) were classified aslow, 18 (60%) as intermediate, and 5 (17%) as high risk of severe chemo-therapy toxicity by CARG Score. The relationship between patients' CARGScore Risk Group and CSHA Clinical Frailty Scale is explored in Supple-mentary 3, with these measures being independent (p-value for Fisher'sexact test of 0.46). Treatment plans compared to standard treatment for ayounger, fitter patient with the same type and stage of cancer were: ‘nodifferent’ for 17 patients (57%); ‘same regimen at reduced dose’ for 4 pa-tients (13%); ‘less intensive regimen at standard dose’ for 5 patients(17%); and ‘less intensive regimen at reduced dose’ for 4 patients (13%).

The median time from clinic consultation to: (i) GA was 5.5 days(range 0–23 days); and (ii) start of chemotherapy was 6 days (range

Table 2Baseline geriatric assessment results (N = 30).

Characteristic Category N (%) Median Range Range of scores

Self-rated health Excellent or very good 13 (43)Good, fair or poor 17 (57)

CSHA Clinical Frailty Rating [30] Fit, or well 21 (70)Vulnerable or frail 9 (30)

Performance statusECOG Performance Status [31] 0 or 1 23 (77)

≥2 7 (23)Karnofsky Performance Rating Scale [32]90–100 11 (37)80 12 (40)≤70 7 (23)

Functional status Independent (score 6) 29 (97) 6 5–6 0–6Katz Activities of Daily Living [18]OARS Instrumental ADLs [19] Dependent ≥1 task 1 (3)

Independent (score 14) 19 (63) 14 4–14 0–14MOS Physical Functioning [20]Dependent ≥1 task 11 (37)Timed Up and Go [15]No limitation 1 (3) 26 15–30 10–30

Falls in last 6 months Some limitation 29 (97)≥14 s 4 (13) 11.7 s 7.3–21.5b14 s 21 (70)Yes 6 (20)

ComorbiditiesCIRS-G Total Score [21] 2 (7) 3.5 0–10

2 1–3CIRS-G IndexNumber with category 3 (severe) 2 (7)Number with category 4 (life threatening)Polypharmacy 2 0–11Number of medicationsSocial Supports Complete social supports 19 (63) 20 4–20 0–20

11 (37)Social Support Survey [24]Some deficit in support

Mood and Cognition Score 0 or 1 27 0 0–3 0–55-Item Geriatric Depression Scale [23] Score ≥ 2 (abnormal) (90) 2 0–14 0–30

Score 0 to 4 (normal) 3 (10)Orientation-Memory-Concentration Test [22]Score ≥ 5 25 (83)

5 (17)NutritionWeight loss in last 6 months Yes 22Mini-Nutritional Assessment Short Form [25] Normal nutrition (score 12 to 14) (73) 11 6–14 0–14

At risk (score 8 to 11) 13 (43)Malnourished (score 0 to 7) 16 (53)

1 (3)G8 [33] N14 11 (37)

≤14 (at risk) 19 (63)Geriatric assessment score* 0 or 1 15 (50)

2 or 3 14 (47)≥4 1 (3)

CARG Toxicity Score [8] Low risk 7 (23) 8 4–12 0–23Intermediate risk 18 (60)High risk 5 (17)

Abbreviations: CSHA- Canadian Study of Health and Aging; ECOG- Eastern Cooperative Oncology Group; OARS- Older Americans Resources and Services; ADL- Activities of Daily Living;MOS- Medical Outcomes Study; CIRS-G- Cumulative Illness Rating Scale in Geriatrics; G8- Geriatric 8; CARG Toxicity Score- Cancer and Aging Research Group's Toxicity Score.*Geriatric assessment score (range 0 to 7) is a summary score for the geriatric assessment performed, where a point is scored for a deficit in a geriatric health domain as follows:- performance status ECOG 2 or more- functional status: TUG N/= 14 s, any dependency in ADLs- nutrition: MNA at risk or malnourished- cognition: at risk or likely consistent with dementia- social supports: b18 (lowest quartile)- psychological state: GDS N/= 2- comorbidity: CIRS G score N 6 (highest quartile)

212 E.B. Moth et al. / Journal of Geriatric Oncology 10 (2019) 210–215

0–23 days). The median time from GA to the start date for chemother-apy was 0 days (range 0–7 days), with most performed on day 1 oftreatment. Though not mandated in the study design, for 11 patientsthe results of the GA and CARG Score were presented to their treatingoncologist prior to the start of planned chemotherapy.

The GA was consistent with oncologists' ‘overall clinical impression’for most patients (24, 80%). The GA provided oncologists with new in-formation for 12 patients (40%) as follows: functional status (n = 6),cognition (n = 3), psychological health (n = 3), polypharmacy (n =3), comorbidity (n = 2), nutrition (n = 6), and social supports (n =2). The GA triggered interventions not otherwise considered for sevenpatients (some with ≥1 intervention): social work (one patient); dieti-tian (four patients); psychologist or psychiatric service (one patient);medication review (one patient), and community services (onepatient).

Oncologists reported that they would have modified their chemo-therapy recommendation based on the GA for none of the 30 patients,and based on the CARG Score for 2 (7%) patients; one patient with aCARG Score of 4 (low risk) would have been changed to a more inten-sive regimen and a patient with a score of 12 (high risk) would havebeen changed to a less intensive regimen. (Fig. 1) Oncologists thoughtthe GA was useful for most patients (26, 87%) and easy to interpret

(29, 97%), and the CARG Score was useful for most patients (25, 83%)and easy to interpret (30, 100%). (Fig. 2) Perceived barriers to the imple-mentation of GA and CARG Score are in Table 3, with recurring themesbeing time and uncertainty about contribution to decision-making andusual practice.

4. Discussion

Key findings of this studywere that oncologists found the results of aGA and CARG Score useful for most patients but were unlikely to usethem to make changes to recommendations about chemotherapy. Pa-tients' performances on a GA were mostly consistent with the clinicalimpression of their treating oncologist, but for some the GA providednew information that prompted supportive interventions (in 23%).

The GA provided new information and prompted non-oncologicalsupportive interventions for one in four patients, lower than other stud-ies. In a recent systematic review by of 19 studies, Hamaker et al. [5]identified at least one non-oncological intervention occurred for a me-dian of 72% of patients (range 26 to 100%) undergoing GA in the oncol-ogy setting. Some studies reported any intervention following a GA, [26]whereas others required the intervention would not have happened aspart of usual care (as in our study), [27] in part explaining the wide

93

100

20

60

7

80

40

0% 50% 100%

Would you have modified your existing chemotherapyrecommendation on the basis of the patient's CARG

Toxicity Score?

Would you have modified your existing chemotherapyrecommendation on the basis of any of the information

gained from the geriatric assessment?

Was the information contained in the geriatric assessmentconsistent with your clinical impression?

Did the geriatric assessment provide you with any newinformation about your patient?

% of patientsNo Yes

Fig. 1. Perceived clinical value and impact on chemotherapy prescribing of the Cancer and Aging Research Group's (CARG) Score and Geriatric Assessment. For the 30 enrolled patients,their treating oncologist was asked to complete a questionnaire regarding the GA and CARG Score in that patient. The proportion of responses in each answer category reflect theproportion of patients for whom their treating oncologist answered ‘yes’ or ‘no’ to the presented questions.

7

3

10

7

7

80

60

77

67

7

37

7

33

useful

easy to interpret

I found the results of the geriatric assessment

I found the results of the geriatric assessment

I found the results of the CARG Score useful

I found the results of the CARG Score easy tointerpret

% of patients

Strongly disagree Disagree Neutral Agree Strongly Agree

Fig. 2.Oncologist ratings of ease of use of theCancer andAging ResearchGroup's (CARG) Score andGeriatric Assessment. For the 30 enrolled patients, their treating oncologistwas asked tocomplete a questionnaire regarding the GA and CARG Score in that patient. The proportion of responses in each answer category reflect the proportion of patients for whom their treatingoncologist agreed, on a 5-point Likert scale, with the statements presented.

213E.B. Moth et al. / Journal of Geriatric Oncology 10 (2019) 210–215

range of results. Oncologists in our study reported that the GA wasconsistent with their overall clinical impression for most patients. Thisimplies that, whilst not having formally assessed geriatric domains,oncologists had formed a clinical impression of these and were not sur-prised by their patient's performance on formal GA measures, and mayexplain the comparatively lower rate of interventions prompted by theGA in our study.

Oncologists would not use the results of the GA to modify theirchemotherapy recommendation in our study. Possible reasons for thisinclude choice-supportive bias (a reluctance to report a different deci-sion might have been present once a decision has already been made),lack of experiencewith GA, inconsistent evidence regarding the compo-nents of the GA that best predict treatment toxicity, [3,28] fear aboutunder-treatment, and again the reported consistency of the GAwith on-cologists' overall clinical impression. Using similar methodology,Decoster et al. [29] found cancer treatment plans for older adults(n = 902) were modified from standard therapy based on clinicaljudgement for 44% (43% in our study), with further changes based onGA in only an additional 6%. The recent review by Hamaker et al. [5]foundhigher rates of change in treatment decisions based on GA. Across11 studies that reported treatment choice before and after GA, a changein oncological management (not only chemotherapy) occurred for amedian of 28% of patients (range 8–54%), the majority to less intensivetreatment. Methodologic differences between the studies included inthis review, particularly with regard to the baseline treatment planused as the comparator, should be noted. For example, if the baselinetreatment planwas nominated prior to a cancer specialist seeing the pa-tient, anymodificationsmade based on clinical judgement thatwas sep-arate to the effect of the GAmight bemissed, overestimating the impactof the GA on treatment decisions.

To our knowledge, oncologists have not previously been asked to re-port on the impact of the CARG Score on their chemotherapy recom-mendation and prescribing. We have previously shown that expectedrates of chemotherapy toxicity influence chemotherapy recommenda-tions. [9] As a chemotherapy toxicity risk score, it would be anticipatedthat the CARG Score would have a similar influence. Three studies[11,12,14] have shown a lack of correlation between oncologists' esti-mates of the likelihood of chemotherapy toxicity and a patient's CARG

Score, raising potential for the score to be providing information differ-ent to clinical judgement. Oncologists in our study were unlikely to usethe CARGScore to guide chemotherapy treatment decisions. One reasonfor this is the observation that for 13 patients (43%) the chemotherapytreatment plan had already beenmodified based on clinical assessment.Other potential reasons proposed by the authors include lack of famil-iarity with the score, challenges translating the score into modificationsto chemotherapy, and uncertainty about its local applicability. Whilstour larger prospective study [12] did not demonstrate predictive valueof the CARG score in our population, oncologists in this substudy werenot aware of this when completing the questionnaires.

Nishijima et al. [14] determined the value of the CARG Score indecision-making about chemotherapy by assessing the agreement be-tween treatment decision (reduced or standard intensity chemother-apy) based on clinical impression and based on the CARG Score in 58older adults. An assumption of this study was that patients with aCARG Score ≥ 10 (high-risk) should be recommended reduced intensitychemotherapy. Patients who received standard intensity chemotherapy(based on oncologist impression) yet had a CARG Score ≥ 10 had higherrates of severe toxicity (88% v 40%, p= .006), suggesting the addition ofthe CARG Score to clinical judgement may improve treatment decision-making, at least for high-risk patients. Whether oncologists wouldmodify their treatment recommendations for these high-risk patients,considering there may be competing benefits of proceeding with stan-dard intensity treatment in some settings, is a question our study soughtin part to explore. For only one of the five patients in our study witha CARG Score of ≥10 would their oncologist have changed theirrecommendation to a less intensive chemotherapy regimen. This is arecognised area for further enquiry.

Strengths of this study include providing novel local data on thevalue and use of the GA and CARG Score and being the first study toseek to evaluate oncologist reported impact of the CARG Score on che-motherapy recommendations. Limitations include the small numberof oncologists (n = 8) and centres (n = 2), thus results are unlikely toreflect the views of all Australian oncologists practicing in varied geo-graphic settings and practice types. The oncologists involved did notroutinely use GAs or screening and so their viewsmay be biased againsttheir use. Choice-supportive bias is possible due to the design of thestudy, as oncologists responded to the questionnaire after they hadmade a recommendation about chemotherapy. The study focussedon the GA and CARG Score with respect to treatment decision-making but did not evaluate other potential benefits or uses of thesemeasures.

5. Conclusion

Oncologists found the results of a GA and CARG Score useful for theirolder patients commencing chemotherapy. The GA was consistent withoncologists' clinical impressions for most patients and provided new in-formation that prompted supportive interventions for 1-in-4 patients.Oncologists were unlikely to modify their chemotherapy recommenda-tions based on the GA or CARG Score, and as such their potential toimpact decision-making about chemotherapy prescribing in this settingwas low. Barriers to the use of such tools in routine practice, and theirrecommended role in guiding chemotherapy treatment decision-making and prescribing, need to be addressed to facilitate implementa-tion of these tools into routine clinical practice.

Ethics Approval and Consent to Participate

Ethics approval was granted by the Sydney Local Health DistrictHuman Research Ethics Committee of Concord Repatriation GeneralHospital (HREC/15/CRGH/102). Signed informed consent was obtainedfrom all participants.

Table 3Comments from oncologists on barriers to use of the GA and CARG Score.

Comments

What do you see as the main barriersto the implementation of a GA inclinical practice?

“Time limitation in a busy clinic, lack ofsupport staff and space to conduct theassessment”“Time”“Ease of access and time”“Limited clinic time”“Expertise and time”“Time, uncertain how formal assessmentwill change decision-making”“Time in a busy practice”“Uncertainty about its value/benefit; andtime”

What do you see as the main barriersto the implementation of the CARGScore in clinical practice?

“Time, practical aspects for example, itwould need to be added to the electronicmedical record for ease of completionand storage; training on how to completeit; not convinced it improves on currentpractice”“Time”“Time, space, and staff”“Limited clinic time”“Expertise and time”“Time, and uncertain if it adds to clinicalassessment”“Time in a busy practice”“Uncertainty about its value; and time”

Abbreviations: GA= Geriatric Assessment; CARG = Cancer and Aging Research Group.

214 E.B. Moth et al. / Journal of Geriatric Oncology 10 (2019) 210–215

Conflict of Interest

A/Prof Beale receives compensation as a consultant for Merck Sharpand Dohme (Aust) P/L, AstraZenica, and Roche Products P/L. The re-maining authors declare no conflict of interest.

Acknowledgement

This project was funded by a Sydney Local Health District CancerServices Research Grant (CIA Dr. EM, other investigators: BK, PLB, PG,VN). Dr. EM was supported in this work by two PhD scholarships: aUniversity of Sydney Australian Postgraduate Award (APA), and PhDfunding support from Sydney Catalyst: the Translational CancerResearch Centre of Central Sydney and regional NSW, University of Syd-ney, NSW, Australia and Cancer Institute NSW.

Authors' Contributions

Conception and design: EM, PB, BK, AM, and VN conceived thework. Allauthors were responsible for the subsequent design of the work.Data collection: EM and NS were responsible for data acquisition,Analysis and interpretation of data: EM and AM were responsible fordata analysis and initial interpretation of results. All authors were re-sponsible for final interpretation of results.Manuscript writing: EM prepared the first draft of the manuscript. Allauthors revised the manuscript.Approval of final manuscript: All authors approved the final version ofthe manuscript. All authors agreed to be accountable for all aspects ofthe work.

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Estimating survival time in older adults receiving chemotherapyfor advanced cancer

Erin B. Moth a,b,⁎, Prunella Blinman a,b, Natalie Stefanic b, Vasi Naganathan c,d, Peter Grimison b,e,Martin R. Stockler a,b, Philip Beale a,b, Andrew Martin b, Belinda E. Kiely a,b

a Concord Cancer Centre, Concord Repatriation General Hospital, Sydney, Australiab University of Sydney, Sydney, Australiac Centre for Education and Research on Ageing, Concord Clinical School, Faculty of Medicine and Health, University of Sydney, Australiad Ageing and Alzheimer's Institute, Concord Repatriation General Hospital, Sydney, Australiae Chris O'Brien Lifehouse, Sydney, Australia

a b s t r a c ta r t i c l e i n f o

Article history:Received 17 May 2019Received in revised form 26 August 2019Accepted 27 August 2019Available online xxxx

Purpose:We determined the accuracy of oncologists' estimates of expected survival time (EST) for older adultswith advanced cancer, and explored predictors of survival from a geriatric assessment (GA).Methods: Patients aged ≥65 years starting a new line of palliative chemotherapy were eligible. For each patient atenrolment, oncologists estimated EST and rated frailty (Canadian Study onHealth and Aging Clinical Frailty Scale,1 = very fit, to 7= severely frail), and a researcher completed a GA. We anticipated estimates of EST to be: im-precise [b33% between 0.67 and 1.33 times the observed survival time (OST)]; unbiased (approximately 50% ofparticipants living longer than their EST); and, useful for estimating individualised worst-case (10% living ≤¼times their EST), typical (50% living half to double EST), and best-case (10% living ≥3 times EST) scenarios for sur-vival time. Logistic regression was used to identify independent predictors of OST.Results: The 102 participants [median age 74 years, vulnerable to frail (4–7 on scale) 35%] had amedianOST of 15months. 30% of estimates of EST were within 0.67–1.33 times the OST. 54% of participants lived longer than theirEST, 9% lived ≤1/4 of their EST and 56% lived half to double their EST. Follow-upwas insufficient to observe thoseliving ≥3 times their EST. Independent predictors of OST were frailty (HR 4.16, p b .0001) and cancer type (p=.003).Conclusions: Oncologists' estimates of EST were imprecise, but unbiased and accurate for formulating scenariosfor survival. A pragmatic frailty rating was identified as a potentially useful predictor of OST.

© 2019 Elsevier Ltd. All rights reserved.

Keywords:SurvivalPrognosisCommunicationOlder adultElderly

1. Introduction

Oncologists are frequently asked to provide information about ex-pected survival times to patients with advanced cancer. This informa-tion helps patients and their caregivers make decisions abouttreatment, set goals, establish priorities, and make plans concerning fu-ture care. Prognostic information is desired by most patients. [1,2] Forolder adults with advanced cancer, decision-making about palliativesystemic treatment can be complex, particularly with regard tobalancing its potential benefits and harms in the setting of comorbidi-ties, coexistent functional impairments, and concern about treatmenttoxicity. Patient goals may also differ from younger patients, oftenwith an emphasis on maintenance of function over length of life. [3] Es-timates of survival time for patients with advanced cancer are

frequently imprecise, [4] with a tendency for health professionals tooverestimate survival time, especially for patients close to the end-of-life with a median observed survival of b90 days. [5–9] The heterogene-ity of older adults with cancer, their demonstrated poorer survival com-pared to younger patients, [10] and their relative under-representationin the pertinent clinical trials that often inform estimates of expectedsurvival times, [11,12] may alter the accuracy and nature of oncologists'estimates of expected survival time in this cohort.

We have previously shown that presenting prognostic informationto patients in the form of expected best-case, typical, and worst-casescenarios for survival time offers hope, reassurance, and is preferredover a single point estimate of median survival. [13] These scenariosfor survival time can be calculated using simple multiples of the ob-served median survival time in a clinical trial to estimate the outcomesof the other patients in that trial, or by using simple multiples of the ex-pected survival time estimated for an individual patient. [14–21] Ourprevious studies have shown that 5 to 10% of patients live ≥3 timesthe median survival time (best-case scenario), about 50% will live

Journal of Geriatric Oncology xxx (2019) xxx

⁎ Corresponding author at: Concord Cancer Centre, Building 76, Concord RepatriationGeneral Hospital, Hospital Rd, Concord NSW 2139, Australia.

E-mail address: Erin.Moth@health.nsw.gov.au (E.B. Moth).

JGO-00816; No. of pages: 9; 4C:

https://doi.org/10.1016/j.jgo.2019.08.0131879-4068/© 2019 Elsevier Ltd. All rights reserved.

Contents lists available at ScienceDirect

Journal of Geriatric Oncology

Please cite this article as: E.B. Moth, P. Blinman, N. Stefanic, et al., Estimating survival time in older adults receiving chemotherapy for advancedcancer, J Geriatr Oncol, https://doi.org/10.1016/j.jgo.2019.08.013

between half and double the median survival time (typical scenario),and 5 to 10% will live ≤1/4 times the median survival time (worst-casescenario). [21] The accuracy of using these simple multiples of anoncologist's estimate of “expected survival” to calculate expectedworst-case, typical and best-case scenarios for individual patients hasbeen demonstrated in patients with mixed advanced cancer types (me-dian age 64 years) starting palliative chemotherapy in routine practice.[21] How this method of using simple ‘multiples of the expected sur-vival time’ to calculate scenarios for survival applies to older adults re-ceiving chemotherapy for advanced cancer in everyday practice is notknown.

There may be other ways to inform survival times for older adultswith cancer. Prognostic tools in older adults have largely been devel-oped for use in the setting of hospitalisation, [22] in the community toinform decisions about cancer screening, [23,24] or for those with ad-vanced cancer close to the end-of-life. [25–27] An assessment of multi-ple health domains using a ‘geriatric assessment’ (GA) is recommendedfor older adults with cancer [28,29] although the particular componentsof the GA that are most predictive of survival are unclear. [30–32] Fromthe GA, a summarymeasure to indicate frailty can be derived which hasbeen shown to be predictive of all-causemortality. [33,34] Frailty can bethought of as a condition of decreased physiological reserve and abilityto withstand stressors. [35,36] Frailty can be determined without theneed for a GA which is time consuming. The Canadian Study on Healthand Aging (CSHA) Clinical Frailty Scale [36] is a subjective measure offrailty that has appeal for use in the oncology setting as a single-item,easy-to-use subjective tool that can be completed by the oncologist. Ithas been shown to predict mortality in general geriatric populations,[37–39] but there is a lack of evidence for its use in the oncology setting.

The primary objective of this studywas to determine the nature andaccuracy of oncologists' estimates of survival time for older adults withadvanced cancer being treated with chemotherapy. The secondary ex-ploratory objective was to identify useful predictors of observed sur-vival in this cohort, using measures from a brief geriatric assessment(GA), that included oncologists' assessments of frailty.

2. Methods

2.1. Participants

Participants were selected from a larger prospective study that com-pared oncologists' predictions of chemotherapy toxicitywith the Cancerand Aging Research Group's (CARG) Toxicity Score; and evaluated theimpact of the GA on decision-making about chemotherapy. [40,41] Par-ticipating patients in these studies (N=156) were aged ≥65 years, andstarting an initial or new line of cytotoxic chemotherapy for a solidorgan cancer of any type or stage, and were recruited consecutively attwo tertiary referral cancer centres in Sydney. For this substudy, onlypatients who had an incurable cancer were included. Patients withstage III disease were included if unable to tolerate curativemultimodality treatment. Supplementary Fig. 1 outlines the sample se-lection. There was no lower limit on anticipated survival time for pa-tients to be included in this study, though all had been judgedappropriate to receive chemotherapy. Participating oncologists werethe treating medical oncologists of participating patients. All patientsprovided written, informed consent. Ethics approval was granted bythe Sydney Local Health District's Human Research Ethics Committee(Concord Repatriation General Hospital), (HREC/15/CRGH/102).

2.2. Procedures

At enrolment, oncologists recorded an estimate of expected survivaltime (EST) in months for each patient (the estimated “median survivalfor a group of identical patients”). Oncologists also recorded Eastern Co-operative Oncology Group (ECOG) performance status [42] and frailtyby the CSHA Clinical Frailty Scale. [36] The CSHA Clinical Frailty Scale

asks clinicians to classify patients into one of seven categories rangingfrom ‘very fit’ to ‘severely frail’ using clinical judgement and guided bya description of general appearance, comorbidity, and comparison topeers. An abbreviatedGAwas then completedwith each patient by a re-searcher (EM, NS). Geriatric health domains assessed by GAwere: func-tional status by the Timed Up and Go, [43] Katz Index of Activities ofDaily Living, [44] OARSMultidimensional Functional Assessment Instru-mental Activities of Daily Living, [45] the Medical Outcomes Study(MOS) Physical Functioning Scale, [46] and history of falls; comorbidityby the Cumulative Illness Rating Scale in Geriatrics (CIRS-G); [47] cogni-tion by the Short Blessed (Orientation Memory Concentration) Test;[48] psychological health by the Geriatric Depression Scale 5-ItemShort Form (GDS-5); [49] social supports by the modified MOS SocialSupport Survey; [50] nutrition by the Mini Nutritional AssessmentShort Form (MNA-SF); [51] and chemotherapy toxicity risk by theCARG Toxicity Score. [52] Oncologists were not aware of the results ofthe GA when assessing EST and frailty. Data on survival was capturedprospectively.

2.3. Statistical Analysis

Survival analysis was conducted at a timepoint 13 months from thedate of enrolment of the last participant. We calculated the ratio of ob-served survival time (OST) to EST for each patient. The distribution ofOST/EST ratios was estimated from a Kaplan-Meier analysis to accom-modate instanceswhere OSTwas censored. The accuracy of oncologists'estimates of EST was determined by the proportions of patients withOSTs falling within simple multiples of their oncologist's estimate ofEST.We hypothesised that oncologists' estimates of EST would be unbi-ased, meaning approximately 50% of patients would live longer thantheir EST and 50% would live less than their EST. Based on previouswork, [15–18,21] we anticipated approximately 5 to 10% of patientswould die within one quarter of their oncologist's estimate (OST/ESTb0.25, worst-case scenario), 50% would live from half to double theironcologist's estimate (OST/EST between 0.5 and 2, typical range), and5 to 10% would live three or more times their oncologist's estimate(OST/EST of ≥3, best-case scenario).

For consistency with previous studies [5,15,21,53,54] we defined anoncologist's estimate of EST as ‘precise’ if it fell within 0.67 to 1.33 timesthe OST. This method allows a wider interval for precision for patientswith longer observed survival times, where estimates are inherentlymore uncertain, than for patients with shorter observed survival times.

Associations betweenOST and prespecified clinical andGA variables,including EST,were assessed using Cox proportional hazards regression.We dichotomised continuous variables for the regression analysis as fol-lows (cutpoints taken from sample medians): BMI b26 kg/m2 versus≥26 kg/m2; creatinine N78 mmol/L versus ≤78 mmol/L; andhaemoglobin b125 g/dL versus ≥125 g/dL. The ordinal scale of theCSHA Clinical Frailty Scale [36]was dichotomised for inclusion in the re-gression analysis as follows: “very fit”, “well”, and “well, with treatedcomorbid disease” (scores 1 to 3)were grouped as “fit towell”; and “ap-parently vulnerable”, “mildly frail”, “moderately frail”, and “severelyfrail” (scores 4 to 7) were grouped as “vulnerable to frail”. Variablesthat reached the 5% level of significance on univariable analysis were in-cluded in the multivariable analysis using stepwise backward elimina-tion. Oncologists' estimates of EST did not demonstrate significantvariance to justify use of a random effectsmodel. Relationships betweenvariables included in the regression analysis were explored usingSpearman's rank correlation coefficient.

3. Results

3.1. Participant Characteristics

Between August 2015 and March 2017, 102 patients with a medianage of 74 years (range 65 to 86) were enrolled by 10 oncologists at two

2 E.B. Moth et al. / Journal of Geriatric Oncology xxx (2019) xxx

Please cite this article as: E.B. Moth, P. Blinman, N. Stefanic, et al., Estimating survival time in older adults receiving chemotherapy for advancedcancer, J Geriatr Oncol, https://doi.org/10.1016/j.jgo.2019.08.013

centres. Patient characteristics are summarised in Table 1 and findingson GA in Table 2. The most common cancer type was colorectal (33%)followed by upper gastrointestinal (16%). Most patients were receivingpalliative chemotherapy in thefirst-line setting (67%). Oncologists ratedmost patients to be of good performance status (ECOG-PS 0/1, 80%), and‘fit to well’ (scores 1 to 3) on the frailty scale (65%). The frequency dis-tribution of ECOG-PS v CSHA Clinical Frailty Scale is in SupplementaryTable 1.

3.2. Observed and Estimated Survival Times

After amedian follow-upof 19months (range 0 to 27months), therewere 58 deaths (57%). One participant did not return for follow-up afterbaseline assessments. The median OST was 15 months (range 0.5 to27 + months). Fig. 1 shows the frequency distribution of oncologists'estimates of EST. The median estimate of EST was 15.5 months (range4 to 60 months). Most estimates of EST were multiples of 3 or

4 months (97%), or of 6 months (80%). The shortest EST of 4 monthswas for a 72 year-old male, ECOG-PS of 2, being treated with dose-reduced capecitabine/oxaliplatin for advanced colorectal cancer. Thelongest EST of 60 months was for a 70 year-old male, ECOG-PS of 0,starting standard dose docetaxel for advanced castrate resistant pros-tate cancer. Exploratory analysis revealed oncologists' estimates of ESTwere significantly associated with cancer type, ECOG-PS, CSHA ClinicalFrailty Scale, and CARG Toxicity Score.

3.3. Accuracy of Estimated Survival Time

Oncologists' estimates of EST were unbiased (no systematic ten-dency towards over-estimation or under-estimation), with 46% of pa-tients (95%CI 35 to 56%) living shorter than their EST and 54% (95%CI47 to 58%) living longer than their EST. Oncologists' estimates of ESTwere imprecise; only 30% of patients (95%CI 18 to 42%) had an estimateof EST within 0.67 to 1.33 times their OST. 9% of patients (95%CI 4 to14%) lived ≤ one quarter of their EST (worst-case scenario, expected5–10%); 56% of patients (95%CI 42 to 69%) lived half to double theirEST (typical scenario, expected 50%), and due to insufficient follow-up,no patients were observed to live three or more times their EST (best-case scenario, expected 5–10%). A plot of the relationship between ESTand OST is in Fig. 2. Kaplan-Meier curves of observed and expected sur-vival times are in Fig. 3A. Oncologists' estimates of EST had moderatediscriminative value (c-statistic of 0.64).

3.4. Predictors of Observed Survival Time

Predictors of OST on univariable and multivariable analysis areshown in Table 3. Independent predictors of OST were CSHA ClinicalFrailty Scale (HR 4.16, 95%CI 2.34–7.40, p b .0001) and cancer type (p= .003, hazard ratios for cancer types referent to prostate cancer areshown in Table 3). Therewas a 4-fold increase in the risk of death for pa-tients classified as ‘vulnerable to frail’ (CSHA Frailty Scale of 4–7) com-pared to patients classified as ‘fit, to well with treated comorbidity’(CSHA Frailty Scale of 0–3). Fig. 3B shows observed survival by frailtygroup. Patients with upper gastrointestinal, breast and lung cancershad N5-fold increase in the risk of death compare to patients with pros-tate cancer.

Because the accuracy of cancer type as a predictor of OST may be af-fected by the small numbers within each subgroup, a multivariableanalysis excluding cancer type was also performed. When cancertype was excluded CSHA Clinical Frailty Scale (HR 2.78, 95%CI1.57–4.93, p = .0004) and EST (HR 0.96, 95%CI 0.93–0.99, p = .03)were independently associated with OST. Here, the hazard ratio forEST represents a 4% reduction in the risk of death for every one monthincrease in estimated survival time.

4. Discussion

Oncologists' estimates of expected survival time for older adultscommencing chemotherapy for advanced cancer were unbiased (nosystematic tendency towards over- or under-estimation), yet as pointestimates were imprecise. Multiples of each individual's expected sur-vival time were accurate for estimating individualised typical (half todouble EST), and worst-case (≤ one-quarter EST) scenarios for survival.Follow-upwas too short to observe the accuracy of using ≥3 times EST todetermine the accuracy of the individualised best-case scenario; how-ever based on our previous studies, we expect this to also be accurate.[15,17,18] In this study, cancer subtype and a simple, pragmatic ratingof frailty by the treating oncologist were independently associatedwith OST.

Oncologists' estimates of expected survival time in our study wereunbiased but imprecise, consistent with studies in patients of all ageswith advanced cancer. [15,18,21,55] For example, in one study assessingthe accuracy of oncologists' estimates of survival time for 114 patients

Table 1Characteristics of 102 participants.

Characteristic Category N

Sex Male 65Female 37

Age (years) Median = 74 (65 to 86)65 to 69 2670 to 74 2875 to 79 36≥80 12

ECOG performance status(oncologist-assessed)

0 191 632 183 24 0

CSHA clinical frailty scale(oncologist-assessed)

1-Very fit 52-Well 323-Well, with treated comorbiddisease

29

4-Apparently vulnerable 305-Mildly frail 56-Moderately frail 17-Severely frail 0

Employment status Retired or not working 88Working 14

Marital status Married / de facto 71Widowed 10Divorced / separated 10Single 11

Living arrangements Lives with others 83Lives alone 19Care facility 0

Language spoken at home English 75Non-English 27

Community services Yes 10No 92

Cancer type Colorectal 34Upper gastrointestinal 16Prostate 13Gynaecological 10Lung or pleura 9Other genitourinary 9Breast 6Othera 5

Stage of cancer IIIb 9IV 93

Line of chemotherapy for metastaticdisease

1st line 68Subsequent line 34

Chemotherapy regimen Single agent 56Combination chemotherapy 46

Abbreviations: ECOG, Eastern Cooperative Oncology Group; CSHA, Canadian Study onHealth and Aging.

a Other cancer types included: glioblastomamultiforme [1], neuroendocrine tumour [2],adenocarcinoma unknown primary [1], merkel cell carcinoma unknown primary [1].

b Patients with stage III disease were included if they were unable to tolerate curativetreatment, and therefore the intent if chemotherapy was palliative.

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Please cite this article as: E.B. Moth, P. Blinman, N. Stefanic, et al., Estimating survival time in older adults receiving chemotherapy for advancedcancer, J Geriatr Oncol, https://doi.org/10.1016/j.jgo.2019.08.013

Table 2Geriatric assessment measures at baseline on 102 participants.

Characteristic Category n Median Range Range of scores

Self-rated health Excellent, or very good 34Good 42Fair, or poor 25

Functional statusKatz activities of daily living [44] Independent (score 6) 89 6 2–6 0–6

Dependent ≥1 task 13OARS instrumental ADLs [45] Independent (score 14) 56 14 4–14 0–14

Dependent ≥1 task 46MOS physical functioning [46] No limitation 96 26 14–30 10–30

Some limitation 7Timed Up and Go [43] ≥14s 12 secs

b14s 84Falls in last 6 months Yes 17

ComorbiditiesCIRS-G [47] Total Score 4CIRS-G index 1.67 0–12Number with category 3 (severe) 12 0.5–3Number with category 4 (life-threatening) 5

PolypharmacyNumber of medications 3.5 0–16

Social supportsSocial support survey [50] Complete social supports 60 20 4–20 0–20

Some deficit in support 42Mood and cognition

5-Item geriatric depression scale [49] Score 0 or 1 82 0 0–5 0–5Score ≥ 2 (abnormal) 20

Orientation-memory-concentration test [48] Score 0 to 4 (normal) 84 2 0–21 0–30Score ≥ 5 18

NutritionWeight loss in last 6 months Yes 59Mini-nutritional assessment short form [51] Normal nutrition (score 12 to 14) 49

At risk (score 8 to 11) 46Malnourised (score 0 to 7) 7

G8 Score [57] N14 38≤14 (at risk) 64

Geriatric assessment scorea 0 or 1 502 or 3 42≥4 10

CARG toxicity score [52] risk group Low-risk 20Intermediate-risk 60High-risk 22

a Geriatric assessment score (range 0 to 7) is a summary score for the geriatric assessment performed, where a point is scored for a deficit in a geriatric health domain as follows:- Performance status ECOG 2 or more- Functional status: TUG ≥ 14s, any dependency in ADLs- Nutrition: MNA at risk or malnourished- Cognition: at risk or likely consistent with dementia- Social supports: b18 (lowest quartile)- Psychological state: GDS ≥ 2- Comorbidity: CIRS G score N6 (highest quartile)

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Fig. 1. Distribution of oncologists' estimates of expected survival time (EST) for 102 older adults commencing palliative chemotherapy.

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Please cite this article as: E.B. Moth, P. Blinman, N. Stefanic, et al., Estimating survival time in older adults receiving chemotherapy for advancedcancer, J Geriatr Oncol, https://doi.org/10.1016/j.jgo.2019.08.013

with advanced cancer and a median age of 63 years [56] 29% of esti-mates met the same arbitrary definition of precision (within 0.67 to1.33 times the observed survival time). [15] Similarly, in two furtherstudies: one involving 152 patients with advanced gastric cancer andamedian age of 62 years, [18] and, the other in a cohort of routine prac-tice patients with advanced cancer (median age 64 years), [21] 29% ofsurvival estimates in both studies were precise by similar definitions.The consistent imprecision of a single point estimate of survival timeis a result of the inherent variability of survival time in the setting of ad-vanced cancer, regardless of patient age.

Medical oncologists' estimates of expected survival time in our studydid not show a tendency towards either optimism or pessimism. It isgenerally considered that oncologists have a tendency to overestimatesurvival, however we and others have shown this is not true, especiallyfor patients with recently diagnosed advanced cancers andmedian sur-vival times around 12months. [15,21,55] Overly optimistic estimates ofsurvival aremore frequent when patients are at the end of life with sur-vival times measured in days. [5,7] For example, in a study of 468 pa-tients referred to a hospice who had a median survival of 24 days, 63%of estimates of survival time made by referring physicians were too op-timistic (and17% too pessimistic). [5] Variation in biases of survival timeestimates between studiesmay also be explained, in part, by differencesin the expertise of clinicians providing the estimates. Number bias wasobserved in our study, with oncologists preferentially selecting esti-mates of expected survival time that were multiples of 3, 4 or6 months. This is revealing of human tendency to think in round num-bers (or multiples), to place patients within groups (longer and shortersurvivors), and also of these time points being commonly used in thereporting of survival in clinical trials.

Despite the imprecision of oncologists' point estimates of expectedsurvival time in our study, the proportion of patients with an OSTbounded by simple multiples of their EST was remarkably close towhat was expected from previous studies. [14–16,19–21] We expectedand found that approximately 5 to 10% of patients diedwithin one quar-ter of their oncologist's estimate (worst-case scenario), and 50% livedfrom half to double their oncologist's estimate (typical scenario). Withlonger follow-up we expect to find 5 to 10% lived three or more timestheir oncologist's estimate (best-case scenario). These typical, best-case, and worst-case scenarios provide a useful framework forexplaining survival time to patients. When an older patient with ad-vanced cancer requests information about their expected survivaltime, we recommend their treating oncologist start by estimating thepatient's expected survival time based on the estimated median in a

group of similar patients. Scenarios for survival time can then be calcu-lated using multiples of this estimate, as follows: best-case scenario ≥3times the estimate, typical scenario half to double the estimate, andworst-case scenario ≤1/4 times the estimate. To illustrate, if an oncolo-gist estimates the survival for a patient as 12 months, rather than pro-viding the patient this single number estimate they could explain thatthey would expect: 5–10 of 100 similar patients to live 36 months orlonger, the best-case scenario; about 50 of 100 similar patients to livebetween 6 and 24months, the typical scenario; and 5–10 of 100 similarpatients to die within 3 months, the worst-case scenario. We have pre-viously shown that patients prefer this format of explaining expectedsurvival time finding the three scenariosmakemore sense, offer greaterhope, and are more reassuring than a single point estimate of mediansurvival. [13]

While oncologists' estimates of EST were significantly associatedwith OST on univariable analysis, they were only independently associ-ated with OST when cancer type was not considered. Given we havepreviously shown oncologists' estimates of EST are independently pre-dictive of OST in several other cohorts, further evaluation in a larger co-hort of older patients is warranted.

The CSHA Clinical Frailty Scale, [36] a simple and pragmatic measureof frailty, was identified as an independent predictor of observed sur-vival in our study. Though frailty was retained as significant in themul-tivariable analysis over ECOG-PS, the hazards attributable to each in theunivariate analysis were comparable, and indeed there was an associa-tion between the two. Such a measure of frailty is appealing for use inclinical practice bynon-geriatricians, given it is easy to use, andprovidesa single number summary assessment that considers more than patientfunction. An interesting observationwas that of patients given anECOG-PS rating of 1 by their oncologist, one-quarter were rated ‘vulnerable tofrail’ suggesting the frailty scale may be better than performance statusin characterising a population of older patients. The frailty scale alsoproved more valuable for predicting survival in this cohort than anyone component of the GA, which may have implications for serviceswithout the resources for GA implementation, though recognising thata GA has value beyond that of informing prognosis.

4.1. Strengths and Limitations

The main strengths of our study are its prospective design, a priorihypotheses, and inclusion of ‘real-world’ older adults receiving chemo-therapy, rather than clinical trial participants, improving the applicabil-ity and generalisability of results to day-to-day clinical practice.

Patients who have died Patients who are still alive

Obs

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d su

rviv

al ti

me

(mon

ths)

Estimated survival time (months)

40

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00 6 12 18 24 30 36 42 48 54 60 66 72

Fig. 2.Relationship between observed and estimated survival times for each individual patient. Points falling on the 45-degree line represent peoplewho lived exactly as long as predicted.Points above the line represent people who lived longer than predicted, and points below the line represent people who lived shorter than predicted. Note those who were alive at lastfollow-up (represented by a triangle), maywith longer follow-up have an observed survival time that changes their position on the y-axis (and for some this maymove them from belowto above the line).

5E.B. Moth et al. / Journal of Geriatric Oncology xxx (2019) xxx

Please cite this article as: E.B. Moth, P. Blinman, N. Stefanic, et al., Estimating survival time in older adults receiving chemotherapy for advancedcancer, J Geriatr Oncol, https://doi.org/10.1016/j.jgo.2019.08.013

Collection of estimated survival times before starting a new line of che-motherapy is clinically relevant, as this is often a time when prognosticdiscussions take place. This study provides novel data on the value ofthe CSHA Clinical Frailty Scale for informing survival times in thispopulation.

The main limitations of our study are its modest sample size, whichlimits our ability to identify statistically significant associations between

baseline variables and survival, and limits the precision of our estimates.Longer follow-up would result in more precise estimates of observedsurvival time (44 of the 102 patients were still alive at the time of anal-ysis), and in particular would allow estimates of survival time in thosewho lived longest (only 2 deaths among 10 patients observed for30 months or longer). Patients in our study were being treated withchemotherapy, and hence results cannot necessarily be generalised to

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Apparently vulnerable, toseverely frail (CSHA ClinicalFrailty Scale 4-7)

Apparently vulnerable, to severelyfrail (CSHA Clinical Frailty Scale4 to7)

Very fit, to well with treatedcomorbidity (CSHA Clinical FrailtyScale 1-3)

Very fit, to well with treatedcomorbidity (CSHA Clinical FrailtyScale 1 to 3)

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Fig. 3.KaplanMeier curves of: A. observed and estimated survival times for 102 older adults starting palliative chemotherapy; B. observed survival times by frailty; C. estimates of expectedsurvival time by frailty. Estimates of uncertainty (95% CI) are provided for observed survival times.

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those older adults who decline chemotherapy or are not fit enough toreceive chemotherapy. The small number of oncologists (n = 10) andcentres (n = 2) in the study also limits the wider generalisability ofthe results. As each oncologist provided estimates of expected survivaltime for a small number of their patients, variation in estimates (andtheir accuracy) driven by individual oncologist factors was unable tobe adequately evaluated in this dataset. We also do not know if the es-timates of survival provided by oncologists in this study were commu-nicated to patients who requested prognostic information.

4.2. Implications for Practice and Future Research

When providing older patients with information about prognosis,oncologists should be aware of the imprecision of a single point esti-mate of expected survival time. Our study supports the use of thispoint estimate as a starting point for estimating anddescribing expectedbest-case, typical, and worst-case scenarios for survival time for olderadults with incurable cancer. A simple, single-item subjective rating offrailty, the CSHA Clinical Frailty Scale, [36] provided valuable prognostic

information in older adults starting chemotherapy, andmay perform aswell or better in everyday practice and clinical trials than traditionalmeasures of performance. The results of our study at least support fur-ther research to evaluate the utility of this frailty measure in practice.Further research should also study older patients' preferences for re-ceiving prognostic information, what informs oncologists' estimates ofsurvival time, and how knowledge of expected survival time impactson older patients' preferences for palliative systemic treatment.

5. Conclusion

Oncologists' estimates of expected survival time in older adults com-mencing chemotherapy for advanced cancer were unbiased, imprecise,and provided a useful basis for describing expected typical, best-case,andworst-case scenarios for survival time. Frailty, as assessed by oncol-ogists using the CSHA Clinical Frailty Scale, was identified as a predictorof observed survival time in this older population, and warrants furtherstudy.

Table 3Factors associated with observed survival time.

Variable Univariable analysis Multivariable analysis #1⁎

(all variables significant onunivariable analysis included)

Multivariable analysis #2⁎⁎

(all variables significant onunivariable analysis exceptcancer type)

HR 95% CI P HR 95%CI P HR 95%CI P

Estimate of expected survival time (EST) 0.94 0.91–0.98 0.001 – 0.96 0.93–0.99 0.03ECOG PS 0 or 1 0.34 0.19–0.62 0.0004 – –Age group (years) 65 to 69 0.58 0.23–1.39 0.63

70 to 74 0.69 0.29–1.6075 to 79 0.62 0.27–1.40≥ 80 Ref Ref

Cancer type Breast 5.12 1.37–-19.15 0.007 5.45 1.44–-20.6 0.003 NAColorectal 2.71 0.92–-8.00 2.75 0.93–-8.12Upper GIT 6.64 2.16–-20.41 6.36 2.06–-19.60Lung / pleura 3.79 1.07–-13.50 5.79 1.59–-21.09Gynaecological 1.2 0.27–-5.35 0.97 0.22–-4.37GU (other) 1.68 0.42–-6.72 1.91 0.48–-7.69Other 1.64 0.30–-8.98 1.29 0.24–-7.11Prostate Ref Ref Ref Ref

Line of therapy First line 1.63 0.90–2.94 0.12Haemoglobin‡ ≥125 (median) 0.58 0.34–0.98 0.04 – –Creatinine‡ ≤78 (median) 0.97 0.58–1.63 0.91CSHA frailty† Vulnerable to frail‡ 3.45 2.04–5.88 b0.0001 4.16 2.34–7.40 b0.0001 2.78 1.57–4.93 0.0004Self rated health Excellent or very good 0.59 0.31-1.13 0.04 – –

Good 0.45 0.24-0.85Fair or poor Ref Ref

Timed up and go b14 s 0.55 0.27–1.13 0.10Katz ADLs Independent in all ADLs 0.96 0.44–2.12 0.92IADLs Independent in all IADLs 0.48 0.28–0.81 0.006 – –Cognition (OMC) Normal cognition (score 0–4) 0.90 0.47–1.73 0.75Nutrition (MNA-SF) Normal nutritional screening 0.64 0.38–1.10 0.10BMI‡ ≥27 (median) 1.01 0.60–1.71 0.97Weight loss No weight loss 0.52 0.30–0.90 0.02 – –Comorbidity CIRS-G Score ≤ 4 0.65 0.38–1.09 0.10CARG toxicity score Low risk 0.42 0.19-0.95 0.02 – –

Intermediate risk 0.44 0.24-0.81High risk Ref Ref

g8 vulnerability score Not vulnerable (score N 11) 0.63 0.36–1.12 0.11Geriatric assessment score Score 0 or 1 0.47 0.27–0.81 0.006 – –

The hazard ratios reported represent the risk of death, such that a HR N1 represents an increased risk of death, and a HR b 1 represents a reduced risk of death.Abbreviations: NA = not analysed; ECOG-PS = Eastern Cooperative Oncology Group Performance Status; CSHA= Canadian Study on Health and Aging; ADL = activities of daily living;IADL= instrumental activities of daily living; OMC=orientationmemory concentration test; MNA=Mini Nutritional Assessment Short Form; BMI= BodyMass Index; CARG=Cancerand Aging Research Institute. Bold font applied to p-values b 0.05⁎ Multivariable analysis #1: All variables on univariable analysis significant at the 0.05 level of statistical significance were included.⁎⁎ Multivariable analysis #2: All variables on univariable analysis significant at the 0.05 level of statistical significance were included other than cancer type.† Categories on theCSHAClinical Frailty Scalewere dichotomised for the regression analysis as follows: “Veryfit”, “Well”, and “Well,with treated comorbid disease” on the CSHA Clinical

Frailty Scale grouped as “Fit to well”; “Apparently vulnerable”, “Mildly frail”, “Moderately frail”, and “Severely frail” on the CSHA Clinical Frailty Scale grouped as “Vulnerable to frail”.‡ Handling of BMI using clinical cutpoints [BMI ≥25 ‘overweight’ versus BMI b25 (only 1 patientwith BMI b20 ‘underweight’)] rather than the samplemediandid not alter this result (HR

0.94, 95%CI 0.41–2.2, p= .89). Handling of serum creatinine using clinical cutpoints for normal range (females b90,males ⟨110) rather than the samplemedian did not alter this result (HR0.76, 95%CI 0.25–2.27, p= .62). Handling of haemoglobin using clinical cutpoints for normal range (females ≥120, males ≥130) rendered the association non-significant (HR 0.50, 95%CI0.23–1.1, p= .08). This did not alter the independent predictors of survival identified on subsequent multivariable analysis.

7E.B. Moth et al. / Journal of Geriatric Oncology xxx (2019) xxx

Please cite this article as: E.B. Moth, P. Blinman, N. Stefanic, et al., Estimating survival time in older adults receiving chemotherapy for advancedcancer, J Geriatr Oncol, https://doi.org/10.1016/j.jgo.2019.08.013

Ethics Approval and Consent to Participate

Ethics approval was granted by the Sydney Local Health DistrictHuman Research Ethics Committee of Concord Repatriation GeneralHospital (HREC/15/CRGH/102).

Acknowledgement of Funding

Dr. EM was supported in this work by two PhD scholarships: a Univer-sity of Sydney Australian Postgraduate Award (APA), and PhD fundingsupport from Sydney Catalyst: the Translational Cancer Research Centreof Central Sydney and regional NSW, University of Sydney, NSW,Australia and Cancer Institute NSW.

Authors' Contributions

Conception and design: EM, PB, BK, AM, and MS conceived the work.EM, PB, VN, and BK designed the work.Data acquisition: EM, PB, NS, BK, MS, P Beale, PG.Analysis and interpretation of data: EM, AM, and BK were responsiblefor data analysis and initial interpretation of results. All authorswere re-sponsible for final interpretation of results as presented in themanuscript.Manuscript writing: EM prepared the first draft of the manuscript. Allauthors revised the manuscript.Approval of final manuscript: All authors approved the final version ofthe manuscript. All authors agreed to be accountable for all aspects ofthe work.

Disclaimers

Nil.

Declaration of Competing Interest

A/Prof Beale receives compensation as a consultant forMerck Sharp andDohme (Aust) P/L, AstraZenica, and Roche Products P/L. The remainingauthors declare no conflict of interest.

Acknowledgements

This project was funded by a Sydney Local Health District Cancer Ser-vices Research Grant (CIA Dr. EM). Dr. EM was supported in this workby two PhD scholarships: a University of Sydney Australian Postgradu-ate Award (APA), and PhD funding support from Sydney Catalyst: theTranslational Cancer Research Centre of Central Sydney and regionalNSW, University of Sydney, NSW, Australia and Cancer Institute NSW.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jgo.2019.08.013.

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Older adults' preferred and perceived roles in decision-making aboutpalliative chemotherapy, decision priorities and information preferences

Erin B. Moth a,b,⁎, Belinda E. Kiely a,c,d, Andrew Martin d, Vasi Naganathan b,e,f, Stephen Della-Fiorentina c,g,Florian Honeyball h, Rob Zielinski i, Christopher Steer j, Hiren Mandaliya c,Abiramy Ragunathan c, Prunella Blinman a,b

a Concord Cancer Centre, Concord Repatriation General Hospital, Concord, NSW, Australiab Concord Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australiac Macarthur Cancer Therapy Centre, Campbelltown Hospital, Campbelltown, NSW, Australiad National Health and Medical Research Council, University of Sydney, Sydney, NSW, Australiae Centre for Education and Research on Ageing, Concord Repatriation General Hospital, University of Sydney, Sydney, NSW, Australiaf Ageing and Alzheimer's Institute, Concord Repatriation General Hospital, Sydney, NSW, Australiag Southern Highlands Cancer Centre, Bowral, NSW, Australiah Alan Coates Cancer Centre, Dubbo Base Hospital, Dubbo, NSW, Australiai Central West Cancer Care Centre, Orange Base Hospital, Orange, NSW, Australiaj Border Medical Oncology, Albury Wodonga Regional Cancer Centre, Albury, NSW, Australia

a b s t r a c ta r t i c l e i n f o

Article history:Received 2 May 2019Received in revised form 4 July 2019Accepted 30 July 2019Available online xxxx

Aim: Patients with cancer have varied preferences for involvement in decision-making. We sought older adults'preferred and perceived roles in decision-making about palliative chemotherapy; priorities; and information re-ceived and desired.Methods: Patients ≥65y who hadmade a decision about palliative chemotherapy with an oncologist completed awritten questionnaire. Preferred and perceived decision-making roles were assessed by the Control PreferencesScale.Wilcoxon rank-sum tests evaluated associationswith preferred role. Factors important in decision-makingwere rated and ranked, and receipt of, and desire for information was described.Results: Characteristics of the 179 respondents: median age 74y, male (64%), having chemotherapy (83%), vul-nerable (Vulnerable Elders Survey-13 score ≥ 3) (52%). Preferred decision-making roles (n = 173) were activein 39%, collaborative in 27%, and passive in 35%. Perceived decision-making roles (n = 172) were active in42%, collaborative in 22%, and passive in 36% and matched the preferred role for 63% of patients. Associatedwith preference for an active role: being single/widowed (p= .004, OR= 1.49), having declined chemotherapy(p = .02, OR = 2.00). Ranked most important (n = 159) were “doing everything possible” (30%), “my doctor'srecommendation” (26%), “my quality of life” (20%), and “living longer” (15%). A minority expected chemother-apy to cure their cancer (14%). Most had discussed expectations of cure (70%), side effects (88%) and benefits(82%) of chemotherapy. Fewer had received quantitative prognostic information (49%) than desired thisinformation (67%).Conclusion: Older adults exhibited a range of preferences for involvement in decision-making about palliativechemotherapy. Oncologists should seek patients' decision-making preferences, priorities, and informationneeds when discussing palliative chemotherapy.

© 2019 Elsevier Ltd. All rights reserved.

1. Introduction

With an ageing population, oncologists are seeing increasing num-bers of older adults with incurable cancer who require discussions and

decisions about anti-cancer treatment. Such decisions ideally reflect acollaborative process between patient and oncologist with consider-ation of potential harms and benefits, provision of adequate informationabout the cancer and expected outcomes, acknowledgement of patientgoals and priorities, and incorporation of patient preferences. This isthe framework provided by the model of shared decision-making(SDM) [1–3]. SDM is increasingly advocated in cancer care [3,4], espe-cially where theremay bemore than one acceptable option or approach

Journal of Geriatric Oncology xxx (2019) xxx

⁎ Corresponding author at: Concord Cancer Centre, Concord Repatriation GeneralHospital, Concord, NSW, Australia

E-mail address: erin.moth@health.nsw.gov.au (E.B. Moth).

JGO-00803; No. of pages: 7; 4C:

https://doi.org/10.1016/j.jgo.2019.07.0261879-4068/© 2019 Elsevier Ltd. All rights reserved.

Contents lists available at ScienceDirect

Journal of Geriatric Oncology

Please cite this article as: E.B. Moth, B.E. Kiely, A. Martin, et al., Older adults' preferred and perceived roles in decision-making about palliativechemotherapy, decisi..., J Geriatr Oncol, https://doi.org/10.1016/j.jgo.2019.07.026

to treatment, or where treatment decisions are sensitive to the prefer-ences and priorities of patients. This is often the case with older adultsconsidering palliative chemotherapy for incurable cancer.

Whilst SDM promotes a collaborative approach to decision-making,[5] preferences for involvement in decision-making of patients withcancer range from active through to passive roles [6]. The preferreddecision-making roles of older adults however is unclear. Studies in-cluding patients of all ages in the setting of early breast cancer [7–9]and mixed advanced cancers [10,11] have found that increasing agewas associated with a preference for a passive role. Two studies per-formed specifically in older adults with advanced cancers found con-trasting results. One, a study of older adults with advanced colorectalcancer, showed about half of patients (52%) preferred a passive role[12], whereas a recent study reporting qualitative interviews of olderadults with advanced cancer of mixed types found most preferred toplay an active role [13]. There is evidence to support greater decisionsatisfaction where patients are able to play the role in decision makingthat they prefer [14,15], but none on whether or not older adults actu-ally played the role they prefer in decisions about chemotherapy.

Decisions about palliative chemotherapy are influenced by apatient's priorities, their understanding of their cancer, the proposedtreatment, and likely prognosis. Whilst oncologists frequently prioritiseperformance status as the most important factor when making recom-mendations about palliative chemotherapy for older adults [16,17],older adults have different priorities such as maintenance of functionand quality of life [18,19]. Patients with advanced cancer may not un-derstand the goals of palliative chemotherapy nor their prognosis [20],and may not always recall what their oncologist has told them [21].Being adequately informed about prognosis and the goals of treatment,however, allows patients to come to a decision that is consistent withtheir priorities. Understanding the perspectives of older adults whohave recently made such decisions is an important step in betterinforming and improving treatment decision-making in this population.

The aim of this studywas to determine older patients' preferred rolein decision-making about palliative chemotherapy and to compare it tothe role they perceived they had played. Secondary objectives were todetermine (i) factors associated with preference for an active role indecision-making and for achieving preferred role; (ii) factors consid-ered important in decision-making about palliative chemotherapy;(iii) patients' understanding of their cancer, its treatment, and survivaltime; and (iv) information needs.

2. Methods

2.1. Study Design and Setting

A cross-sectional survey study was conducted across two metropol-itan and four regional cancer centres in New South Wales, Australia.Ethics approval for the study was provided by the Sydney Local HealthDistrict Human Research Ethics Committee of Concord RepatriationGeneral Hospital (HREC/15/CRGH/208).

2.2. Participants

Eligibility criteria included patients aged ≥65 years with a new diag-nosis of an advanced solid organ cancer, who had seen a medical oncol-ogist and discussed palliative chemotherapy as a treatment option. Adecision regarding chemotherapy had to have been made in the last12 weeks. Adequate English language proficiency was also required.

2.3. Participant Questionnaire

2.3.1. Distribution and DesignThe study questionnaire was piloted on a focus group of 6 older

adults with advanced cancer. Feedback on content and readability wasobtained and incorporated into the final version.

Potential participants were identified through their treatingoncologist, aswell as reviewof outpatient clinic referrals by study inves-tigators. Questionnaires were distributed in person by either treatingoncologists, or a research officer, nurse, or trainee oncologist (site spe-cific) who was trained in the study inclusion criteria, and who may ormay not have been involved in the patient's care. The number of ques-tionnaires distributed at each site was recorded. Questionnaires wereprovided in a sealed, take-home envelope and responses were anony-mous. Completion and return of the questionnaire constituted consentto participate. Questionnaires could be returned by reply-paid envelope,or in person to the cancer centre.

Participating oncologists were familiar with the content of the ques-tionnaire. Study posterswere displayed in clinical areas at each site, andmonthly returned survey tallies sent to investigators to encourage re-cruitment. The survey was distributed as an anonymous survey withwaiver of face-to-face consent to allow ease of distribution at multiplecentres that were geographically distant, with varied study-specificresources.

2.3.2. Patient CharacteristicsSociodemographics, tumour, and treatment details were obtained

by self-report. Patient-rated global quality of life was measured by vi-sual analogue scale, with anchor points defined as “worst possible”and “best possible” quality of life. Patient-rated performance status(Pt-PS) [22] required participants to select one of five statements thatbest described their function, ranging from “normalwith no limitations”to “pretty much bedridden, rarely out of bed.” The Vulnerable Elders'Survey (VES-13), [23] was used as a measure of vulnerability, with an“at risk” cut-off score of ≥3 [23,24].

2.3.3. Involvement in Decision-makingParticipants' preferences for involvement in decision-making were

assessed using the validated Control Preferences Scale (CPS) [9,25].The CPS asks participants to select one of five statements, A to E, thatbest describes their preferred role in decision-making from an activerole (A = “I prefer to make the final selection about which treatment Iwill receive”) to a passive role (E = “I prefer to leave all decisions re-garding treatment to my doctor”). Participants' preferred role was ob-tained by asking which of the responses on the CPS best described therole they preferred to play in decision-making about chemotherapy.Participants' perceived role was obtained by asking which role best de-scribed the role they perceived they had played, on a version of theCPS modified to past tense [26]. (Supplementary Table 1).

2.3.4. Decision Satisfaction, Factors Influencing Decisions, andInformation Needs

Decision satisfaction was determined using the 6-item Satisfactionwith Decision Scale (SWDS) [27]. The importance of 12 pre-specifiedfactors inmaking decisions about palliative chemotherapywere elicitedby a 4-point Likert scale (from “not at all important” to “very impor-tant”) with participants also asked to rank one of these factors as themost important factor in decision-making. The 12 factors affectingdecision-making about chemotherapywere selected by the study inves-tigators following literature review, and no changes to these factorswere suggested by the patient focus group during questionnaire design.The importance of the opinion of ‘significant others’ was assessed on a5-point scale [28] with responses ranging from “I do not care at allabout their opinion” to “I take their opinion very seriously”. Participantswere also asked if their doctor discussed (i) if their cancerwas able to becured, (ii) was expected to shorten their life, (iii) the length of time theymay live, (iv) the benefits and risks of chemotherapy, and then if theyhad desired this information (yes/no). Participants were asked “whatis your understanding of how long you have to live?” with response asquantitative estimate of survival time in months or years.

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2.4. Statistical Analysis

The populationwas described using frequencies and proportions (%)for categorical variables, and means and medians for continuous vari-ables. For Likert scales, the proportion of responses in each answer cat-egorywasdetermined, andmean ratings calculated by assigning ordinalvalues to each answer category.

Responses on the CPS and modified CPS were categorised into threedecision-making roles: active (responses A and B), collaborative (re-sponse C), and passive (responses D and E). Proportions of patientswith responses within these categories were described for preferredand perceived roles. The five possible responses on the CPS andmodifiedCPS were then assigned ordinal scores from 1 to 5 (1=most active, 2=active, 3= collaborative, 4= passive, 5=most passive) to measure thedifferencebetweenpreferred andperceived role,with 0 indicatingnodif-ference and±4 indicatingmaximal difference. Differences between pre-ferred and perceived roles were evaluated using the Wilcoxon signed-rank test (omitting ties). Factors associated with preferred role were de-termined using theWilcoxon rank sum test (Mann-WhitneyU test)withordinal scale, and preference for an active role (over collaborative or pas-sive) summarisedbyodds ratios.Associationswith concordancebetweenpreferred and perceived roles were assessed using Chi-squared tests ofassociation and summarised by odds ratios. Agreement between receiptand desire for items of information were assessed using Cohen's kappa.

We aimed to include responses from 200 patients across all sites,with a study of N=200 providing for confidence intervals of estimatedproportions of +/− b7%.

3. Results

323 surveys were distributed, with 179 surveys returned and in-cluded in the analysis, giving a response rate of 55%. Surveys with oneor more missing or incomplete responses were included, with the me-dian number of missing responses per item on the questionnairebeing seven, (range 1 to 20). The number of complete responses per sur-vey item is detailed with presentation of the results.

3.1. Patient-Reported Measures, Demographic and Clinical Details

Patient demographics, clinical details, and patient-reported mea-sures are summarised in Table 1. The median age was 74 years (range65 to 92 years), and most (148, 83%) had decided to have chemother-apy. Patient-reported global quality of life was good (median of 68 ona VAS scale of 0 to 100; range 1–100). Most patients classified them-selves as of good performance status (Pt-PS 0 or 1 in 126, 72%), thoughhalf (92, 52%) were vulnerable by VES-13 (score of ≥3). Decision satis-faction was high (mean SWDS= 4.52) with little variance (IQR 4 to 5).

3.2. Preferred and Perceived Roles in Decision-Making

Preferreddecision-makingroles (n=173)wereactive in39%, collab-orative in 27%, and passive in 35%. Perceived decision-making roles (n=172)were active in 42%, collaborative in 22%, and passive in 36% (Fig. 1).Preferred and perceived decision-making roleswere concordant for 63%(n=109) of participants (Table 2). For those where preferred and per-ceived roleswere discordant (n=63),most (n=43) had a discrepancyscorebetweenrolesof+/−1, representinga singlemove ineitherdirec-tion on the CPS. Near equal proportions perceived having played amoreactive role than preferred (19%), as perceived having played amore pas-sive role thanwas preferred (18%) (Supplementary Table 2).

The assessment of factors associated with preferred role in decision-making is summarised in Supplementary Table 3. Preferring a more ac-tive role was associated with being single/widowed [WRS p= .004, OR1.49 (for active role over passive/collaborative)] and declining chemo-therapy (WRS p= .02, OR 2). The assessment of associations of achiev-ing the desired role (concordance between preferred and perceived

roles) is summarised in Supplementary Table 4. University educatedwere less likely to report achieving their desired role [Chi-test p =.002, OR 0.32 (for concordance)], and those who were married/in a defacto relationship were more likely to report achieving their desiredrole (Chi-test p = .02, OR 2.24).

3.3. Factors Important in Decision-making

The five factors rated as the most important in making a decisionabout palliative chemotherapy were “my quality of life” (mean

Table 1Respondent characteristics (n = 179).

Characteristic Number %d

Age, median (range) 74y (65 to 92)Cancer centre Metropolitan 95 53

Regional 84 47Sex Male 114 64

Female 64 36Cancer type Lung / pleura 41 23

Colorectal 40 23Prostate 33 19Upper gastrointestinal 18 10Gynaecological 13 8Genitourinary (non-prostate) 8 4Breast 7 4Othera 17 9

Marital status Married / de facto 123 69Separated / widowed / single 55 31

Language spoken at home English 168 94Non-English 10 6

Living arrangements Lives alone 34 19Lives with others 144 81

Children Yes 163 92Dependent children Yes 6 3Educational status Schooling 98 56

Trade or technical qualification 56 32University or college degree 23 13

Employment status Retired or on a pension 139 78Employed 38 21Unemployed 1 1

Close friend / relativedied from cancer

Yes 146 83

Friend / relative availablefor care

None of the time 10 6Some of the time 42 24Most, or all of the time 125 70

Time to travel to cancercentre

≤1 h N 1 h 14632

8218

Having chemotherapy Yes 148 83No 23 13Unsure 7 4

Significant other presentat most recentconsultationb

On my own 25 14Partner or spouse 110 62Other family, friend, or carer 85 24

Patient-ratedperformance status[22]

Normal, no limitations 30 17Not normal self, up and aboutmost of the day

96 55

Not feeling up to most things, butin bed or chair less than half ofday

33 19

Little activity, most of day in bedor chair

17 10

Pretty much bed-ridden, rarelyout of bed

0 0

Global quality of life scoreVAS

(mean) 67 (1−100)

Vulnerable Elders Survey(VES-13) [23]

VES-13 of 0, 1 or 2 85 48VES-13 of ≥3 (vulnerable) 92 52

Satisfaction with decisionscalec [27]

(mean) 4.52 (4–5)

a Other includes: head and neck [4]; brain [1]; unknown primary [6]; anal [1]; ‘unsure’[1]; ‘liver’ [3]; and ‘lymph nodes’ [1]

b Percentages do not total 100%, as more than one significant other could be selected;c Range from 0 to 5, where 5 is greatest satisfaction.d Percentages not inclusive of missing responses (maximummissing responses for any

patient characteristic tabled was 3).

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importance rating of 2.88 on a scale of 0 to 3), “my doctor's recommen-dation” (2.84), “the benefits of chemotherapy” (2.72), and “doing every-thing possible to fight the cancer” (2.71), with 80% or more ofrespondents rating these as “very important”. The single most importantfactor (n=159) in decreasing frequencywere “doing everything possi-ble to fight the cancer” (30%), “my doctor's recommendation” (26%),“my quality of life” (20%), and “living longer” (15%). Only one-third ofrespondents considered “how old I am” to be very important to theirdecision-making about chemotherapy. (Table 3).

Significant others whose opinions were rated as most importantwere the cancer specialist (mean importance rating of 4.71 on a scaleof 1 to 5), general practitioner (4.37), and partner or spouse (4.08).(Fig. 2).

3.4. Information Needs and Understanding

Participants' expectations of palliative chemotherapy are outlined inFig. 3. A minority (24, 14%) expected palliative chemotherapy to curetheir cancer. Agreement between information received and desired ispresented in Supplementary Fig. 1. Of those (108 of 162, 67%) who de-sired quantitative information on expected survival time, 69% (74 of108) received it. When asked about their understanding of expectedsurvival time, over half (99 of 170, 58%) stated they “did not know”,and one-fifth (34 of 170, 20%) preferred not to answer. One-third (33of 99) of participants who “did not know” had discussed expected sur-vival time with their oncologist. A minority (20 of 167, 12%) of partici-pants provided a quantitative estimate of their expected survival time,themedian estimate being 23months (range 3 to 120 months). Havingdiscussed the concept of cure with their oncologist was not associatedwith the expectation of cure (p = .55).

4. Discussion

When making decisions about palliative chemotherapy, an equalproportion of older adults preferred an active or passive role, and leastpreferred a collaborative role. Most older adults reported playing theirpreferred role in decision-making. ‘Doing everything possible’ was themost frequently ranked number one consideration for decision-making about chemotherapy. The opinion of the cancer specialist andGP were held in high regard. Some patients were uncertain about thetreatment goals of palliative chemotherapy, and many received less in-formation from their oncologist than they desired, particularly aboutprognosis.

In contrast to our results for preferred decision-making roles, priorstudies performed in patients of all ages with predominantly earlybreast or prostate cancers, showed most patients preferred a collabora-tive role [6] and older age predicted a preference for amore passive role.[7–11] In our study of older adults, older age (≥75 years versusb75 years)was not associatedwith preferred role. Elkin et al. [12] deter-mined the decision-making preferences of 73 older adults (aged≥70 years) with metastatic colorectal cancer where over half (38 of73, 52%) preferred a passive role. Contrasting with this, in a decade-later study by Puts et al. [13] reporting on qualitative interviews in 32older adults (aged ≥70 years) who had made a decision about chemo-therapy for varied advanced cancers, most (12 of 29, 41%) preferredan active role. The small sample size of these studies and the fact thatthey were conducted years apart (cohort effect) may explain the differ-ence in role preferences between these studies.

The rates of concordance between preferred and perceived decision-making roles in our study (63%) were within the ranges found in a sys-tematic review by Tariman et al. [29] of between 42 and 72%, andwherethere was discrepancy between preferred and perceived roles, this waslow (that is, the role thatwas playedwas close to the preferred role). In-terestingly, those in our studywhoweremarriedweremore likely to re-port achieving their desired decision-making role, and were less likelyto prefer playing an active role. This may reflect a tendency over a life-time of partnership to involve others in decision-making, althoughthis has not always been observed. [11,26,30–33] Having declined che-motherapy predicted a preference for a more active role, perhaps indi-cating a group of older adults who have upheld their preferencesabout treatment. To our knowledge, patient-rated performance statushas not been evaluated as a determinant of role preference previously,and was not associated with preferred role in our study. The recognisedlack of consistency in preferred roles and predictors of preferreddecision-making roles across studies, [6,34] and that role preferencesmay change over time, [31,35–37] supports the need to elicit patients'role preferences at regular clinical encounters. Helping patients achievetheir desired role in decision-making reduces anxiety about decisions,[38] improves patient satisfaction and reduces decisional conflict.[14,15]

Older adults in our study were motivated to have chemotherapy,wanted to live longer, maintain quality of life, and be guided by their

Table 2Preferred versus perceived roles in decision making about palliative chemotherapy.

Perceived roleb Preferred role

APatient alone

BPatient with doctor input

CShared decision

DDoctor with patient input

EDoctor alone

Total (%)

A Patient alone 7a [4] 6 (3) 1 (1) 1 (1) 0 (0) 15 (9)B Patient with doctor input 5 (3) 37a [22] 8 (5) 7 (4) 0 (0) 57 (33)C Shared decision 2 (1) 6 (3) 26a [15] 3 (2) 1 (1) 38 (22)D Doctor with patient input 0 (0) 2 (1) 6 (3) 22a [13] 5 (3) 35 (20)E Doctor alone 1 (1) 1 (1) 4 (2) 4 (2) 17a [10] 27 (16)

Total (%) 15 (9) 52 (30) 45 (26) 37 (22) 23 (13) 172 (100%) c

a Complete agreement between preferred and actual roles in 109 (63%) patients.b No evidence of a preference for a more active role than was experienced (p = .97, WRS).c 172 of the 179 returned surveys had completed responses for both preferred and perceived roles (7 missing or uninterpretable responses).

0

5

10

15

20

25

30

35

40

45

Passive Collaborative Active

Pro

port

ion

of p

atie

nts

(%)

Decision-making role

Preferred role

Perceived role

Fig. 1. Distribution of preferred and perceived roles in decision-making. “Active role”includes choice options A and B on the Control Preferences Scale, “collaborative role”choice option C, and “passive role” choice options D and E. Complete responses forpreferred decision-making roles N = 173 (6 missing), and for perceived decision-making roles N = 172 (7 missing).

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oncologist. In a systematic review by Puts et al. [19] the ‘doctor's recom-mendation’was identified as themost consistent reason for older adultsto accept or decline treatment, a finding supported by a study from thesame authors using semi-structured interviews [13]. Soto et al. [18]used 3 different tools to evaluate the priorities of older adults with can-cer (n=121) with regard to health outcomes. A large proportion of re-spondents (44%) rated other outcomes (function, freedom from pain, orfreedom from symptoms) as more important than survival, and mostagreed they would rather maintain their function and/or thinking abil-ity over living longer. Direct comparison with our study is difficult dueto methodological differences, though both support a need to incorpo-rate patient goals and priorities into treatment decision-making. Ofnote, and consistent with previous studies, [13] patients in our studywere less concerned with “how old I am” or their “other health prob-lems”, which are often concerns for their oncologist. [16] Of some inter-est were patients' subjective ratings of their performance status. Ofthose patients who rated their performance status as good (0 or 1),about one-third (38 of 115) were ‘vulnerable’ on the VES-13screening tool.

The role of the oncologist and GP in decisions about palliative che-motherapy for older adults were affirmed in this study. The importanceof the oncologist in guiding treatment acceptance or rejection for olderadults has been found previously [13,19,39]. The importance of the GPlikely reflects the trust older adults have with their GP as coordinatorsof their care over many years. The knowledge GPs have of their patientsenables them to tailor the provision of information, frame discussionabout relative benefits and harms, and help patients to make the rightdecision for them. Older adults and oncologists should endeavour to in-volve GPsmore in key decisions about cancer treatments including che-motherapy. GPs have expressed a desire for such involvement. [13]

Of concern was our finding that not all patients in our study under-stood that palliative chemotherapy would not cure their cancer. This isconsistent with other studies in older adults with cancer [12,13] andin studies of patients of all ages with advanced cancer [13,20,40–42].In the largest of these studies (n = 1193), only 31% of patients withmetastatic lung cancer and 19% of patients with metastatic colorectalcancer understood that palliative chemotherapy was ‘not at all likely’to lead to cure their cancer [20]. These resultsmay reflect gaps in patientunderstanding, patients maintaining hope, or patients' preference notto be aware of prognosis, or oncologists not having discussed the con-cept of cure. Given most participants in our study (71%) reported theironcologist had discussed with them whether their cancer could be

cured, at least for some patients there were appreciable gaps in under-standing or acceptance of the message conveyed.

Oncologists provided less information thanwas desired by some pa-tients and did not always match patients' information preferences.Whilst most patients with advanced cancer want some indication oftheir prognosis, [43,44] information needs are often varied with respectto the desire formore detailed quantitative prognostic information, [44]similar to our study. Interestingly, a greater proportion of patients in ourstudy desired quantitative prognostic information (67%) than in Elkinet al.'s [12] decade-earlier study (44%), which may reflect changing ex-pectations for information over time. Interestingly, very few patientsprovided a quantitative estimate of their survival time when asked.This could reflect patients not having discussed this with their oncolo-gist, not wishing to commit an estimate to paper, adopting a fatalisticapproach and hence answering “I do not know” (as in no one knows,it is up to fate), or being unable to recall this information.

This study adds comprehensive knowledge on the decision-makingpreferences of older adults with advanced cancer, including determin-ing preferred and perceived decision-making roles and exploring the

2.35

2.59

2.86

3.79

4.08

4.37

4.71

1 2 3 4 5

Colleagues (164)

Friends (164)

Other family (162)

Children (168)

Partner (170)

Local doctor (168)

Cancer specialist (170)

Mean Importance Rating

Fig. 2. Importance of the opinion of significant others. Respondentswere asked to rate theimportance of the opinion of each significant other using a scale ranging from “I do notcare at all” to “I take their opinion very seriously”. The number of responses providedare in parentheses. Scores assigned to each Likert category for calculation of meanswere: 1 = do not care at all; 2 = care a little; 3 = care somewhat; 4 = take opinionseriously; 5 = take opinion very seriously.

Table 3Rating and ranking of factors considered important by older adults in making a decision about palliative chemotherapy.

Factor N Not at allimportantN (%)

A littleimportantN (%)

ModeratelyimportantN (%)

VeryimportantN (%)

Mean importanceratinga (0–3)

N (%) ranking as single mostimportant factorb

My quality of life 168 0 (0) 0 (0) 20 (12) 148 (88) 2.88 31 (19%)My doctor's recommendation 173 3 (2) 3 (2) 12 (7) 155 (9) 2.84 42 (26%)The benefits of chemotherapy 170 8 (5) 1 (1) 21 (12) 140 (82) 2.72 1 (1%)Doing everything possible to fight thecancer

172 4 (2) 7 (4) 24 (14) 137 (80) 2.71 48 (30%)

Maintaining my independence 169 4 (2) 5 (3) 36 (21) 124 (73) 2.66 1 (1%)Living longer 169 5 (3) 7 (4) 35 (21) 122 (72) 2.62 24 (15%)Having someone to look after me duringtreatment

169 15 (9) 13 (8) 32 (19) 109 (64) 2.39 0 (0%)

The side effects of chemotherapy 170 7 (4) 21 (12) 54 (32) 88 (52) 2.31 3 (2%)Being able to look after my partner /spouse or family

165 28 (17) 14 (8) 25 (15) 98 (60) 2.17 6 (4%)

My other health problems 167 30 (18) 29 (17) 45 (27) 63 (38) 1.84 1 (1%)How old I am 164 42 (26) 21 (13) 45 (27) 56 (34) 1.70 1 (1%)How far I would need to travel fortreatment

168 49 (29) 24 (14) 38 (23) 57 (34) 1.61 1 (1%)

N = the number of responses received for that item.a Mean importance rating represents the arithmetic mean of importance scores assigned, with scores for each Likert category as: 0 = not at all important; 1 = a little important; 2 =

moderately important; 3 = very important.b After rating each factor, participants were asked to select just one factor as the single most important factor when making a decision about treatment. 20 responses were missing or

unable to be included (selected more than one factor).

5E.B. Moth et al. / Journal of Geriatric Oncology xxx (2019) xxx

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relationship between preferences and geriatric measures of vulnerabil-ity. The multi-centre design across metropolitan and regional centresprovides a broad geographical cross section of older adults with cancerin Australia. Limitations of our study include the use of pre-specified re-sponse options to evaluate factors important in decision-making, limit-ing understanding into other motivating factors. Sampling bias is also alimitation. Patients from culturally and linguistically diverse back-grounds were likely underrepresented due to the requirement for En-glish proficiency. The majority (87%) of included patients had decidedto have chemotherapy, likely due to their continued contact with par-ticipating cancer centres that facilitated recruitment. Their attitudes,preferences and priorities may differ to older adults who had declinedor were not suitable for chemotherapy. Treating oncologists were alsoinvolved in identifying patients for the study, possibly leading toover-representation of thosewho had a positive decision-making expe-rience and an informative consultation, and under-representation ofthose who were in very poor health or psychological distress, or withwhom oncologists perceived a difficult interaction. Oncologists werealso familiar with the survey content which may have influenced clinicconsultations. Response bias is also considered. Participants receivingsurveys from treating oncologists also may report more favourably ontheir experiences, and those who chose to complete and return surveysmay have had different experiences or be differently motivated fromthose who chose not to. Affirmation bias, introduced through askingpatients about a decision that they have already made, also should beconsidered. Lack of data on non-responders is also a weakness of thestudy.

5. Conclusion

Older adults with incurable cancer hold varied preferences for in-volvement in decision-making about palliative chemotherapy, andmost played the role that they preferred. To facilitate shared decision-making, oncologists should seek patients' decision-making preferences,priorities, and information needs when discussing andmaking a recom-mendation about palliative chemotherapy.

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jgo.2019.07.026.

Ethics Approval and Consent to Participate

Ethics approval was granted by the Sydney Local Health District HumanResearch Ethics Committee of Concord Repatriation General Hospital(HREC/15/CRGH/208).

Authors' Contributions

Conception and design: EM, PB, BK, AM, and VN conceived the work.EM, PB, BK, and VN designed the work.Data acquisition: EM, PB, BK, HM, AR, SD, CS, FH, and RZ.Analysis and interpretation of data: EM, AM, PB, and BK were responsi-ble for data analysis and initial interpretation of results. All authorswereresponsible for final interpretation of results as presented in themanuscript.Manuscript writing: EM prepared the first draft of the manuscript. Allauthors revised the manuscript.Approval of final manuscript: All authors approved the final version ofthe manuscript. All authors agreed to be accountable for all aspects ofthe work.

Disclosures and Conflict of Interest Statements

Dr. Christopher Steer reports personal fees in the form of honoraria forwork on advisory boards for Janssen, Merck, and MSD outside thework in this manuscript, and honoraria and travel for education activi-ties for Mundipharma, and Teva, outside the work in this manuscript.Dr. Florian Honeyball reports personal fees from Astrazenica, outsidethe work in this manuscript, and is a member of an advisory board forAstellas Pharma Australia.

Acknowledgement of Funding

Dr. EM was supported in this work by two PhD scholarships: a Univer-sity of Sydney Australian Postgraduate Award (APA), and PhD fundingsupport from Sydney Catalyst: the Translational Cancer Research Centreof Central Sydney and regional NSW, University of Sydney, NSW,Australia and Cancer Institute NSW.

Acknowledgements

The authors wish to acknowledge Dr. Sarah Khan, Macarthur CancerTherapy Centre, for data acquisition. The authors would also like to ac-knowledge and thank the participants of the study across all cancer cen-tres, who very kindly took the time to engage in this research.

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Please cite this article as: E.B. Moth, B.E. Kiely, A. Martin, et al., Older adults' preferred and perceived roles in decision-making about palliativechemotherapy, decisi..., J Geriatr Oncol, https://doi.org/10.1016/j.jgo.2019.07.026