PREDICTION OF LONG-TERM COGNITIVE DECLINE FOLLOWING

POSTOPERATIVE DELIRIUM IN OLDER ADULTS

Elizabeth E. Devore1, Tamara G. Fong2,3, Edward R. Marcantonio2,4, Eva M. Schmitt2, Thomas

G. Travison2,4, Richard N. Jones2,5*, Sharon K. Inouye2,4*

*Co-senior authors

1 Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard

Medical School, Boston, Massachusetts

2 Aging Brain Center, Institute of Aging Research, Hebrew SeniorLife, Boston, Massachusetts

3 Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School,

Boston, Massachusetts

4 Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School,

Boston, Massachusetts

5 Departments of Psychiatry and Human Behavior and Neurology, Brown University Warren

Alpert Medical School, Providence, Rhode Island

Corresponding author: Elizabeth E. Devore, 181 Longwood Avenue, Room 448, Boston,

Massachusetts 02115; phone: 617-525-2042; fax: 617-525-2008; email:

nheed@channing.harvard.edu

Word count: Abstract (248), Text (4,630)

This is the author’s manuscript which was subsequently submitted to and accepted for publication. J Gerontol A Biol Sci Med Sci. 2017; 72(12):1697-1702.

ABSTRACT Background: Increasing evidence suggests that postoperative delirium may result in long-term

cognitive decline among older adults. Risk factors for such cognitive decline are unknown.

Methods: We studied 126 older participants without delirium or dementia upon entering the

Successful AGing After Elective Surgery (SAGES) study, who developed postoperative delirium

and completed repeated cognitive assessments (up to 36 months of follow up). Pre-surgical

factors were assessed preoperatively and divided into nine groupings of related factors

(“domains”). Delirium was evaluated at baseline and daily during hospitalization using the

Confusion Assessment Method diagnostic algorithm, and cognitive function was assessed using

a neuropsychological battery and the Informant Questionnaire for Cognitive Decline in the

Elderly (IQCODE) at baseline and six-month intervals over three years. Linear regression was

used to examine associations between potential risk factors and rate of long-term cognitive

decline over time. A domain-specific and then overall selection method based on adjusted R2

values was used to identify explanatory factors for the outcome.

Results: The General Cognitive Performance (GCP) score (combining all neuropsychological

test scores), IQCODE score, and living alone were significantly associated with long-term

cognitive decline. GCP score explained the most variation in rate of cognitive decline (13%), and

six additional factors—IQCODE score, cognitive independent activities of daily living

impairment, living alone, cerebrovascular disease, Charlson comorbidity index score, and

exhaustion level—in combination explained 32% of variation in this outcome.

Conclusions: Global cognitive performance was most strongly associated with long-term

cognitive decline following delirium. Pre-surgical factors may substantially predict this outcome.

Keywords: Risk factors, cognition, elective surgery

INTRODUCTION

Delirium, an acute confusional state, is a common condition affecting up to 50% of older

adults in the hospital, with serious potential consequences including cognitive and functional

impairment, institutionalization, and mortality(1). Previous studies have indicated that

individuals with delirium following surgery often experience cognitive impairment, although few

studies have assessed cognitive function beyond six to twelve months postoperatively(2); still,

emerging evidence suggests that some individuals never return to their previous cognitive

level(2), and instead exhibit accelerated cognitive decline over the longer term(3). In the SAGES

study, we previously reported that individuals had linear trajectories of cognitive decline over

postoperative months two to 36, and these trajectories were significantly steeper among

participants who developed delirium compared to those who did not(3).

The etiology of delirium associated with long-term cognitive sequelae is unclear.

Multiple risk factors have been identified for delirium(4), and validated prediction models have

been developed to target high-risk individuals for intervention(5-8). Moreover, intervention

programs such as the Hospital Elder Life Program and proactive geriatric consultation have

successfully targeted modifiable risk factors (e.g., sleep and mobilization), with 30-40%

reductions in delirium among older hospitalized patients(9, 10). In the absence of delirium, there

are several established risk factors for cognitive decline in older adults (e.g., age and education),

although most of these factors are not modifiable(11). However, risk factors specific for delirium

associated with cognitive decline have not yet been identified. Exploring such factors is a

necessary first step toward understanding the etiology of delirium associated with long-term

cognitive decline, and identifying potential therapeutic targets for intervention. This paper

specifically examines whether pre-surgical factors predict the rate of long-term cognitive decline

among older persons without dementia who developed postoperative delirium. Identifying such

factors could be useful in selecting high-risk individuals for targeted prevention or randomized

trials aimed at stalling cognitive decline following delirium.

METHODS

Study sample

The SAGES study is an ongoing prospective cohort study of older adults undergoing

elective major non-cardiac surgery. The study design and methods have been described

previously(12). Briefly, eligible participants were age 70 years and older, English speaking,

scheduled to undergo elective surgery at one of two Harvard-affiliated academic medical centers

and with an anticipated length of stay of at least three days. Eligible surgical procedures were:

total hip or knee replacement, lumbar, cervical, or sacral laminectomy, lower extremity arterial

bypass surgery, open abdominal aortic aneurysm repair, and colectomy. Exclusion criteria

included evidence of dementia, delirium, hospitalization within three months, terminal condition,

legal blindness, severe deafness, history of schizophrenia or psychosis, and history of alcohol

abuse. A total of 566 patients were enrolled between June 18, 2010 and August 8, 2013, and six

individuals with possible dementia were excluded after a detailed adjudication process described

previously(12), leaving 560 cohort participants. Written informed consent was obtained from all

participants according to procedures approved by the institutional review boards of Beth Israel

Deaconess Medical Center and Brigham and Women’s Hospital, the two study hospitals, and

Hebrew SeniorLife, the study coordinating center, all located in Boston, Massachusetts.

In this paper, we focused on 134 participants who developed postoperative delirium and

had cognitive data during follow up.

Assessment of pre-surgical factors

An initial home interview was conducted to gather detailed information on health and

functioning an average of two weeks prior to hospitalization for scheduled surgery. Basic

demographic, medical, and lifestyle information was obtained, and well-validated assessments of

cognitive, physical, and mental function were administered (see Supplement A for details). The

five components of the Fried frailty index (unintentional weight loss, exhaustion, low physical

activity, low grip strength, and slow timed walk) were assessed(13), and a blood sample was

collected from which apolipoprotein (APOE) 4 genotype and C-reactive protein (CRP) levels

were analyzed. A trained physician conducted medical record reviews after participants were

discharged from the hospital, which provided additional information on pre-surgical factors.

Assessment of delirium

Beginning with the first postoperative day, delirium symptoms were assessed daily

during hospitalization using brief cognitive tests(12, 14), the Delirium Symptom Interview(15),

and acute changes in mental status reported by family and nurses. Delirium diagnosis was based

on the Confusion Assessment Method (CAM) diagnostic algorithm(16), a widely used and well-

validated assessment tool with very high sensitivity, specificity, and inter-rater reliability(17,

18). Delirium symptoms were also obtained from medical records using a validated delirium

assessment(19, 20) with adjudication of cases by an expert delirium panel. For this study, a

combined approach based on either CAM or medical record assessment of delirium (and used in

prior studies) was utilized to identify incident delirium(20).

Assessment of cognitive function

Cognitive function was assessed at the home interview, and repeated at one, two, and six

months after hospital discharge, and at six-month intervals thereafter up to 36 months following

discharge. The neuropsychological battery evaluated cognitive domains putatively most affected

by delirium(12, 21) (see Supplement B for details). We combined all test scores into a weighted

composite summary measure, the General Cognitive Performance (GCP) score, using previously

published methods(22); this score was calibrated to a nationally representative sample of older

adults(23), where the mean for persons aged ≥70 years is expected to be 50, with a standard

deviation of 10(21). The GCP score is sensitive to change with minimal floor and ceiling

effects(21, 22), and has been applied in previous studies(3, 24-26). For longitudinal GCP values,

we applied a previously described method of correction to these scores to account for learning

effects over time(27-29).

As previously reported in this cohort, the pattern of GCP scores over time indicated

cognitive decline at postoperative month one, recovery of cognitive function above baseline at

month two, and gradual cognitive decline beginning at month two and dropping below baseline

over months twelve to 36; this pattern was more pronounced among participants who developed

postoperative delirium compared to those who did not(3). Loss to follow up was minimal due to

death (7%) and withdrawal from the study (5%), and non-differential with respect to delirium

status; therefore, 89% of eligible participants had complete data on the GCP score over time(3).

Statistical analysis

We divided pre-surgical factors into nine related groupings or domains: demographics

(age, sex, race, education, and living arrangement), lifestyle factors (smoking status, alcohol

intake, and socioeconomic status), cognitive function (baseline GCP score and IQCODE score),

physical function (ADL and IADL impairments), mental health/quality of life (GDS scale and

SF-12 composite and subscale scores), sensory function (hearing and vision impairment), frailty

(Fried frailty index components), medical factors (surgery type, CCI score, cardiovascular

disease, peripheral vascular disease, diabetes, and cerebrovascular disease), and biomarkers

(APOE 4 genotype and CRP level). Our outcome of interest was the rate of cognitive decline

over three years of follow up, which was estimated previously for each participant based on

mixed effects regression models with random effects for baseline and change over time; this

slope was estimated beginning at postoperative month two because, on average, participants

started a linear trajectory of gradual cognitive decline at that time(3).

We used simple linear regression to estimate mean differences in rates of cognitive

decline (and 95% confidence intervals [CIs]) for each unit increase in continuous predictors and

for each level of a categorical predictor compared to a chosen reference level. We divided mean

differences associated with continuous predictors by twice the standard deviation of the predictor

to make the scaling similar to that of binary predictors(30). Next, for each domain of pre-surgical

factors, we used an adjusted R2-based selection method to determine an order of variable

selection into successive linear regression models (with rate of cognitive decline as the outcome).

We selected variables into the model by maximizing the adjusted R2 value (i.e., total proportion

of variation explained in the outcome corrected for the number of variables in the model), and

we obtained total R2 values (i.e., total proportion of outcome variation explained by all variables

in the model) for each successive model as well. For each domain, we retained variables if they

increased the adjusted R2 value of the model, or if they entered the model first when no factors

met this threshold, and these variables were considered for our final model. Finally, we evaluated

all variables retained from domain-specific models (with age and sex forced into the model)

using the selection procedure described above and obtained adjusted and total R2 values. The

subset of variables that increased the adjusted R2 value of this model constituted our final model.

We evaluated the normality of studentized residuals in our final linear regression model using the

Shapiro-Wilk test)(31), and identified potential outliers based on graphical methods and the

absolute values of these residuals (threshold of two)(ref); we also detected influential

observations based on the Cook’s Distance (threshold of four divided by the square root of the

sample size)(ref). The entire selection process was repeated if outliers or influential points were

identified and these participants were excluded.

For each model, participants with complete information on contributing variables were

included; as previously reported, there is little missing data in this sample(32). All statistical

analyses were conducted in SAS version 9.3.

RESULTS

Of the 134 participants with postoperative delirium, we initially identified five

individuals who were potential outliers and three individuals with influential data in our final

model, and subsequently excluded these participants from further analyses. In Table 1, selected

pre-surgical characteristics are described among 552 participants in the overall SAGES cohort

and separately for 126 participants who developed postoperative delirium. Overall, the cohort

had an average age of 76.8 years at baseline; it was 60% female and 8% non-white. Participants

with postoperative delirium were slightly older, had lower GCP scores, and more often had

multiple comorbidities (as indicated by the Charlson comorbidity index score) compared to the

overall cohort. Other differences were relatively small comparing participants with delirium to

the whole cohort.

In univariate models, worse cognitive function prior to surgery (Domain 3) was

significantly associated with faster cognitive decline over follow up; specifically, participants

with lower GCP scores and higher IQCODE scores at baseline had greater slopes of cognitive

decline (mean differences: 0.47 points/year [95% CI: 0.24, 0.71] per half-standard deviation

higher GCP score at pre-operative assessment, and -0.34 points/year [95% CI: -0.61, -0.08] per

half-standard deviation higher IQCODE score) (Supplemental Table 1). Living alone (a

component of Domain 1) was related to significantly faster cognitive decline (mean difference: -

0.32 points/year, 95% CI: -0.59, -0.05), and other variables (i.e., any cognitive independent

activities of daily living [IADL], hearing impairment, vision impairment, weight loss, and

cerebrovascular disease) appeared to be related to faster cognitive decline, but these associations

did not reach statistical significance.

When we examined the domain-specific contribution of pre-surgical factors to rates of

cognitive decline (Supplemental Table 2), baseline cognitive performance (Domain 3) explained

the most variation in rates of cognitive decline: GCP scores accounted for 11.1% and IQCODE

scores contributed another 3.7%, and both variables improved the explanatory power of the

model based on increased adjusted R2 values upon entry of these variables to the model. Medical

factors (Domain 8) explained 7.8% of variation in cognitive decline, with the Charlson

comorbidity index score, diabetes, cerebrovascular disease, and cardiovascular disease

explaining 3.1%, 1.8%, 1.3%, and 0.9% of variation in cognitive decline, respectively; these

variables improved the model based on their adjusted R2 values. Demographic variables

(Domain 1) explained 5.8% of variation, with the majority of this variation explained by living

alone (4.4%); this variable was the only one to increase the adjusted R2 value of this domain-

specific model. Frailty variables (Domain 7) explained 4.5% of variation in cognitive decline,

with slow timed walk explaining 1.5% of variation and less exhaustion explaining 1.8%; both of

these variables improved the model based on their adjusted R2 values. Other domains contributed

less to explaining variation in the outcome, and only had one variable that improved the

explanatory power of the respective models.

In our final step, baseline GCP score was found to explain the most variation in rate of

cognitive decline (13.0%) when factors retained from each of the domain-specific models were

considered for inclusion (in addition to age and sex, which were forced into the model and

contributed 3.0% to explained variation) (Table 2). Living alone, less exhaustion, Charlson

comorbidity index score, IQCODE score, cerebrovascular disease, and any cognitive IADL

impairment contributed another 6.4%, 3.2%, 2.1%, 2.0%, 1.3%, and 1.0% to explaining variation

in the outcome, respectively. Beyond age and sex, these seven factors—GCP score, living alone,

exhaustion level, Charlson comorbidity index score, IQCODE score, cerebrovascular disease,

and cognitive IADL impairment—improved the amount of explained variation in the outcome

based on an increase in the adjusted R2 value with variable entry into the model. The total

amount of variation in rates of cognitive decline explained by these variables (i.e., total model R2

value) was 31.8%. The residuals from this final model did not violate the assumption of

normality for linear regression models (Shapiro-Wilk statistic, W=0.99, p=0.5).

DISCUSSION

We found that worse cognitive performance (GCP and IQCODE scores) and living alone

prior to surgery were significantly associated with long-term cognitive decline in older

participants with postoperative delirium in SAGES. Baseline GCP score contributed most

substantially to explained variation in rates of cognitive decline, and a total of seven factors—

GCP score, IQCODE score, cognitive IADL impairment, living alone, cerebrovascular disease,

Charlson comorbidity index, and exhaustion level—accounted for 32% of the variation in this

outcome. These results suggest that pre-surgical factors may have important influences on long-

term cognitive decline following postoperative delirium in older adults.

Our findings serve to confirm and extend prior work. Baseline cognitive function has

been previously demonstrated to be a strong risk factor for cognitive decline in older adults(11),

and was also shown to be the dominant predictor of cognitive decline over time in a small study

of community-dwelling older individuals (R2=37%)(33). This result is consistent with our

finding that baseline GCP score is the main factor predicting long-term cognitive decline among

older individuals with postoperative delirium, and additional baseline cognitive measures (i.e.,

impaired IQCODE score and cognitive IADL impairment) contribute to prediction as well. In

addition, previous work in SAGES found that lower GCP score at baseline was linearly

associated with greater risk of delirium (relative risk=2.0 for each half standard deviation

decrease in GCP score)(34), and furthermore delirium was associated with steeper rates of

cognitive decline over the three-year follow up(3). The present study provides additional insight

by suggesting that lower baseline GCP scores predict greater rates of long-term cognitive decline

among those who develop postoperative delirium, in a graded fashion that holds across the full

range of baseline GCP scores in this sample.

Prior epidemiologic studies have also demonstrated that cerebrovascular disease(35, 36)

and living alone(37, 38) are important risk factors for cognitive decline in older individuals, and

we identified these variables as significant predictors of cognitive decline in our sample of older

participants with delirium. Cerebrovascular disease has been recognized as increasing the risk of

delirium as well(1), and brain injury from both types of events might cause worse deterioration

in cognitive function over time. In contrast, living alone may result in less physical, social, and

cognitive stimulation at home prior to surgery, leading to reduced cognitive reserve and greater

vulnerability to decline following postoperative delirium.

Other predictors that were identified in our analyses included the Charlson comorbidity

index and exhaustion, although these variables appeared to be associated with cognitive decline

in a counterintuitive direction (i.e., multiple comorbidities and more exhaustion were associated

with less decline) in our sample of participants with delirium. These results are likely due to

inherent limitations of our multivariable modeling approach in disentangling interrelated chronic

disease factors when entered into our models simultaneously. Although this approach can

generally identify true predictors, it can incorporate extraneous predictors into a final model(39);

therefore, our findings should be interpreted with caution and need replication in future studies.

Without replication, our individual predictors cannot be considered suitable for clinical

applications. Clearly, additional studies are needed to evaluate pre-surgical factors that

contribute to long-term cognitive decline following postoperative delirium, as well as the

potential interplay of perioperative and postoperative factors in the prediction of this outcome.

In recent studies, common neuropathologic factors associated with Alzheimer’s disease,

cerebrovascular disease, and Lewy-body disease predicted 41% of variation in rates of cognitive

decline among older adults(40), and postmortem factors (e.g., indicators of neuronal density and

neural tissue integrity) explained additional variation in this outcome(41, 42). In our study, the

total percentage of explained variation was 32% based on a variety of clinical characteristics, but

similar neuropathologic factors could potentially account for remaining variation in cognitive

decline. More studies are needed to explore the combination of clinical and neuropathologic

factors that explain this variation.

Major strengths of this study include: rigorous data collection, standardized delirium

assessments, careful characterization of baseline cognition, repeated neuropsychological

assessments, relatively long follow up, and careful optimization of the GCP score for analyses of

cognitive decline. We also had a wide range of data available to explore potential risk factors at

baseline of this study. However, our study has several limitations that should be considered.

First, we have evaluated a subset of 126 participants who developed postoperative delirium in

the SAGES cohort, and this modest sample size limits the power of our analyses. We may have

missed important associations of interest, and the certainty of our estimates related to observed

associations and model building is decreased; low power also increases the risk of identifying

false positive results, which may have occurred in this study(43). Hence, these results will need

to be replicated in future, larger studies. Second, we cannot rule out the possibility that some

participants had preclinical dementia at baseline, despite our extensive efforts to exclude

individuals with dementia from this cohort. For example, if some individuals had mild cognitive

impairment at baseline, we may have overestimated the association between lower GCP score

and faster cognitive decline in our sample; however, this is less likely to be driving the observed

association because we found the association held across the full range of baseline cognitive

scores—even among those with the highest scores. Finally, our sample included participants who

were aged ≥70 years, highly educated, and mostly white race, and therefore these results may be

limited in their generalizability to younger or more diverse samples.

In summary, we found pre-surgical factors that were associated with, and contributed to

explained variation in, long-term cognitive decline among older participants who experienced

postoperative delirium. Baseline cognitive function contributed most substantially to explaining

variation in the rate of cognitive decline, and a set of seven identified factors explained over one-

quarter of the variation in the outcome. Further research should evaluate the relative contribution

of pre- and post-surgical factors to long-term cognitive decline following delirium, as well as the

extent to which these factors predict such cognitive decline in older adults with and without

postoperative delirium.

FUNDING

This work was supported by the National Institute on Aging (P01AG031720 and

K07AG041835 to S.K.I.; R01AG044518 to S.K.I./ R.N.J.; and R01AG030618, K24AG035075,

and R01AG051658 to E.R.M.). S.K.I. holds the Milton and Shirley F. Levy Family Chair.

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Table 1. Selected Baseline Characteristics of Participants, Including Those With Post-Operative Delirium, in the Successful AGing After Elective Surgery (SAGES) Study a

All Participants

(n=552) Participants with

Delirium (n=126)

Continuous variables Mean ± Standard deviation Age, in years 76.8 ± 5.2 77.2 ± 4.8 Education, in years 14.8 ± 3.0 14.7 ± 3.0 Leisure-time activity level, in Metabolic Equivalent Times expended per week

742 ± 1230 739 ± 1495

Global Cognitive Performance score, in standard units 56.9 ± 7.4 54.7 ± 6.4 Informant Questionnaire for Cognitive Decline in the Elderly score

3.1 ± 0.2 3.2 ± 0.3

Geriatric Depression Scale score, in points 2.7 ± 2.6 3.0 ± 2.8 Categorical variables Percentages Female sex 60 60 Non-white race 8 10 Living alone 30 27 Current smoking 5 5 ≥1 weekly alcohol intake 38 30 Any activities of daily living impairment 8 9 Any independent activities of daily living impairment 31 35 Any cognitive independent activities of daily living impairment 6 10 Hearing impairment 31 35 Surgery type Orthopedic 79 78 Vascular 7 9 Gastrointestinal 14 13 Charlson comorbidity index score, in points 0 44 41 1 26 17 ≥2 30 42 Cardiovascular disease 13 17 Diabetes 22 25 Cerebrovascular disease 6 8 a Data are derived from 552 participants, except there is missing information on alcohol intake (4 missing), Informant Questionnaire for Cognitive Decline in the Elderly score (12 missing), and Geriatric Depression Scale score (2 missing).

Table 2. Overall Contribution of Remaining Potential Risk Factors to Explained Variation in Cognitive Decline Among Participants With Post-Operative Delirium in the Successful AGing After Elective Surgery (SAGES) Study a,b

Total R2

value

Change in total R2 value

Adjusted R2 value, adding

variables successively c

Variables forced into the model 1. Age -- -- -- 2. Sex 0.0301 -- 0.0088 Variables retained from Supplemental Table 2 3. Global Cognitive Performance score 0.1601 0.1300 0.1322 4. Living alone 0.2237 0.0636 0.1888 5. Frailty component 2: exhaustion 0.2554 0.0317 0.2131 6. Cerebrovascular disease 0.2680 0.0126 0.2175 7. Informant Questionnaire for Cognitive Decline in the Elderly

score 0.2881 0.0201 0.2301

8. Charlson comorbidity index score 0.3087 0.0206 0.2346 9. Any cognitive independent activities of daily living impairment 0.3184 0.0097 0.2362 10. Diabetes 0.3256 0.0072 0.2352 11. C-reactive protein level 0.3325 0.0069 0.2336 12. Cardiovascular disease 0.3391 0.0066 0.2317 13. Frailty component 5: slow timed walk 0.3470 0.0079 0.2313 14. Geriatric Depression Scale score 0.3504 0.0034 0.2255 15. Hearing impairment 0.3530 0.0026 0.2185 16. Smoking status 0.3533 0.0003 0.1981 a Data are derived from 126 participants, except there is missing information on Informant Questionnaire for Cognitive Decline in the Elderly score (2 missing), frailty component 5 (27 missing), and C-reactive protein level (3 missing). b Variables are numbered according to the order in which they were selected into the model. c Adjusted R2 values represent the proportion of variation explained as variables are added successively to the final model, with a correction for the number of variables entered in the model. An increase in adjusted R2 with addition of a variable to the model (shown in bold type) indicates the variable improved the explanatory power of the model, and a decrease in adjusted R2 indicates the variable does not improve the explanatory power of the model.