BMJ Open · Meschi, Tiziana; University of Parma, Department of Clinical and Experimental Medicine Primary Subject Heading: Geriatric medicine Secondary Subject

For peer review only

Multimorbidity in elderly hospitalized patients and risk of Clostridium difficile infection: a retrospective study with

Cumulative Illness Rating Scale (CIRS)

Journal: BMJ Open

Manuscript ID: bmjopen-2015-009316

Article Type: Research

Date Submitted by the Author: 06-Jul-2015

Complete List of Authors: Ticinesi, Andrea; University of Parma, Department of Clinical and Experimental Medicine Nouvenne, Antonio; University of Parma, Department of Clinical and

Experimental Medicine Folesani, Giuseppina; University of Parma, Italian Workers' Compensation Authority (INAIL) Research Center at University of Parma Prati, Beatrice; Parma University Hospital, Internal Medicine and Critical Subacute Care Unit Morelli, Ilaria; Parma University Hospital, Internal Medicine and Critical Subacute Care Unit Guida, Loredana; Parma University Hospital, Internal Medicine and Critical Subacute Care Unit Turroni, Francesca; University of Parma, Department of Life Sciences Ventura, Marco; University of Parma, Department of Life Sciences Lauretani, Fulvio; Parma University Hospital, Geriatrics Unit

Maggio, Marcello; University of Parma, Department of Clinical and Experimental Medicine Meschi, Tiziana; University of Parma, Department of Clinical and Experimental Medicine

<b>Primary Subject Heading</b>:

Geriatric medicine

Secondary Subject Heading: Infectious diseases, Gastroenterology and hepatology

Keywords: Multimorbidity, Clostridium difficile, Elderly, Frailty, Proton pump inhibitors, Antibiotic

For peer review only - http://bmjopen.bmj.com/site/about/guidelines.xhtml

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Multimorbidity in elderly hospitalized patients and risk of Clostridium difficile infection: a 1

retrospective study with Cumulative Illness Rating Scale (CIRS) 2

Andrea Ticinesi1-2

, Antonio Nouvenne1-2*

, Giuseppina Folesani3, Beatrice Prati

1-2, Ilaria Morelli

1, 3

Loredana Guida1, Francesca Turroni

4, Marco Ventura

4, Fulvio Lauretani

5, Marcello Maggio

2, 4

Tiziana Meschi1-2

5

1 Internal Medicine and Critical Subacute Care Unit, Parma University Hospital, Parma, Italy 6

2 Department of Clinical and Experimental Medicine, University of Parma, Parma, Italy 7

3 Italian Workers’ Compensation Research Center at University of Parma, Parma, Italy 8

4 Laboratory of Probiogenomics, Department of Life Sciences, University of Parma, Parma, Italy 9

5 Geriatrics Unit, Parma University Hospital, Parma, Italy 10

11

*Corresponding author: 12

Antonio Nouvenne M.D. 13

Internal Medicine and Critical Subacute Care Unit 14

Parma University Hospital, 15

University of Parma 16

Department of Clinical and Experimental Medicine 17

Via A. Gramsci 14 18

43126 PARMA 19

ITALY 20

Phone: +39 0521 703626 21

Mobile: +39 3492258317 22

Fax: +39 0521 702383 23

e-mail: [email protected] 24

25

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Abstract word count: 293 1

Main text word count: 2298 2

Tables: 3 3

Figures: 0 4

Number of references: 29 5

Key words: multimorbidity, elderly, CIRS, Clostridium difficile, proton pump inhibitors, 6

antibiotic, antifungal 7

8

Other authors’ mail addresses: 9

Andrea Ticinesi: [email protected] 10

Giuseppina Folesani: [email protected] 11

Beatrice Prati: [email protected] 12

Ilaria Morelli: [email protected] 13

Loredana Guida: [email protected] 14

Francesca Turroni: [email protected] 15

Marco Ventura: [email protected] 16

Fulvio Lauretani: [email protected] 17

Marcello Maggio: [email protected] 18

Tiziana Meschi: [email protected] 19

20

21

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ABSTRACT 1

2

Objectives: To identify the role of chronic comorbidities, considered together in literature-validated 3

index (Cumulative Illness Rating Scale, CIRS), and treatment with antibiotics or proton pump 4

inhibitors (PPIs) as risk factors for hospital-acquired Clostridium difficile infection (CDI) in elderly 5

multimorbid hospitalized patients. 6

Design: Retrospective cohort study. 7

Setting: Subacute hospital geriatric care ward in Italy. 8

Participants: 505 (238 M, 268 F) elderly (age≥65) frail patients. 9

Main outcome measures: The relationship between CDI and CIRS Comorbidity Score, CIRS 10

Severity Index, number of comorbidities, antibiotic, antifungal and PPI treatment, length of hospital 11

stay was assessed through age- and sex-adjusted and multivariate logistic regression models. CIRS 12

Comorbidity Score was handled after categorization in quartiles. 13

Results: Mean age was 81±10 years. Forty-three patients (22 M, 21 F) developed CDI. Both CIRS 14

Comorbidity Score and CIRS Severity Index were significantly higher in CDI-positive group, after 15

adjustment for age and sex (respectively, median 14, IQR 11 to 18, vs 13, IQR 8 to 17, p=0.036 and 16

median 1.92, IQR 1.77 to 2.23, vs 1.85, IQR 1.54 to 2.10, p=0.028). At a multivariate logistic 17

regression analysis, subjects in the highest quartile for CIRS Comorbidity Score (≥17) carried a 18

significantly higher risk of CDI (OR 4.72, 95%CI 1.18 to 18.87, p=0.03) than subjects in the lowest 19

quartile (<9). Further variables significantly associated with CDI were number of comorbidities 20

(OR 1.55, 95%CI 1.04 to 2.30, p=0.03), antibiotic therapy (OR 2.62, 95%CI 1.21 to 5.66, p=0.01) 21

and length of hospital stay before admission to our unit (OR 1.03, 95%CI 1.00 to 1.05, p=0.04). PPI 22

treatment was not associated to CDI neither at univariate nor at multivariate analysis. 23

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Conclusions: Multimorbidity, measured through literature-validated indexes like CIRS, may be 1

independently associated with the risk of CDI in a population of multimorbid elderly subjects with 2

prolonged hospital stay. 3

4

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STRENGTHS AND LIMITATIONS OF THIS STUDY 1

• Calculation of CIRS (Cumulative Illness Rating Scale) scores is a rapid inexpensive way to 2

determine the overall multimorbidity burden of elderly hospitalized patients. 3

• The association of multimorbidity with Clostridium difficile infection (CDI) has never been 4

extensively performed in elderly multimorbid subjects with prolonged hospital stay. 5

• Retrospective study design may limit generalizability of results. 6

• Asymptomatic carriers of Clostridium difficile were not identified. 7

• Functional performance of patients and polypharmacy, although indirectly estimated by 8

CIRS, were not considered as potential confounders. 9

10

11

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INTRODUCTION 1

2

Clostridium difficile infection (CDI) is one of the leading healthcare-associated pathogens in 3

Western countries, responsible for diarrhea and colitis in subjects with abnormal gut microbiota and 4

impaired local immune response1. Even if the number of community-acquired CDIs is continuously 5

rising2, most cases occur in patients with prolonged hospitalization and are responsible for a 6

significant rise in mortality and burden of healthcare costs3. 7

Antibiotic therapy in the previous 30 days has been traditionally considered as the most important 8

risk factor for CDI. Virtually, exposure to all antibiotic classes has been linked to an increased risk, 9

with the highest odds ratios for cephalosporines, clindamycin and carbapenems4. This risk is also 10

consistently influenced by timing and duration of antibiotic administration5. 11

However, a significant number of hospital- and community-acquired CDI occur in patients without 12

any recent antibiotic exposure in personal history6-8

. 13

As such, although it has been suggested that widespread antibiotic use can increase environmental 14

Clostridium difficile contamination and thus the risk of exposure also for those who were not 15

administered antibiotics7, alternative factors may be involved. Older age, cancer chemotherapy, 16

inflammatory bowel disease, chronic kidney disease (CKD), organ transplantation, 17

immunodeficiency and exposure to asymptomatic carriers or infected patients have all been 18

recognized as independent risk factors1,9,10

. Chronic proton-pump inhibitor (PPI) exposure may also 19

be associated with CDI11

, even if some studies have denied this association after adjustment for 20

potential confounders12

. 21

However, since most hospital-acquired cases of CDI occur in oldest-old patients, with a high burden 22

of chronic comorbidities affecting multiple organs and systems13

, multimorbidity itself may play a 23

relevant role in defining the risk of CDI14

. Multimorbidity scores have already been demonstrated 24

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as useful tools for predicting the risk of severe hospital-acquired infections by multi-drug resistant 1

bacteria15,16

. 2

Therefore, the aim of this retrospective hospital-based study is to evaluate the risk factors of CDI in 3

a cohort of frail multimorbid hospitalized elderly with a prolonged hospital stay, with a particular 4

focus on multimorbidity measured through Cumulative Illness Rating Scale (CIRS) Comorbidity 5

Score and Severity Index, two literature-validated17

indexes that have been demonstrated as 6

particularly useful in defining the prognostic trajectory of geriatric hospitalized patients18

. 7

8

9

10

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METHODS 1

2

With a retrospective cohort study design, all clinical records of patients admitted to the Critical 3

Subacute Care Unit of Parma University Hospital, in Northern Italy, from January 1st to June 30

th 4

2013 were analyzed. This unit is an internal medicine ward primarily dedicated to elderly 5

multimorbid frail patients who need a prolonged hospital stay for critical conditions preventing an 6

early discharge. Admission is generally planned after some days of hospital stay in other acute-care 7

medical or surgical wards. 8

Inclusion criteria were age ≥65 years old, absence of a well-defined terminal condition with a 9

survival prognosis <30 days, presence of at least two of the following criteria defining frailty: 10

reduced muscular strength, reduced gait speed, forced bed rest, lack of autonomy in activities of 11

daily living, >5% weight loss in the previous 6 months. 12

CDI was defined according to the presence of at least one stool sample with laboratory confirmation 13

of positive Clostridium difficile toxin assay in a patient with diarrhea or visualization of 14

pseudomembranes on colonoscopic examination. Diarrhea was defined as 3 or more loose bowel 15

movements per day, with no other known cause. All other patients fulfilling eligibility criteria were 16

considered as CDI-negative. 17

Since the cumulative incidence of CDI in our unit, according to Healthcare Hospital Direction, has 18

been 8 cases per 100 patients in 2013 and considering that the number of admissions performed was 19

1432, a period of observation of six months (January-June 2013) was considered sufficient to reach 20

the target number of 452 patients, to have an absolute precision of 2.5% and a confidence level of 21

95%. 22

For each patient, several variables were considered for possible association with CDI: age, sex, 23

hospital stay before transferal to our unit, total length of stay, exposure to antibacterial, antifungal 24

and PPI treatment, CIRS Severity Index, CIRS Comorbidity Score, overall number of 25

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comorbidities. Presence of specific comorbidities (including cardiovascular disease, respiratory 1

disease, dementia, stroke, cancer, CKD, liver disease) and inhospital death were also recorded. 2

CIRS scores are validated in scientific literature for geriatric hospitalized patients17

and based on 3

the assignment of a score (from 0 to 4) equivalent to disease severity to each of 14 items that 4

represent possible organs of systems that can be affected by a chronic disease. CIRS Comorbidity 5

Score is the sum of all scores assigned to 14 items, while CIRS Severity Index is the number of 6

items that rank three or four in disease severity. All the considered variables were collected from 7

clinical records of eligible patients. CIRS scores had been calculated for each patient at the time of 8

admission to our ward and recorded in clinical records. Subjects with missing CIRS scores in 9

clinical records were not considered for final analysis. 10

Variables were reported as percentages, means ± standard deviation (SD) or, for non-normally 11

skewed distributions, median and interquartile range (IQR). Characteristics of subjects were 12

compared across CDI positivity and negativity using Kruskal-Wallis tests for continuous variables, 13

after adjustment for age and sex. Logistic regression models were used to examine the relationship 14

between CIRS Comorbidity Score after stratifying in quartiles and risk of having CDI. Logistic 15

regression models were adjusted for age and sex (model 1), and quartiles of CIRS Comorbidity 16

Score were also adjusted for other variables that were significant in the univariate analysis or for 17

variables that are reported as significantly associated in literature (model 2). To further verify the 18

possible association between CIRS Comorbidity Score and CDI, considering that antibiotic 19

treatment is by far the most common and powerful risk factor for CDI, an additional analysis was 20

also performed after categorizing patients according to exposure to antibiotic treatment. All 21

analyses were performed using SAS statistical package, version 9.1 (SAS Institute Inc., Cary, North 22

Carolina) with a type I error of 0.05. 23

This study was carried out without any extra-institutional funding. The study protocol was approved 24

by local ethics committee (Comitato Etico per Parma, ID 10739). All the clinical investigations 25

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were performed according to the principles expressed in the Declaration of Helsinki. Given the 1

retrospective design of the study, specific informed consent was obtained, according to Italian law. 2

3

4

5

6

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RESULTS 1

2

The total number of patients admitted to the Subacute Care ward from January to June 2013 was 3

633 (298 M, 335 F). One hundred and twenty-eight of them (60 M, 68 F) were excluded from the 4

study for not meeting inclusion criteria (120 patients, 56 M, 64 F) or for lack of CIRS scores in 5

clinical records (8 patients, 4 M, 4 F). The remaining 505 patients (238 M, 267 F, mean age 81±10 6

years old) were considered for statistical analysis. Their general characteristics are summarized in 7

Table 1. The most frequent chronic comorbidities were cardiovascular disease (55%), respiratory 8

disease (44%), dementia (43%) and stroke (30%). Exposures to PPI, systemic antibacterial and 9

antifungal treatment in the whole cohort were respectively 87%, 44% and 13%. Inhospital mortality 10

rate was 22%. 11

Forty-three patients out of 505 (22 M, 21 F, 8.5%) were classified as CDI-positive according to 12

criteria exposed above. The other 462 patients were therefore classified as CDI-negative. As 13

highlighted in Table 2, at an age- and sex-adjusted analysis, both CIRS Severity Index (median 14

1.92, IQR 1.77 to 2.23, vs 1.85, IQR 1.54 to 2.10, p=0.028) and CIRS Comorbidity Score (median 15

14, IQR 11 to 18, vs 13, IQR 8 to 17, p=0.036) were significantly higher in CDI-positive than in 16

CDI-negative patients. Moreover, also antibacterial and antifungal therapy exposure was higher for 17

CDI-positive patients, while PPI treatment was not (Table 2). 18

At an age- and sex-adjusted logistic regression model (Table 3, model 1), subjects in the highest 19

quartile of CIRS Comorbidity Score (values≥17) carried out a significantly higher risk of CDI, as 20

compared with those in the lowest quartile (values<9) (OR 2.89, 95% CI 1.02 to 8.54, p=0.045). 21

This relationship was also confirmed at a multivariable logistic regression model (model 2), shown 22

in Table 3 too (OR for highest vs lowest quartile 4.72, 95% CI 1.18 to 18.87, p=0.03). Other factors 23

significantly associated with CDI at multivariate analysis include number of comorbidities (OR 24

1.55, 95% CI 1.04 to 2.30, p=0.03), antibacterial therapy (OR 2.62, 95% CI 1.21 to 5.66, p=0.01) 25

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and duration of hospital stay before transferal to our unit (OR 1.03, 95% CI 1.00 to 1.05, p=0.04) 1

(Table 3). 2

To explore the relationship between multimorbidity measured through CIRS Comorbidity Score 3

and CDI, a further analysis was performed categorizing patients according to exposure to antibiotics 4

(223 patients with antibiotic, of whom 31 CDI positive, and 282 patients without antibiotic, of 5

whom 12 CDI positive). CIRS Comorbidity Score was significantly higher in CDI positive subjects 6

only in antibiotic-treated group (median CDI positive 15, IQR 12 to 18, vs median CDI negative 12, 7

IQR 8 to 17, p=0.016). 8

9

10

11

12

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DISCUSSION 1

2

This study demonstrates that, in a cohort of multimorbid elderly patients with prolonged hospital 3

stay, multimorbidity may be significantly and independently associated with CDI onset, especially 4

for those who have a very high number of chronic comorbidities (i.e., those who have a CIRS 5

Comorbidity Score ≥17). Antibiotic therapy and duration of hospital stay are confirmed risk factors 6

for CDI in our cohort. 7

Our study has several limitations. First, even if the considered cohort has a high burden of 8

multimorbidity and a high incidence of CDI, the retrospective design did not allow to explore the 9

contribution of single diseases in CDI risk and to provide data about Clostridium difficile 10

colonization status in subjects without CDI, that can represent an important risk factor in elderly19

. 11

Second, no phenotypical characterization of Clostridium difficile strains is provided, while the risk 12

of CDI at least partly relies on microbe-related factors20

. Moreover, full data about polypharmacy, 13

notably exposure to antidepressants, opioids and non-steroidal anti-inflammatory drugs, and 14

functional status of patients, all factors recently associated with a possible increased risk of CDI21-

15

24, are lacking. 16

Even if CIRS scores have been demonstrated as accurate in detecting the overall comorbidity 17

burden and defining prognosis in hospitalized elderly patients18

, its narrow range of values may not 18

be sufficiently accurate for describing clinical complexity. Results shown in Table 3 actually 19

demonstrate that the simple number of comorbidities is independently associated to CDI risk, and 20

that CIRS Comorbidity Score reaches a significant association when ≥17, as compared to those 21

patients in the first quartile (<9). Thus, these results suggest that multimorbidity may be involved in 22

defining CDI risk, in addition to antibiotic therapy and prolonged hospital stay, as already 23

established in medical literature. 24

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Very few studies have explored the association between overall multimorbidity burden and CDI 1

onset and, to our knowledge, none has assessed multimorbidity through CIRS before, which is 2

perhaps the main strength of the present study. Moreover, the focus on multimorbid elderly 3

hospitalized patients allows to understand the contribution of other risk factors besides antibiotic 4

therapy exactly on those patients who most frequently develop CDI13

. A recent study carried out 5

with a geriatric methodology, considering comorbidity in a literature-validated index (Charlson 6

score) very similar to CIRS, failed to demonstrate a correlation between multimorbidity and CDI 7

onset and severity, but included in final analysis also patients younger than 6524

. However, in other 8

reports Charlson score was proven as a significant determinant of an adverse outcome in patients 9

with hospital-acquired CDI25-26

. Stevens and colleagues14

recently demonstrated that a comorbidity 10

index specifically designed for infectious diseases investigations (Chronic Disease Score-Infectious 11

Disease, CDS-ID) is a significant predictor of CDI onset in a large retrospective cohort of adult 12

inpatients. However, this research was not focused on high-risk patients (i.e., geriatric patients 13

admitted to an internal medicine ward and with prolonged hospital stay) but included all subjects 14

admitted to a third-level specialty care hospital who received at least two days of antibiotic 15

treatment. Moreover, CDS-ID considers only some possible comorbidities (peptic ulcer disease, 16

kidney disease, diabetes, cancer, respiratory illness, organ transplant) and not the complex broad 17

spectrum of disease that may coexist in geriatric frail patients, like CIRS15

. However, our results 18

seem to confirm that multimorbidity plays a relevant role also in a geriatric setting. 19

It is noteworthy that in our cohort 12 CDI-positive patients out of 43 were not exposed to previous 20

antibiotic treatment. In this cases, it is plausible that the crucial factor is wide ward antibiotic use 21

level, increasing environmental contamination and ecologic pressure7, and that multimorbidity plays 22

only a secondary role. 23

According to our results, PPI exposure was not associated to CDI onset, consistently with most 24

recent literature reports12

. However, the widespread use of these drugs in our cohort (86.5%) may 25

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represent a possible bias and issue a warning on possible inappropriateness of PPI prescription in 1

elderly multimorbid patients. A recent multicentric study carried out in our country has actually 2

demonstrated that PPI administration in internal medicine wards is unjustified in 62% of cases, thus 3

highlighting the need for thorough medication revision both at admission and discharge from 4

hospital27

. Moreover, in oldest old patients the prevalence of atrophic gastritis is very high, thus 5

making gastric acid suppression virtually useless28

. 6

In conclusion, multimorbidity may represent an additional risk factor for CDI onset in elderly frail 7

patients with prolonged hospital stay, alongside with antibiotic treatment and duration of stay itself. 8

CIRS Comorbidity Score may be a useful tool to estimate this additional risk, especially in patients 9

who undergo long courses of antibiotic treatment, although the simple evaluation of the number of 10

chronic comorbidities may carry out a higher accuracy. Since elderly subjects are nowadays those 11

who most frequently develop CDI, further studies are needed to explore the association between 12

this infection and the domains of multimorbidiy, frailty and polypharmacy, that are intrinsic 13

features of geriatric patients admitted to hospital. A precise definition of risk factors, other than 14

antibiotic therapy, involved in CDI will allow to issue effective preventive measures and limit CDI-15

related health costs, that are constantly increasing29

. 16

17

18

19

20

21

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CONTRIBUTORS 1

A.T. and A.N. are responsible for study concept and design, drafting the manuscript and critical 2

revision of the manuscript. G.F. is responsible for statistical analysis and interpretation of data. 3

B.P., I.L. and L.G. are responsible for data collection, interpretation and critical revision of the 4

manuscript. F.T. and M.V. are responsible for study concept and design and critical revision of the 5

manuscript. F.L. is responsible for statistical analysis, interpretation of data and critical revision of 6

the manuscript. M.M. and T.M. are responsible for study concept and design and critical revision of 7

the manuscript. 8

9

10

FUNDING 11

This research received no specific grant form any funding agency in the public, commercial or not-12

for-profit section and was carried out without any extra-institutional funding. 13

14

15

COMPETING INTERESTS 16

None declared for all authors. 17

18

19

PATIENT CONSENT 20

Obtained according to Italian law. 21

22

23

ETHICS APPROVAL 24

Approved by the Ethics Committee of Parma province (ID n. 10739). 25

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States. N Engl J Med 2015;372:825-834. 5

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Clostridium difficile infections. Estimating all-cause mortality and length of stay. 7

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Clostridium difficile infection: a population-based study. Am J Gastroenterol 2012;107:89-19

95. 20

9. Wilcox MH, Mooney L, Bendall R, Settle CD, Fawley WN. A case-control study of 21

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10. Chitnis AS, Holzbauer SM, Belflower RM, et al. Epidemiology of community-associated 1

Clostridium difficile infection, 2009 through 2011. JAMA Intern Med 2013;173(14):1359-2

1367. 3

11. Dial S, Delaney JAC, Barkun AN, Suissa S. Use of gastric acid-suppressive agents and the 4

risk of community-acquired Clostridium difficile-associated disease. JAMA 2005;294:2989-5

2995. 6

12. Novack L, Kogan S, Gimpelevich L, et al. Acid suppression therapy does not predispose to 7

Clostridium difficile infection: the case of the potential bias. PLoS One 2014;9(10):e110790. 8

13. Mellace L, Consonni D, Jacchetti G, et al. Epidemiology of Clostridium difficile-associated 9

disease in internal medicine wards in Northern Italy. Intern Emerg Med 2013;8:717-723. 10

14. Stevens V, Concannon C, van Wijngaarden E, McGregor J. Validation of the chronic disease 11

score-infectious disease (CDS-ID) for the prediction of hospital-associated Clostridium 12

difficile infection (CDI) within a retrospective cohort. BMC Infect Dis 2013;13:150. 13

15. McGregor JC, Kim PW, Perencevich EN, et al. Utility of Chronic Disease Score and Charlson 14

Comorbidity Index as comorbidity measures for use in epidemiologic studies of antibiotic-15

resistant organisms. Am J Epidemiol 2005;161(5):483-493. 16

16. Nouvenne A, Ticinesi A, Lauretani F, et al. Comorbidities and disease severity as risk factors 17

for carbapenem-reistant Klebsiella pneumoniae colonization: report of an experience in an 18

internal medicine unit. PLoS One 2014;9(10):e110001. 19

17. Salvi F, Miller MD, Grilli A, et al. A manual of guidelines to score the modified cumulative 20

illness rating scale and its validation in acute hospitalized elderly patients. J Am Geriatr Soc 21

2008;56:1926-1931. 22

18. Zekry D, Loures Valle BH, Lardi C, et al. Geriatrics index of comorbidity was the most 23

accurate predictor of death in geriatric hospital among six comorbidity scores. J Clin 24

Epidemiol 2010;63:1036-1044. 25

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19. Alasmari F, Seiler SM, Hink T, Burnham CAD, Dubberke ER. Prevalence and risk factors for 1

asymptomatic Clostridium difficile carriage. Clin Infect Dis 2014;59:216-222. 2

20. Loo VG, Bourgault AM, Poirier L, et al. Host and pathogen factors for Clostridium difficile 3

infection and colonization. N Engl J Med 2011;365:1693-1703. 4

21. Rogers MAM, Greene MT, Young VB, et al. Depression, antidepressant medications, and risk 5

of Clostridium difficile infection. BMC Med 2013;11:121. 6

22. Suissa D, Delaney JAC, Dial S, Brassard P. Non-steroidal anti-inflammatory drugs and the 7

risk of Clostridium difficile-associated disease. Br J Clin Pharmacol 2012;74:370-375. 8

23. Mora AL, Salazar M, Pablo-Caeiro J, et al. Moderate to high use of opioid analgesics are 9

associated with an increased risk of Clostridium difficile infection. Am J Med Sci 10

2012;343:277-280. 11

24. Rao K, Micic D, Chenoweth E, et al. Poor functional status as a risk factor for severe 12

Clostridium difficile infection in hospitalized older adults. J Am Geriatr Soc 2013;61:1738-13

1742. 14

25. Rodriguez-Pardo D, Almirante B, Bartolomé RM, et al. Epidemiology of Clostridium difficile 15

infection and risk factors for unfavorable clinical outcomes: results of a hospital-based study 16

in Barcelona, Spain. J Clin Microbiol 2013;51:1465-1473. 17

26. Hardt C, Berns T, Treder W, Dumoulin FL. Univariate and multivariate analysis of risk 18

factors for severe Clostridium difficile-associated diarrhea: importance of co-morbidity and 19

serum C-reactive protein. World J Gastroenterol 2008;14:4338-4341. 20

27. Pasina L, Nobili A, Tettamanti M, et al. Prevalence and appropriateness of drug prescriptions 21

for peptic ulcer and gastro-esophageal reflux in a cohort of hospitalized elderly. Eur J Intern 22

Med 2011;22:205-210. 23

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28. Goni E, Caleffi A, Nouvenne A, et al. Gastric function assessed by Gastropanel® in very old 1

patients (over 80 years old) and appropriateness of PPI administration. Helicobacter 2

2014;19(S1):166. 3

29. Eckmann C, Wasserman M, Latif F, Roberts G, Beriot-Mathiot A. Increased hospital length 4

of stay attributable to Clostridium difficile infection in patients with four co-morbidities: an 5

analysis of hospital episode statistics in four European countries. Eur J Health Econ 6

2013;14:835-846. 7

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TABLE 1 1

Main features of the population studied (n=505). 2

3

Mean age, years 80 ± 11

Men, % (n) 47 (238/505)

Mean hospital stay before transferal to our unit,

days

20.8 ± 19.8

Mean length of stay in our unit, days 15.5 ± 11.9

Mean total length of stay, days 36.2 ± 24.3

Death, % (n) 22 (108/505)

Infection (Clostridium difficile excluded),

prevalence % (n)

62 (313/505)

Cardiovascular disease, prevalence % (n) 55 (278/505)

Respiratory disease, prevalence % (n) 44 (222/505)

Dementia, prevalence % (n) 43 (216/505)

Stroke, prevalence % (n) 30 (150/505)

Cancer, prevalence % (n) 25 (126/505)

Chronic kidney disease, prevalence % (n) 24 (122/505)

Liver disease, prevalence % (n) 9 (48/505)

Clostridium difficile infection, prevalence % (n) 8.5 (43/505)

PPI treatment, % (n) 87 (434/505)

Antibiotic treatment, % (n) 44 (223/505)

Antifungal treatment, % (n) 13 (67/505)

4

Data reported as mean ± standard deviation or percentages whenever appropriate. 5

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TABLE 2 1

Comparison between general characteristics of Clostridium difficile infection (CDI)-positive and 2

CDI-negative patients. 3

4

CDI-

POSITIVE

PATIENTS

(N=43)

CDI-

NEGATIVE

PATIENTS

(N=462)

Age and

sex-

adjusted

p*

Mean age, years 82.6 ± 8.5 80.4 ± 10.4 0.20

Men, n (%) 22 (51.2) 216 (46.8) 0.41


days

23 ± 18 21 ± 20 0.43

Mean length of stay in our unit, days 30 ± 16 14 ± 11 0.02

Mean total length of stay, days 53 ± 28 35 ± 27 0.03

CIRS Severity Index, median (IQR) 1.92 (1.77-2.23) 1.85 (1.54-2.10) 0.028

CIRS Comorbidity Score, median (IQR) 14 (11-18) 13 (8-17) 0.036

Number of comorbidities, median (IQR) 3 (3-5) 3 (2-4) 0.01

PPI chronic treatment, n (%) 40 (93) 394 (85.8) 0.19

Antibacterial treatment, n (%) 31 (72.1) 192 (41.6) <0.001

Antifungal treatment, n (%) 10 (23.3) 57 (12.3) 0.03

* when appropriate. 5

Data reported as mean ± standard deviation or median and interquartile range (IQR) whenever 6

appropriate. 7

8

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TABLE 3 1

Factors associated to a higher risk for Clostridium difficile infection at age- and sex-adjusted (model 2

1) and multivariate (model 2) logistic regression models. 3

ODDS

RATIO 95% CI p

Model 1 (age- and sex-adjusted)

CIRS Comorbidity Score, First quartile (<9) - - -

Second quartile (≥9, <13) 2.49 0.84-7.42 0.10

Third quartile (≥13, <17) 2.54 0.85-7.64 0.09

Fourth quartile (≥17) 2.89 1.02-8.54 0.045

Age 1.01 0.97-1.05 0.47

Sex (women vs men) 0.78 0.41-1.49 0.45

Model 2 (fully adjusted)

CIRS Comorbidity Score, First quartile (<9) - - .

Second quartile (≥9, <13) 3.18 0.43-23.48 0.25

Third quartile (≥13, <17) 2.39 0.78-7.29 0.12

Fourth quartile (≥17) 4.72 1.18-18.87 0.03

Number of chronic comorbidities 1.55 1.04-2.30 0.03

Antibacterial therapy 2.62 1.21-5.66 0.01

Antifungal therapy 1.56 0.68-3.58 0.29

PPI treatment 1.58 0.45-5.51 0.47

Age 1.03 0.98-1.07 0.17

Sex (women vs men) 0.69 0.35-1.39 0.30

Mean length of stay in our unit 1.01 0.99-1.02 0.60

Mean hospital stay before transferal to our

unit 1.03 1.00-1.05 0.04

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STROBE 2007 (v4) checklist of items to be included in reports of observational studies in epidemiology*

Checklist for cohort, case-control, and cross-sectional studies (combined)

Section/Topic Item # Recommendation Reported on page #

Title and abstract 1 (a) Indicate the study’s design with a commonly used term in the title or the abstract Page 1, line 2

(b) Provide in the abstract an informative and balanced summary of what was done and what was found Pages 3-4

Introduction

Background/rationale 2 Explain the scientific background and rationale for the investigation being reported From page 6 line 3 to

page 7 line 2

Objectives 3 State specific objectives, including any pre-specified hypotheses Page 7, lines 3-7

Methods

Study design 4 Present key elements of study design early in the paper Page 8, line 3

Setting 5 Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data

collection Page 8, lines 3-8

Participants 6 (a) Cohort study—Give the eligibility criteria, and the sources and methods of selection of participants. Describe

methods of follow-up

Case-control study—Give the eligibility criteria, and the sources and methods of case ascertainment and control

selection. Give the rationale for the choice of cases and controls

Cross-sectional study—Give the eligibility criteria, and the sources and methods of selection of participants

Page 8, lines 9-17 and

from page 8, line 23

to page 9 line 10

(b) Cohort study—For matched studies, give matching criteria and number of exposed and unexposed

Case-control study—For matched studies, give matching criteria and the number of controls per case Page 8, lines 18-19

Variables 7 Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic

criteria, if applicable Page 8, lines 13-17

and from page 8 line

23 to page 9 line 2

Data sources/ measurement 8* For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe

comparability of assessment methods if there is more than one group Page 9 lines 3-10

Bias 9 Describe any efforts to address potential sources of bias Page 9 lines 14-21

Study size 10 Explain how the study size was arrived at Page 8 lines 18-22

Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen

and why Page 9 lines 11-12

and 16-21

Statistical methods 12 (a) Describe all statistical methods, including those used to control for confounding Page 9 lines 11-23

(b) Describe any methods used to examine subgroups and interactions Page 9 lines 18-21

(c) Explain how missing data were addressed Page 9 lines 9-10

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(d) Cohort study—If applicable, explain how loss to follow-up was addressed

Case-control study—If applicable, explain how matching of cases and controls was addressed

Cross-sectional study—If applicable, describe analytical methods taking account of sampling strategy

Not applicable

(e) Describe any sensitivity analyses Page 8 lines 18-22

Results

Participants 13* (a) Report numbers of individuals at each stage of study—eg numbers potentially eligible, examined for eligibility,

confirmed eligible, included in the study, completing follow-up, and analysed Page 11 lines 3-7

(b) Give reasons for non-participation at each stage Page 11 lines 3-7

(c) Consider use of a flow diagram Not applicable

Descriptive data 14* (a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and

potential confounders Page 11, lines 7-13

and Table 1

(b) Indicate number of participants with missing data for each variable of interest Page 11 lines 5-6

(c) Cohort study—Summarise follow-up time (eg, average and total amount) Not applicable

Outcome data 15* Cohort study—Report numbers of outcome events or summary measures over time From page 11 line 13

to page 12 line 8

Case-control study—Report numbers in each exposure category, or summary measures of exposure Not applicable

Cross-sectional study—Report numbers of outcome events or summary measures Not applicable

Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95%

confidence interval). Make clear which confounders were adjusted for and why they were included From page 11 line 13

to page 12 line 2;

Tables 2-3

(b) Report category boundaries when continuous variables were categorized Page 11 lines 20-21

(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period Not applicable

Other analyses 17 Report other analyses done—eg analyses of subgroups and interactions, and sensitivity analyses Page 12 lines 3-8

Discussion

Key results 18 Summarise key results with reference to study objectives Page 13 lines 3-7

Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction

and magnitude of any potential bias Page 13 lines 8-24

Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results

from similar studies, and other relevant evidence Page 14 line 1 – page

15 line 6

Generalisability 21 Discuss the generalisability (external validity) of the study results Page 13 lines 8-24

Other information

Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on

which the present article is based Page 16 lines 12-13

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*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies.

Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE

checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at

http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.

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Multimorbidity in elderly hospitalized patients and risk of Clostridium difficile infection: a retrospective study with

Cumulative Illness Rating Scale (CIRS)

Journal: BMJ Open

Manuscript ID: bmjopen-2015-009316.R1

Article Type: Research

Date Submitted by the Author: 18-Aug-2015

Complete List of Authors: Ticinesi, Andrea; University of Parma, Department of Clinical and Experimental Medicine Nouvenne, Antonio; University of Parma, Department of Clinical and

Experimental Medicine Folesani, Giuseppina; University of Parma, Italian Workers' Compensation Authority (INAIL) Research Center at University of Parma Prati, Beatrice; Parma University Hospital, Internal Medicine and Critical Subacute Care Unit Morelli, Ilaria; Parma University Hospital, Internal Medicine and Critical Subacute Care Unit Guida, Loredana; Parma University Hospital, Internal Medicine and Critical Subacute Care Unit Turroni, Francesca; University of Parma, Department of Life Sciences Ventura, Marco; University of Parma, Department of Life Sciences Lauretani, Fulvio; Parma University Hospital, Geriatrics Unit

Maggio, Marcello; University of Parma, Department of Clinical and Experimental Medicine Meschi, Tiziana; University of Parma, Department of Clinical and Experimental Medicine

<b>Primary Subject Heading</b>:

Geriatric medicine

Secondary Subject Heading: Infectious diseases, Gastroenterology and hepatology

Keywords: Multimorbidity, Clostridium difficile, Elderly, Proton pump inhibitors, Antibiotic


BMJ Open on 17 F

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1

Multimorbidity in elderly hospitalized patients and risk of Clostridium difficile infection: a 1

retrospective study with Cumulative Illness Rating Scale (CIRS) 2

Andrea Ticinesi1-2

, Antonio Nouvenne1-2*

, Giuseppina Folesani3, Beatrice Prati

1-2, Ilaria Morelli

1, 3

Loredana Guida1, Francesca Turroni

4, Marco Ventura

4, Fulvio Lauretani

5, Marcello Maggio

2, 4

Tiziana Meschi1-2

5

1 Internal Medicine and Critical Subacute Care Unit, Parma University Hospital, Parma, Italy 6

2 Department of Clinical and Experimental Medicine, University of Parma, Parma, Italy 7

3 Italian Workers’ Compensation Research Center at University of Parma, Parma, Italy 8

4 Laboratory of Probiogenomics, Department of Life Sciences, University of Parma, Parma, Italy 9

5 Geriatrics Unit, Parma University Hospital, Parma, Italy 10

11

*Corresponding author: 12

Antonio Nouvenne M.D. 13

Internal Medicine and Critical Subacute Care Unit 14

Parma University Hospital, 15

University of Parma 16

Department of Clinical and Experimental Medicine 17

Via A. Gramsci 14 18

43126 PARMA 19

ITALY 20

Phone: +39 0521 703626 21

Mobile: +39 3492258317 22

Fax: +39 0521 702383 23

e-mail: [email protected] 24

25

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2

Abstract word count: 229 1

Main text word count: 2209 2

Tables: 3 3

Figures: 1 4

Number of references: 29 5

Key words: multimorbidity, elderly, CIRS, Clostridium difficile, proton pump inhibitors, 6

antibiotic, antifungal 7

8

Other authors’ mail addresses: 9

Andrea Ticinesi: [email protected] 10

Giuseppina Folesani: [email protected] 11

Beatrice Prati: [email protected] 12

Ilaria Morelli: [email protected] 13

Loredana Guida: [email protected] 14

Francesca Turroni: [email protected] 15

Marco Ventura: [email protected] 16

Fulvio Lauretani: [email protected] 17

Marcello Maggio: [email protected] 18

Tiziana Meschi: [email protected] 19

20

21

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3

ABSTRACT 1

2

Objectives: To identify the role of chronic comorbidities, considered together in a literature-3

validated index (Cumulative Illness Rating Scale, CIRS), and antibiotic or proton pump inhibitor 4

(PPI) treatments as risk factors for hospital-acquired Clostridium difficile infection (CDI) in elderly 5

multimorbid hospitalized patients. 6

Design: Retrospective cohort study. 7

Setting: Subacute hospital geriatric care ward in Italy. 8

Participants: 505 (238 M, 268 F) elderly (age≥65) multimorbid patients. 9

Main outcome measures: The relationship between CDI and CIRS Comorbidity Score, number of 10

comorbidities, antibiotic, antifungal and PPI treatments, length of hospital stay was assessed 11

through age- and sex-adjusted and multivariate logistic regression models. CIRS Comorbidity Score 12

was handled after categorization in quartiles. 13

Results: Mean age was 80.7±11.3 years. Forty-three patients (22 M, 21 F) developed CDI. The 14

prevalence of CDI increased among quartiles of CIRS Comorbidity Score (3.9% first quartile vs 15

11.1% fourth quartile, age- and sex-adjusted p=0.03). In the multivariate logistic regression 16

analysis, subjects in the highest quartile of CIRS Comorbidity Score (≥17) carried a significantly 17

higher risk of CDI (OR 5.07, 95%CI 1.28 to 20.14, p=0.02) than subjects in the lowest quartile 18

(<9). The only other variable significantly associated with CDI was antibiotic therapy (OR 2.62, 19

95%CI 1.21 to 5.66, p=0.01). PPI treatment was not associated to CDI. 20

Conclusions: Multimorbidity, measured through CIRS Comorbidity Score, is independently 21

associated with the risk of CDI in a population of elderly subjects with prolonged hospital stay. 22

23

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STRENGTHS AND LIMITATIONS OF THIS STUDY 1

• Calculation of CIRS (Cumulative Illness Rating Scale) scores is a rapid inexpensive way to 2

determine the overall multimorbidity burden of elderly hospitalized patients. 3

• The association of multimorbidity with Clostridium difficile infection (CDI) has never been 4

extensively studied in elderly multimorbid subjects with prolonged hospital stay. 5

• Retrospective study design may limit the generalizability of results. 6

• Asymptomatic carriers of Clostridium difficile were not identified. 7

• Physical performance of patients and polypharmacy were not considered as potential 8

confounders. 9

10

11

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INTRODUCTION 1

2

Clostridium difficile infection (CDI) is one of the leading healthcare-associated infections in 3

Western countries, responsible for diarrhea and colitis in subjects with abnormal gut microbiota and 4

impaired local immunity1. Even if the prevalence of community-acquired CDI is continuously 5

rising2, most cases occur in patients with prolonged hospitalization, accounting for a rise in 6

mortality and burden of healthcare costs3. 7

Systemic antibiotic therapy in the previous 30 days has been traditionally considered as the most 8

important risk factor for CDI. Virtually, exposure to all types of antibiotics has been linked to an 9

increased risk of CDI, with the highest odds ratios for cephalosporins, clindamycin and 10

carbapenems4. This risk is also consistently influenced by timing and duration of antibiotic 11

treatment5. 12

However, a significant number of hospital- and community-acquired CDI occurs in patients without 13

any recent antibiotic exposure in their personal history6-8

. 14

As such, although widespread antibiotic use can increase environmental Clostridium difficile 15

contamination and thus the risk of infection also for those who are not on antibiotic treatment7, 16

alternative factors may be involved. Older age, cancer chemotherapy, inflammatory bowel disease, 17

chronic kidney disease (CKD), organ transplantation, immunodeficiency and exposure to 18

asymptomatic carriers or infected patients have all been recognized as independent risk factors1,9,10

. 19

Chronic proton-pump inhibitor (PPI) therapy may also be associated with CDI11

, even if some 20

studies have questioned the strength of this independent association12

. 21

Since most hospital-acquired cases of CDI occur in oldest-old patients, with a high burden of 22

chronic comorbidities affecting multiple organs and systems13

, multimorbidity itself may play a 23

relevant role in defining the risk of CDI14

. Multimorbidity scores have been demonstrated as useful 24

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6

tools for predicting the risk of severe hospital-acquired infections by other multi-drug resistant 1

bacteria15,16

. 2

Therefore, the aim of this retrospective hospital-based study is to evaluate the risk factors of CDI in 3

a cohort of multimorbid hospitalized elderly with a prolonged hospital stay. We focus on the 4

possible role of Cumulative Illness Rating Scale (CIRS) Comorbidity Score, a literature-validated17

5

index particularly useful in defining the prognostic trajectory of geriatric hospitalized patients18

. 6

7

8

9

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METHODS 1

2

All clinical records of patients admitted to the Critical Subacute Care Unit of Parma University 3

Hospital, in Northern Italy, from January 1st to June 30

th 2013 were analyzed by using a 4

retrospective cohort study design. This unit is an internal medicine ward primarily devoted to 5

elderly multimorbid patients requiring prolonged hospital stay for critical conditions. Admission is 6

generally planned after some days of hospital stay in other acute-care medical or surgical wards. 7

Inclusion criteria for this study were age ≥65 years old, absence of a well-defined terminal 8

condition with a survival prognosis <30 days, presence of at least two of the following criteria: 9

reduced muscular strength, reduced gait speed, forced bed rest, lack of autonomy in activities of 10

daily living, >5% weight loss in the previous 6 months. 11

Clostridium difficile infection was defined according to the presence of at least one stool sample 12

with laboratory confirmation of positive Clostridium difficile toxin assay in a patient with diarrhea 13

or visualization of pseudomembranes on colonoscopic examination. Diarrhea was defined as 3 or 14

more loose bowel movements per day, with no other known cause. All other patients fulfilling 15

eligibility criteria were considered as CDI-negative. 16

The cumulative incidence of CDI in our unit, according to data from Healthcare Hospital Direction, 17

consisted of 8 cases per 100 patients in 2013, with 1432 unit-admissions in the same year. 18

Therefore, we considered six months (January-June 2013) as a sufficient time-period of observation 19

to reach the target number of 452 patients, and in order to achieve an absolute precision of 2.5% and 20

a confidence level of 95%. 21

For each patient, the following variables were considered for possible association with CDI: age, 22

sex, hospital stay before transferal to our unit, total length of stay, antibacterial, antifungal and PPI 23

treatment, CIRS Comorbidity Score, overall number of comorbidities. Presence of specific 24

comorbidities (including cardiovascular disease, respiratory disease, dementia, stroke, cancer, CKD, 25

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liver disease) and inhospital death were also recorded. CIRS is a tool validated in scientific 1

literature for geriatric hospitalized patients17

. The calculated score ranging from 0 to 4 is the result 2

of disease severity for each of 14 items representing possible organs affected by a chronic disease. 3

CIRS Comorbidity Score is the sum of all scores assigned to 14 items. CIRS Severity Index is the 4

number of items ranking three or four in disease severity. All the considered variables were 5

collected from clinical records of eligible patients. CIRS Comorbidity Score was calculated for each 6

patient at the time of admission to our ward and recorded in clinical records. Subjects with missing 7

data in clinical records were not considered for final analysis. 8

Variables were reported as number and percentage, mean ± standard deviation (SD) or, for non-9

normally skewed distributions, median and interquartile range (IQR). Characteristics of subjects 10

were compared across CDI positivity and negativity using Kruskal-Wallis tests for continuous 11

variables, after adjustment for age and sex. Logistic regression models were used to examine the 12

relationship between CIRS Comorbidity Score after stratification in quartiles and the risk of having 13

CDI. Logistic regression models were adjusted for age and sex (model 1). The relationship between 14

quartiles of CIRS Comorbidity Score and CDI was also adjusted for other variables that were 15

significant in the univariate analysis (model 2). Since antibiotic treatment is the most common and 16

powerful risk factor for CDI, an additional analysis was also performed after categorizing patients 17

according to exposure to antibiotic treatment to better test the association between CIRS 18

Comorbidity Score and CDI. All analyses were performed using SAS statistical package, version 19

9.1 (SAS Institute Inc., Cary, North Carolina) with a type I error of 0.05. 20

This study was carried out without any extra-institutional funding. The study protocol was approved 21

by local ethics committee (Comitato Etico per Parma, ID 10739). All the clinical investigations 22

were performed according to the principles expressed in the Declaration of Helsinki. Given the 23

retrospective design of the study, specific informed consent was obtained, according to Italian law. 24

25

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RESULTS 1

2

The total number of patients admitted to the Subacute Care ward from January to June 2013 was 3

633 (298 M, 335 F). One hundred and twenty-eight of them (60 M, 68 F) were excluded from the 4

study for not meeting inclusion criteria (120 patients, 56 M, 64 F) or for missing CIRS scores in 5

clinical records (8 patients, 4 M, 4 F). The remaining 505 patients (238 M, 267 F, mean age 81±10 6

years old) were considered for statistical analysis. The general characteristics of 505 patients are 7

summarized in Table 1. The most frequent chronic comorbidities were cardiovascular disease 8

(55%), respiratory disease (44%), dementia (43%) and stroke (30%). Exposure to PPI, systemic 9

antibacterial and antifungal treatment in the whole cohort were respectively 87%, 44% and 13%. In-10

hospital mortality rate was 22%. 11

Forty-three patients out of 505 (22 M, 21 F, 8.5%) were classified as CDI-positive according to 12

criteria exposed above. The other 462 patients were therefore classified as CDI-negative. The 13

prevalence of CDI by quartile of CIRS Comorbidity Score is shown in Figure 1. Age- and sex-14

adjusted test for linear trend showed a significant association between CDI and quartiles of CIRS 15

comorbidity score (p=0.03). 16

Table 2 shows a comparison between CDI-positive and negative patients for all other considered 17

covariates. Notably, CDI-positive patients had significantly higher rates of antibacterial and 18

antifungal therapy and longer hospital stays. PPI treatment did not significantly differ between the 19

two groups. 20

In age- and sex-adjusted logistic regression model (Table 3, model 1), subjects in the highest 21

quartile of CIRS Comorbidity Score (values≥17) carried out a significantly higher risk of CDI, as 22

compared with those in the lowest quartile (values<9) (OR 2.89, 95% CI 1.02 to 8.54, p=0.045). 23

This relationship was also confirmed in the multivariable logistic regression model (model 2), 24

shown in Table 3 (OR for highest vs lowest quartile 5.07, 95% CI 1.28 to 20.14, p=0.02). The only 25

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other factor significantly associated with CDI was the antibacterial therapy (OR 2.61, 95% CI 1.21 1

to 5.64, p=0.01) (Table 3). An alternative multivariable logistic regression model including number 2

of comorbidities instead of CIRS Comorbidity Score is shown in Supplementary File. 3

To explore the relationship between multimorbidity measured through CIRS Comorbidity Score 4

and CDI, a further analysis was performed categorizing patients according to exposure to antibiotics 5

(223 patients with antibiotic, of whom 31 CDI positive, and 282 patients without antibiotic, of 6

whom 12 CDI positive). CIRS Comorbidity Score was significantly higher in CDI positive subjects 7

only in antibiotic-treated group (median CDI positive 15, IQR 12 to 18, vs median CDI negative 12, 8

IQR 8 to 17, p=0.016). 9

10

11

12

13

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DISCUSSION 1

2

This study shows that, in a cohort of elderly patients with prolonged hospital stay, multimorbidity is 3

significantly and independently associated with CDI onset, especially for those who have a very 4

high number of chronic comorbidities (i.e., those who have a CIRS Comorbidity Score ≥17). 5

Antibiotic therapy is confirmed to be an independent risk factor for CDI in our cohort. 6

Our study has several limitations. First, even if the considered cohort has a high burden of 7

multimorbidity and a high incidence of CDI, the retrospective design did not allow to explore the 8

contribution of single diseases and Clostridium difficile colonization status in defining the risk of 9

CDI19

. Second, no phenotypical characterization of Clostridium difficile strains is provided, while 10

the risk of CDI at least partly depends on microbe-related factors20

. Moreover, information on other 11

factors recently associated with risk of CDI, like polypharmacy, notably exposure to 12

antidepressants, opioids and non-steroidal anti-inflammatory drugs, and functional status21-24

, was 13

not available in this cohort. 14

Despite previous reports stating that CIRS Comorbidity Score is associated with overall 15

comorbidity burden and prognosis in hospitalized elderly patients18

, its narrow range of values may 16

not be sufficiently accurate for describing clinical complexity in our cohort. As shown in Table 3, 17

only CIRS Comorbidity Score in the top quartile (≥17) was significantly associated with CDI, as 18

compared to those patients in the first quartile (<9). Thus, multimorbidity exerts an additional 19

contribution to a lesser extent than antibiotic therapy in defining the risk of CDI. 20

Very few studies have explored the association between multimorbidity and CDI and, to our 21

knowledge, none has assessed it through CIRS. The information on CIRS is perhaps the main 22

strength of the present study. Moreover, the focus on a population of elderly patients with 23

prolonged hospital stay allows to understand the contribution of other risk factors besides antibiotic 24

therapy exactly on those patients who more frequently develop CDI13

. Interestingly, a recent study 25

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carried out with a geriatric methodology and considering multimorbidity in a literature-validated 1

index (Charlson score), failed to demonstrate a significant correlation between this parameter and 2

CDI onset and severity. Moreover, the cohort considered for the final analysis consisted also of 3

patients younger than 6524

. However, in other reports Charlson score was proven as a significant 4

determinant of an adverse outcome in patients with hospital-acquired CDI25-26

. Stevens and 5

colleagues14

recently demonstrated that a multimorbidity index specifically designed for infectious 6

disease investigations (Chronic Disease Score-Infectious Disease, CDS-ID) is a significant 7

predictor of CDI in a large retrospective cohort of adult inpatients. However, this research did not 8

focus on high-risk patients (i.e., geriatric patients admitted to an internal medicine ward and with 9

prolonged hospital stay) but included all subjects admitted to a third-level specialty care hospital 10

who received at least two days of antibiotic treatment. Moreover, unlike CIRS, CDS-ID takes into 11

account only some potential conditions, namely peptic ulcer disease, kidney disease, diabetes, 12

cancer, respiratory illness, organ transplant and not the complex broad spectrum of disease that may 13

coexist in geriatric complex patients15

. However, our results seem to confirm that multimorbidity 14

plays a relevant role also in a geriatric setting. 15

It is noteworthy that in our cohort 12 CDI-positive patients out of 43 were not exposed to previous 16

antibiotic treatment. In this case, it is plausible that the crucial factor for infection onset is the high 17

level of ward antibiotic use that may have increased the environmental contamination and ecologic 18

pressure7. Thus, multimorbidity may play only a secondary role. 19

PPI exposure was not associated to CDI onset, and these data is consistent with most of recent 20

literature12

. However, the widespread use of these drugs in our cohort (86.5%) may represent a 21

possible bias and questions the potential inappropriateness of PPI prescription in this specific 22

setting of older patients. A recent Italian multicenter study conducted in internal medicine wards 23

has shown that PPI administration is unjustified in 62% of cases, highlighting the need for thorough 24

medication revision both at hospital admission and discharge27

. Moreover, in oldest old patients the 25

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prevalence of atrophic gastritis is very high, thus making gastric acid suppression virtually 1

useless28

. 2

In conclusion, multimorbidity may represent an additional risk factor for CDI onset in elderly 3

patients with prolonged hospital stay, alongside with antibiotic treatment. CIRS Comorbidity Score 4

may be a useful tool to estimate this additional risk, especially in patients who undergo long term 5

antibiotic treatment. Since elderly subjects are nowadays those who most frequently develop CDI, 6

further studies are needed to explore the association between this infection and the domains of 7

multimorbidity, frailty and polypharmacy, that are intrinsic features of geriatric patients admitted to 8

hospital. A precise definition of risk factors, other than antibiotic therapy, involved in CDI will 9

allow to adopt effective preventive measures in order to limit the increasing CDI-related health 10

costs29

. 11

12

13

14

15

16

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CONTRIBUTORS 1

A.T. and A.N. are responsible for study concept and design, drafting the manuscript and critical 2

revision of the manuscript. G.F. is responsible for statistical analysis and interpretation of data. 3

B.P., I.L. and L.G. are responsible for data collection, interpretation and critical revision of the 4

manuscript. F.T. and M.V. are responsible for study concept and design and critical revision of the 5

manuscript. F.L. is responsible for statistical analysis, interpretation of data and critical revision of 6

the manuscript. M.M. and T.M. are responsible for study concept and design and critical revision of 7

the manuscript. 8

9

10

FUNDING 11

This research received no specific grant form any funding agency in the public, commercial or not-12

for-profit section and was carried out without any extra-institutional funding. 13

14

15

COMPETING INTERESTS 16

None declared for all authors. 17

18

19

PATIENT CONSENT 20

Obtained according to Italian law. 21

22

23

ETHICS APPROVAL 24

Approved by the Ethics Committee of Parma province (ID n. 10739). 25

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REFERENCES 1

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1548. 3

2. Lessa FC, Mu Y, Bamberg WM, et al. Burden of Clostridium difficile infection in the United 4

States. N Engl J Med 2015;372:825-834. 5

3. Lofgren ET, Cole SR, Weber DJ, Anderson DJ, Moehring RW. Hospital-acquired 6

Clostridium difficile infections. Estimating all-cause mortality and length of stay. 7

Epidemiology 2014;25(4):570-575. 8

4. Slimings C, Riley TV. Antibiotics and hospital-acquired Clostridium difficile infection: 9

update of systematic review and meta-analysis. J Antimicrob Chemother 2014;69:881-891. 10

5. Brown KA, Fisman DN, Moineddin R, Daneman N. The magnitude and duration of 11

Clostridium difficile infection risk associated with antibiotic therapy: a hospital cohort study. 12

PLoS One 2014;9(8):e105454. 13

6. Barletta JF, Sclar DA. Proton pump inhibitors increase the risk for hospital-acquired 14

Clostridium difficile infection in critically ill patients. Crit Care 2014;18:714. 15

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and the risks of Clostridium difficile infection. JAMA Intern Med 2015;175(4):626-633. 17

8. Khanna S, Pardi DS, Aronson SL, et al. The epidemiology of community-acquired 18

Clostridium difficile infection: a population-based study. Am J Gastroenterol 2012;107:89-19

95. 20

9. Wilcox MH, Mooney L, Bendall R, Settle CD, Fawley WN. A case-control study of 21

community-associated Clostridium difficile infection. J Antimicrob Chemother 2008;62:388-22

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10. Chitnis AS, Holzbauer SM, Belflower RM, et al. Epidemiology of community-associated 1

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1367. 3

11. Dial S, Delaney JAC, Barkun AN, Suissa S. Use of gastric acid-suppressive agents and the 4

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2995. 6

12. Novack L, Kogan S, Gimpelevich L, et al. Acid suppression therapy does not predispose to 7

Clostridium difficile infection: the case of the potential bias. PLoS One 2014;9(10):e110790. 8

13. Mellace L, Consonni D, Jacchetti G, et al. Epidemiology of Clostridium difficile-associated 9

disease in internal medicine wards in Northern Italy. Intern Emerg Med 2013;8:717-723. 10

14. Stevens V, Concannon C, van Wijngaarden E, McGregor J. Validation of the chronic disease 11

score-infectious disease (CDS-ID) for the prediction of hospital-associated Clostridium 12

difficile infection (CDI) within a retrospective cohort. BMC Infect Dis 2013;13:150. 13

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Comorbidity Index as comorbidity measures for use in epidemiologic studies of antibiotic-15

resistant organisms. Am J Epidemiol 2005;161(5):483-493. 16

16. Nouvenne A, Ticinesi A, Lauretani F, et al. Comorbidities and disease severity as risk factors 17

for carbapenem-reistant Klebsiella pneumoniae colonization: report of an experience in an 18

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17. Salvi F, Miller MD, Grilli A, et al. A manual of guidelines to score the modified cumulative 20

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2008;56:1926-1931. 22

18. Zekry D, Loures Valle BH, Lardi C, et al. Geriatrics index of comorbidity was the most 23

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19. Alasmari F, Seiler SM, Hink T, Burnham CAD, Dubberke ER. Prevalence and risk factors for 1

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20. Loo VG, Bourgault AM, Poirier L, et al. Host and pathogen factors for Clostridium difficile 3

infection and colonization. N Engl J Med 2011;365:1693-1703. 4

21. Rogers MAM, Greene MT, Young VB, et al. Depression, antidepressant medications, and risk 5

of Clostridium difficile infection. BMC Med 2013;11:121. 6

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risk of Clostridium difficile-associated disease. Br J Clin Pharmacol 2012;74:370-375. 8

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2012;343:277-280. 11

24. Rao K, Micic D, Chenoweth E, et al. Poor functional status as a risk factor for severe 12

Clostridium difficile infection in hospitalized older adults. J Am Geriatr Soc 2013;61:1738-13

1742. 14

25. Rodriguez-Pardo D, Almirante B, Bartolomé RM, et al. Epidemiology of Clostridium difficile 15

infection and risk factors for unfavorable clinical outcomes: results of a hospital-based study 16

in Barcelona, Spain. J Clin Microbiol 2013;51:1465-1473. 17

26. Hardt C, Berns T, Treder W, Dumoulin FL. Univariate and multivariate analysis of risk 18

factors for severe Clostridium difficile-associated diarrhea: importance of co-morbidity and 19

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27. Pasina L, Nobili A, Tettamanti M, et al. Prevalence and appropriateness of drug prescriptions 21

for peptic ulcer and gastro-esophageal reflux in a cohort of hospitalized elderly. Eur J Intern 22

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28. Goni E, Caleffi A, Nouvenne A, et al. Gastric function assessed by Gastropanel® in very old 1

patients (over 80 years old) and appropriateness of PPI administration. Helicobacter 2

2014;19(S1):166. 3

29. Eckmann C, Wasserman M, Latif F, Roberts G, Beriot-Mathiot A. Increased hospital length 4

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analysis of hospital episode statistics in four European countries. Eur J Health Econ 6

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TABLE 1 1

Main features of the population studied (n=505). 2

3

Age, years 80.7 ± 11.3

Men 47 (238/505)


days

20.8 ± 19.8

Mean length of stay in our unit, days 15.5 ± 11.9

Mean total length of stay, days 36.2 ± 24.3

Death 22 (108/505)

Infection (Clostridium difficile excluded) 62 (313/505)

Cardiovascular disease 55 (278/505)

Respiratory disease 44 (222/505)

Dementia 43 (216/505)

Stroke 30 (150/505)

Cancer 25 (126/505)

Chronic kidney disease 24 (122/505)

Liver disease 9 (48/505)

Clostridium difficile infection 8.5 (43/505)

PPI treatment 87 (434/505)

Antibacterial treatment 44 (223/505)

Antifungal treatment 13 (67/505)

4

Data are reported as percentage (number), or mean ± standard deviation whenever appropriate. 5

6

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TABLE 2 1

Comparison between general characteristics of Clostridium difficile infection (CDI)-positive and 2

CDI-negative patients. 3

4

CDI-

POSITIVE

(N=43)

CDI-

NEGATIVE

(N=462)

p*

Age, years 82.6 ± 8.5 80.4 ± 10.4 0.20

Men 22 (51.2) 216 (46.8) 0.41


days

23 ± 18 21 ± 20 0.43

Mean length of stay in our unit, days 30 ± 16 14 ± 11 0.02

Mean total length of stay, days 53 ± 28 35 ± 27 0.03

Number of comorbidities 3 [3-5] 3 [2-4] 0.01

PPI treatment 40 (93) 394 (85.8) 0.19

Antibacterial treatment 31 (72.1) 192 (41.6) <0.001

Antifungal treatment 10 (23.3) 57 (12.3) 0.03

Cardiovascular disease 23 (53.5) 255 (55.2) 0.55

Respiratory disease 20 (46.5) 202 (43.7) 0.91

Dementia 23 (53.5) 193 (41.7) 0.21

Stroke 17 (39.5) 133 (28.8) 0.18

Cancer 8 (18.6) 117 (25.4) 0.40

Chronic kidney disease 16 (37.2) 106 (22.9) 0.05

Liver disease 7 (16.3) 41 (8.9) 0.09

* Age- and sex-adjusted when appropriate. 5

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Data are reported as number (%), mean ± standard deviation or median [interquartile range (IQR)] 1

whenever appropriate. 2

3

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TABLE 3 1

Factors associated to a higher risk for Clostridium difficile infection at age- and sex-adjusted (model 2

1) and multivariate (model 2) logistic regression models. 3

ODDS

RATIO 95% CI p

Model 1 (age- and sex-adjusted)

CIRS Comorbidity Score, First quartile (<9) - - -

Second quartile (≥9, <13) 2.49 0.84-7.42 0.10

Third quartile (≥13, <17) 2.54 0.85-7.64 0.09

Fourth quartile (≥17) 2.89 1.02-8.54 0.045

Age 1.01 0.97-1.05 0.47

Sex (women vs men) 0.78 0.41-1.49 0.45

Model 2 (fully adjusted)

CIRS Comorbidity Score, First quartile (<9) - - .

Second quartile (≥9, <13) 3.50 0.48-25.38 0.22

Third quartile (≥13, <17) 2.55 0.85-7.68 0.09

Fourth quartile (≥17) 5.07 1.28-20.14 0.02



Age 1.03 0.98-1.07 0.21

Sex (women vs men) 0.67 0.34-1.33 0.26


Mean total length of stay 1.05 0.99-1.02 0.43

4

5

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FIGURE 1 1

Prevalence of Clostridium difficile infections (CDI) after quartile categorization of CIRS 2

(Cumulative Illness Rating Scale) Comorbidity Score in the studied population (n=505). 3

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Prevalence of Clostridium difficile infections (CDI) after quartile categorization of CIRS (Cumulative Illness Rating Scale) Comorbidity Score in the studied population (n=505).

58x42mm (300 x 300 DPI)

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SUPPLEMENTARY TABLE 1 for reviewer 1.

Number of comorbidities and risk for Clostridium difficile infection at multivariate logistic

regression models.

OR 95% CI p

Number of comorbidities 1.18 0.96-1.46- 0.12



Age 1.02 0.98-1.06 0.30

Sex (women vs men) 0.67 0.34-1.31 0.24


Mean total length of stay 1.00 0.99-1.02 0.44

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SUPPLEMENTARY FIGURE 1 for reviewer 1

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STROBE 2007 (v4) checklist of items to be included in reports of observational studies in epidemiology*

Checklist for cohort, case-control, and cross-sectional studies (combined)

Section/Topic Item # Recommendation Reported on page #

Title and abstract 1 (a) Indicate the study’s design with a commonly used term in the title or the abstract Page 1, line 2

(b) Provide in the abstract an informative and balanced summary of what was done and what was found Page 3

Introduction

Background/rationale 2 Explain the scientific background and rationale for the investigation being reported From page 5 line 3 to

page 6 line 2

Objectives 3 State specific objectives, including any pre-specified hypotheses Page 3, lines 3-6

Methods

Study design 4 Present key elements of study design early in the paper Page 7, line 5

Setting 5 Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data

collection Page 7, lines 3-7

Participants 6 (a) Cohort study—Give the eligibility criteria, and the sources and methods of selection of participants. Describe

methods of follow-up

Case-control study—Give the eligibility criteria, and the sources and methods of case ascertainment and control

selection. Give the rationale for the choice of cases and controls

Cross-sectional study—Give the eligibility criteria, and the sources and methods of selection of participants

Page 7, lines 8-16 and

page 8, lines 7-8

(b) Cohort study—For matched studies, give matching criteria and number of exposed and unexposed

Case-control study—For matched studies, give matching criteria and the number of controls per case Page 7, lines

15-18

Variables 7 Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic

criteria, if applicable Page 7, lines 12-16

and from page 7 line

22 to page 8 line 8

Data sources/ measurement 8* For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe

comparability of assessment methods if there is more than one group Page 8 lines 2-8

Bias 9 Describe any efforts to address potential sources of bias Page 8 lines 14-19

Study size 10 Explain how the study size was arrived at Page 7 lines 17-21

Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen

and why Page 8 lines 9-10 and

13-14

Statistical methods 12 (a) Describe all statistical methods, including those used to control for confounding Page 8 lines 9-20

(b) Describe any methods used to examine subgroups and interactions Page 8 lines 16-19

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(c) Explain how missing data were addressed Page 8 lines 7-8

(d) Cohort study—If applicable, explain how loss to follow-up was addressed

Case-control study—If applicable, explain how matching of cases and controls was addressed

Cross-sectional study—If applicable, describe analytical methods taking account of sampling strategy

Not applicable

(e) Describe any sensitivity analyses Page 7 lines 17-21

Results

Participants 13* (a) Report numbers of individuals at each stage of study—eg numbers potentially eligible, examined for eligibility,

confirmed eligible, included in the study, completing follow-up, and analysed Page 9 lines 3-7

(b) Give reasons for non-participation at each stage Page 9 lines 3-7

(c) Consider use of a flow diagram Not applicable

Descriptive data 14* (a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and

potential confounders Page 9, lines 7-11 and

Table 1

(b) Indicate number of participants with missing data for each variable of interest Page 9 lines 5-6

(c) Cohort study—Summarise follow-up time (eg, average and total amount) Not applicable

Outcome data 15* Cohort study—Report numbers of outcome events or summary measures over time From page 9 line 12

to page 10 line 2

Case-control study—Report numbers in each exposure category, or summary measures of exposure Not applicable

Cross-sectional study—Report numbers of outcome events or summary measures Not applicable

Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95%

confidence interval). Make clear which confounders were adjusted for and why they were included From page 9 line 21

to page 12 line 2;

Tables 2-3

(b) Report category boundaries when continuous variables were categorized Page 11 lines 21-23

(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period Not applicable

Other analyses 17 Report other analyses done—eg analyses of subgroups and interactions, and sensitivity analyses Page 10 lines 3-8

Discussion

Key results 18 Summarise key results with reference to study objectives Page 11 lines 3-6

Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction

and magnitude of any potential bias Page 11 lines 7-20

Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results

from similar studies, and other relevant evidence Page 11 line 21 –

page 13 line 2

Generalisability 21 Discuss the generalisability (external validity) of the study results Page 11 line 21 –

page 12 line 15

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Other information

Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on

which the present article is based Page 14 lines 12-13

*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies.

Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE

checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at

http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.

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Documents

BMJ Open · Meschi, Tiziana; University of Parma, Department of Clinical and Experimental Medicine Primary Subject Heading: Geriatric medicine Secondary Subject