Validation of the Charlson Comorbidity Index for Predicting Functional Outcome of Stroke

doi:10.1016/j.apmr.2007.11.049

Archives of Physical Medicine and Rehabilitation

Volume 89, Issue 7, July 2008, Pages 1276-1283

https://doi.org/10.1016/j.apmr.2007.11.049 Get rights and content

Abstract

Tessier A, Finch L, Daskalopoulou SS, Mayo NE. Validation of the Charlson Comorbidity Index for predicting functional outcome of stroke.

Objective

To determine whether a separate comorbidity index is needed to predict functional outcome after stroke, we compared the predictability of the Charlson Comorbidity Index (CMI) and the Functional Comorbidity Index (FCI) to that of a stroke-specific comorbidity index with function quantified with a measure developed with a Rasch model as outcome.

Design

Two prospective inception cohort studies, in 1996 through 1998 and in 2002 through 2005, with up to 9 months of follow-up.

Setting

Participants enrolled in 2 studies were recruited from acute care hospitals in the Montreal area.

Participants

For study one, 1027 persons with a first stroke discharged into the community were eligible; the 437 who were interviewed a second time at 6 months were included in the analysis. In study two, 235 of 262 patients with stroke were enrolled.

Interventions

Not applicable.

Main Outcome Measures

To predict recovery, we developed 3 stroke-specific comorbidity algorithms based on the estimated strength of association between comorbidities and stroke function. The various indices were compared on the basis of their predictive ability with a c statistic.

Results

In study 1, the c statistics were .758, .763, .766, and .763 for the stroke-specific algorithms 1, 2, and 3 and the CMI, respectively. In study 2, the c statistics were .680, .700, .704, .714, and .714 for the algorithms 1, 2, and 3, the CMI, and the FCI, respectively.

Conclusions

For purposes of case-mix adjustment, the CMI seems to be more than adequate.

Section snippets

Methods

First, we developed predictive models linking comorbidity to function; comorbidities were defined through a list of stroke-specific conditions and were weighted according to their impact on function. The predictive ability of the stroke-specific functional outcome comorbidity algorithms was then compared with the predictive ability of the CMI.⁹ The second step consisted of confirming the comparative predictability of stroke-specific algorithms to the CMI⁹ and FCI¹³ in a separate sample.

Results

Selected characteristics of both samples are presented in table 1. The mean age of the participants was 70 and 72 years for studies 1 and 2, respectively. They were predominantly men, with ischemic stroke. About 80% had a moderate or severe stroke, and the main discharge destination was home at the time of follow-up after 14.7 and 16.9 days in the hospital for studies 1 and 2, respectively. Hypertension and diabetes were among the most common conditions. People in study 1 had on average 4

Discussion

Comorbidities can compromise recovery from a stroke. The CMI⁹ is widely used in studies predicting function,25, 26, 27 although its use can be questioned because it was developed to predict mortality and morbidity. To account better for the effect of comorbidities when assessing rehabilitation programs, several comorbidity indices have been recently developed to predict functional outcomes.13, 14, 15, 16, 20 To verify the need for a stroke-specific comorbidity index, we developed 3

Conclusions

The CMI, although originally developed for predicting mortality and subsequently validated for some indicators of morbidity, predicted function as well as, if not better than, stroke-specific comorbidity indices. For purposes of case-mix adjustment, the CMI seems to be more than adequate for explaining variability in functional outcome poststroke; it is widely known and easily obtained from either clinical or administrative data. Use of standard assessments for comorbidity will facilitate

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Original articleValidation of the Charlson Comorbidity Index for Predicting Functional Outcome of Stroke

Abstract

Objective

Design

Setting

Participants

Interventions

Main Outcome Measures

Results

Conclusions

Section snippets

Methods

Results

Discussion

Conclusions

Arch Phys Med Rehabil

J Clin Epidemiol

Arch Phys Med Rehabil

Ann Epidemiol

J Chronic Dis

J Clin Epidemiol

J Clin Epidemiol

J Clin Epidemiol

Arch Phys Med Rehabil

J Clin Epidemiol

J Clin Epidemiol

Arch Phys Med Rehabil

Heart disease and stroke statistics—2007 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee

Circulation

Performance of comorbidity scores to control for confounding in epidemiologic studies using claims data

Am J Epidemiol

Does the addition of functional status indicators to case-mix adjustment indices improve prediction of hospitalization, institutionalization, and death in the elderly?

Med Care

Predictive variables on disability and quality of life in stroke outpatients undergoing rehabilitation

Neurol Sci

Comorbidity in post-stroke rehabilitation

Eur J Neurol

Original article
Validation of the Charlson Comorbidity Index for Predicting Functional Outcome of Stroke