Impact of Comorbidities on Stroke Rehabilitation Outcomes: Does the Method Matter?

Objectives: To examine the impact of comorbidities in predicting stroke rehabilitation outcomes and to examine differences among 3 commonly used comorbidity measures-the Charlson Index, adjusted clinical groups (ACGs), and diagnosis cost groups (DCGs)-in how well they predict these outcomes. Design: Inception cohort of patients followed for 6 months. Setting: Department of Veterans Affairs (VA) hospitals. Participants: A total of 2402 patients beginning stroke rehabilitation at a VA facility in 2001 and included in the Integrated Stroke Outcomes Database. Interventions: Not applicable. Main Outcome Measures: Three outcomes were evaluated: 6-month mortality, 6-month rehospitalization, and change in FIM score. Results: During 6 months of follow-up, 27.6% of patients were rehospitalized and 8.6% died. The mean FIM score increased an average of 20 points during rehabilitation. Addition of comorbidities to the age and sex models improved their performance in predicting these 2 outcomes based on changes in c statistics for logistic and R values for linear regression models. While ACG and DCG models performed similarly, the best models, based on DCGs, had a c statistic of .74 for 6-month mortality and .63 for 6-month rehospitalization, and an R-2 of .111 for change in FIM score. Conclusions: Comorbidities are important predictors of stroke rehabilitation outcomes. How they are classified has important implications for models that may be used in assessing quality of care.