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Impact of comorbidities on stroke rehabilitation outcomes: does the method matter?
Dan R Berlowitz1, Helen Hoenig, Diane C Cowper
1Center for Health Quality, Outcomes and Economic Research, Bedford VA Hospital, Bedford, MA 01730, USA. dberlow@bu.edu
Insights
Comorbidities significantly impact stroke rehabilitation outcomes, influencing mortality and rehospitalization rates. The classification method for comorbidities, such as diagnosis cost groups (DCGs), affects predictive model accuracy for patient care assessment.
Area of Science:
- Neurology
- Rehabilitation Medicine
- Health Services Research
Background:
- Comorbidities are common in stroke patients and can affect rehabilitation outcomes.
- Accurate prediction of stroke rehabilitation outcomes is crucial for effective patient management and quality assessment.
Purpose of the Study:
- To evaluate the predictive power of comorbidities on stroke rehabilitation outcomes.
- To compare the effectiveness of three comorbidity measures: Charlson Index, Adjusted Clinical Groups (ACGs), and Diagnosis Cost Groups (DCGs) in predicting outcomes.
Main Methods:
- An inception cohort of 2402 patients undergoing stroke rehabilitation at VA hospitals was followed for 6 months.
- Outcomes assessed included 6-month mortality, 6-month rehospitalization, and change in Functional Independence Measure (FIM) score.
- Logistic and linear regression models were used to assess the predictive performance of different comorbidity measures.
Main Results:
- During 6 months, 27.6% of patients were rehospitalized and 8.6% died. The mean FIM score improved by 20 points.
- Incorporating comorbidities improved the prediction of outcomes compared to age and sex alone.
- Diagnosis Cost Groups (DCGs) demonstrated the strongest predictive performance, with a c-statistic of 0.74 for mortality and 0.63 for rehospitalization, and an R² of 0.111 for FIM score change.
Conclusions:
- Comorbidities are significant predictors of stroke rehabilitation outcomes.
- The method used to classify comorbidities impacts the accuracy of predictive models.
- Findings have implications for quality of care assessments in stroke rehabilitation.
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 outcomes based on changes in c statistics for logistic and R(2) 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.