AbstractBackground and Purpose—
Prediction of outcome after stroke rehabilitation may help clinicians in decision-making and planning rehabilitation care. We developed and validated a predictive tool to estimate the probability of achieving improvement in physical functioning (model 1) and a level of independence requiring no more than supervision (model 2) after stroke rehabilitation.Methods—
The models were derived from 717 patients admitted for stroke rehabilitation. We used multivariable logistic regression analysis to build each model. Then, each model was prospectively validated in 875 patients.Results—
Model 1 included age, time from stroke occurrence to rehabilitation admission, admission motor and cognitive Functional Independence Measure scores, and neglect. Model 2 included age, male gender, time since stroke onset, and admission motor and cognitive Functional Independence Measure score. Both models demonstrated excellent discrimination. In the derivation cohort, the area under the curve was 0.883 (95% confidence intervals, 0.858–0.910) for model 1 and 0.913 (95% confidence intervals, 0.884–0.942) for model 2. The Hosmer–Lemeshow χ2 was 4.12 (P=0.249) and 1.20 (P=0.754), respectively. In the validation cohort, the area under the curve was 0.866 (95% confidence intervals, 0.840–0.892) for model 1 and 0.850 (95% confidence intervals, 0.815–0.885) for model 2. The Hosmer–Lemeshow χ2 was 8.86 (P=0.115) and 34.50 (P=0.001), respectively. Both improvement in physical functioning (hazard ratios, 0.43; 0.25–0.71; P=0.001) and a level of independence requiring no more than supervision (hazard ratios, 0.32; 0.14–0.68; P=0.004) were independently associated with improved 4-year survival. A calculator is freely available for download at https://goo.gl/fEAp81.Conclusions—
This study provides researchers and clinicians with an easy-to-use, accurate, and validated predictive tool for potential application in rehabilitation research and stroke management.