Abstract
Prediction of post-stroke functional outcomes is crucial for allocating medical resources. In this study, a total of 577 patients were enrolled in the Post-Acute Care-Cerebrovascular Disease (PAC-CVD) program, and 77 predictors were collected at admission. The outcome was whether a patient could achieve a Barthel Index (BI) score of >60 upon discharge. Eight machine-learning (ML) methods were applied, and their results were integrated by stacking method. The area under the curve (AUC) of the eight ML models ranged from 0.83 to 0.887, with random forest, stacking, logistic regression, and support vector machine demonstrating superior performance. The feature importance analysis indicated that the initial Berg Balance Test (BBS-I), initial BI (BI-I), and initial Concise Chinese Aphasia Test (CCAT-I) were the top three predictors of BI scores at discharge. The partial dependence plot (PDP) and individual conditional expectation (ICE) plot indicated that the predictors’ ability to predict outcomes was the most pronounced within a specific value range (e.g., BBS-I < 40 and BI-I < 60). BI at discharge could be predicted by information collected at admission with the aid of various ML models, and the PDP and ICE plots indicated that the predictors could predict outcomes at a certain value range.
Highlights
Stroke is a major cause of disability and imposes substantial social and economic burdens [1,2]
A total of 577 patients were included in this analysis after 56 patients who did not undergo the assessment at the termination of the Post-Acute Care-Cerebrovascular Disease (PAC-CVD) program were excluded
The mean rehabilitation timing, i.e., the interval from the stroke onset to the commencement of rehabilitation, was 13.2 ± 5.3 days, and the LOS in the post-acute care (PAC)-CVD ward was 52.3 ± 23.7 days
Summary
Stroke is a major cause of disability and imposes substantial social and economic burdens [1,2]. Post-stroke rehabilitation is pivotal for managing disability and improving quality of life [3]. Because of the high diversity of stroke-induced disabilities, predicting their functional outcomes is difficult. Numerous factors may affect post-stroke functional outcomes, including age [4], cognition [5,6], comorbidities [7], post-stroke intervention [8,9], and stroke characteristics, such as severity [10], type [11], location [12,13], and volume [14]. 795,000 patients globally were newly diagnosed as having stroke in Medical resources must be allocated to patients with a more favorable rehabilitation potential to help them achieve their rehabilitation goals.
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