Comparing the contribution of each clinical indicator in predictive models trained on 980 subacute stroke patients: a retrospective study

Abstract

Abstract Post-stroke disability affects patients’ lifestyles after discharge, and it is essential to predict functional recovery early in hospitalization to allow time for appropriate decisions. Previous studies reported important clinical indicators, but only a few clinical indicators were analyzed due to insufficient numbers of cases. Although review articles can exhaustively identify many prognostic factors, it remains impossible to compare the contribution of each predictor. This study aimed to determine which clinical indicators contribute more to predicting the functional independence measure (FIM) at discharge by comparing standardized coefficients. In this study, 980 participants were enrolled to build predictive models with 32 clinical indicators, including the stroke impairment assessment set (SIAS). Trunk function had the most significant standardized coefficient of 0.221. The predictive models also identified easy FIM sub-items, SIAS, and grip strength on the unaffected side as having positive standardized coefficients. As for the predictive accuracy of this model, R2 was 0.741. This is the first report that included FIM sub-items separately in post-stroke predictive models with other clinical indicators. Trunk function and easy FIM sub-items were included in the predictive model with larger positive standardized coefficients. This predictive model may predict prognosis with high accuracy, fewer clinical indicators, and less effort to predict.