2026 年 29 巻 2 号 p. 133-142
Objectives: This study aimed to develop 2 multiple linear regression models to predict the Functional Independence Measure (FIM) motor score at discharge in patients with stroke and low FIM at admission (total FIM score ≤55) admitted to a convalescent rehabilitation ward, and to compare their predictive accuracy and internal validity. Model 1 used admission assessments only, whereas Model 2 combined admission assessments with reassessment information at 3 weeks after admission. Methods: Participants were 104 patients with stroke admitted with a total FIM score of ≤55. The outcome was the discharge FIM motor score (continuous variable). Candidate predictors included basic information and measures of physical and cognitive function. Models were developed using multiple linear regression, and internal validity was evaluated through bootstrap resampling. In Model 1, predictors were age, days since onset, the Berg Balance Scale, and admission FIM motor and cognitive scores. In Model 2, predictors were age, days since onset, admission FIM cognitive score, and the FIM motor score at 3 weeks after admission. Results: Model 2 showed a higher adjusted R2 (0.71) and smaller prediction errors than Model 1 across the evaluated error metrics, indicating superior predictive accuracy. The calibration of Model 2 was also improved compared with Model 1 based on internal validation. Conclusions: Incorporating reassessment information at 3 weeks improved time-updated prediction of discharge activities of daily living outcomes in this single-center cohort. However, given limited generalizability, external validation using multicenter data (and, if necessary, model updating) is required before broader clinical application.
