Development and validation of clinical prediction model for functional independence measure following stroke rehabilitation

Abstract

Objectives: To develop and internally validate a clinical prediction model that includes balance ability and nutritional indices for the motor-functional independence measure (M-FIM) at 90 days post-stroke stroke.

Materials and methods: This retrospective, single-center study included 566 patients with stroke undergoing rehabilitation at our rehabilitation hospital. The primary outcome was the M-FIM score of >61 at 3 months post-strokes onset. Stepwise conditional forward selection was first used to identify predictors for the achievement of M-FIM>61 at 90 days post-stroke, from 25 potential predictors at admission. The selected predictors were dichotomized with cut-off values to establish scoring systems, resulting in the B-ADL model, which includes postural balance (B), albumin level, age, arm function (A), days since stroke onset (D), and level of activities of daily living (ADL) (L). For internal validation, we corrected the optimism of the area under the curve of receiver operating characteristic curve (AUROC) induced by overfitting the original data using the bootstrap validation method. Calibration capacity was assessed using a calibration plot.

Results: We developed a clinical model to predict the M-FIM at 90 days post-stroke onset. The AUROC of the B-ADL model was 0.92 (sensitivity, 93.7%; specificity, 89.7%). The B-ADL model showed high accuracy with an AUROC of 0.970 in the internal validation. The scoring system in the validation cohort had a cut-off value of 5.5/12 points to predict the achievement of M-FIM>61 (AUROC: 0.950; 95% CI 0.930-0.970).

Conclusions: The B-ADL model accurately predicted M-FIM >61 at 90 days post-stroke on the day of admission to the recovery rehabilitation ward. The B-ADL model is useful for optimizing rehabilitation programs and resource allocation, allowing for targeted interventions after stroke.

Keywords: Activities of daily living; Functional independence measure; Prediction; Rehabilitation; Stroke.