Developing empirical models to enhance stroke rehabilitation.

Purpose: Models identifying functional indicators most strongly associated with favourable and unfavourable outcomes may bolster evidence to improve stroke rehabilitation assessment and intervention. This study examined the feasibility of decision analysis methods for developing data-driven models that examined associations between specific functional indicators and global disability.

Method: Data were derived from functional assessment of 67 participants 3 months following stroke. Decision analysis methods were used to examine specific activity and body function indicators associated with global disability, and the degree of limitation or impairment that contributed to favourable and unfavourable outcomes, in 2 models. The feasibility of decision analysis methods was evaluated.

Results: Of the 26 activity indicators, dressing was most strongly associated with global disability, followed by bill mailing, shopping and sweeping. Of 15 body function indicators, facial weakness and mental functions were most strongly associated with global disability. The misclassification risk estimates were fair for the two models.

Conclusions: Findings suggest that decision analysis methods show promise for developing models examining associations between specific functional indicators and disability. Further study with these methods may identify specific priorities for functional assessment and intervention in stroke rehabilitation.