Background and purpose: Only a few studies have been conducted to predict motor recovery of the arm after stroke. The aims of this study were to identify which clinical variables, assessed at different points in time, were predictive of motor recovery, and to construct useful regression equations.
Method: One hundred consecutive stroke patients who had an obvious motor deficit of the upper limb were evaluated on entry to the study (two to five weeks post-stroke) and at two, six and 12 months after stroke. The Brunnström-Fugl-Meyer test was used as the outcome measure. Predictors included demographic data, overall disability, clinical neurological features, neuropsychological factors and secondary shoulder complications.
Results: In multiple regression analyses, motor performance was invariably retained as the predictive factor with the highest R-square. Other significant predictive variables were overall disability, muscle tone, proprioception and hemi-inattention. Between 53% and 89% of the total amount of variance was accounted for in all selected models. The accuracy of prediction from clinical measurement in the acute phase diminished as the time span of measurement of outcome increased. Similarly, assessment of the variables at two and six months, rather than in the acute stage, resulted in a considerable improvement in the percentage variance explained at 12 months. The highest accuracy was obtained when predictions were made step-by-step in time.
Conclusions: It is possible to predict motor recovery of the upper limb accurately through the use of a few clinical measures. Predictive equations are proposed, the use of which are practicable in both clinical practice and research.