Predicting Improvement in Gait After Stroke

Abstract

Background and Purpose— To study the longitudinal relationship of functional change in walking ability and change in time-dependent covariates and to develop a multivariate regression model to predict longitudinal change of walking ability. Methods— A total of 101 acute stroke patients with first-ever ischemic middle cerebral artery strokes was used as the population. Prospective cohort study based on 18 repeated measurements over time during the first poststroke year. Baseline characteristics as well as longitudinal information from functional ambulation categories (FAC), Fugl–Meyer leg score (FM-leg), Motricity index leg score (MI-leg), letter cancellation task (LCT), Fugl-Meyer balance (FM-balance), and timed balance test (TBT) were obtained. Intervention consisted of a basic rehabilitation program with additional arm, leg, or air splint therapy. Main outcome measure constituted change scores on FAC over time. Results— In total, 1532 of the 1717 change scores were available for regression analysis. The regression model showed that TBT change scores were the most important factor in predicting improvement on FAC (β=0.094; P <0.001) followed by changes scores on FM-leg (β=0.014; P <0.001) and reduction in LCT omissions (β=−0.010; P <0.001) and MI leg test (β=0.001; P <0.001). In addition, time itself was significantly negatively associated with improvement (β=−0.002; P <0.001). Conclusion— Improvement in standing balance control is more important than improvement in leg strength or synergism to achieve improvement in walking ability, whereas reduction in visuospatial inattention is independently related to improvement of gait. Finally, time itself is an independent covariate that is negatively associated with change on FAC, suggesting that most pronounced improvements occur earlier after stroke.