Associations between gait patterns, brain lesion factors and functional recovery in stroke patients

Abstract

Brain CT scans and neurological condition were evaluated in 74 stroke patients. Firstly, we found that using a classification-tree technique based on CT scan parameters (an innovative method, analyzing four parameters simultaneously) coincided with our previously proposed kinematic artificial neural network (ANN) classification technique for 71.3% of patients. Lesion size and location were found to be the most significant CT scan predictors of gait classification. Secondly, we sought to gauge post-rehabilitation functional recovery in patients within the same three groups of gait pattern. We found significant differences in scores between the three gait pattern groups, before and after rehabilitation (Kruskal-Wallis test, p<0.001), while significant improvement was observed in each group (Wilcoxon text; p<0.01). We conclude that patient classification into pathological gait groups on the basis of gait or CT scan parameters may serve as an early predictor of future functional outcome.