Service de Médecine Physique et Réadaptation, Cliniques universitaires Saint-Luc, Avenue Hippocrate 10, 1200, Brussels, Belgium.
Secteur des Sciences de la Santé, Institut de Recherche Expérimentale et Clinique (IREC), Neuro Musculo Skeletal Lab (NMSK), Université catholique de Louvain, Avenue Mounier 53, 1200, Brussels, Belgium.
Acta Neurol Belg. 2020 Aug;120(4):783-790. doi: 10.1007/s13760-020-01320-7. Epub 2020 Mar 12.
The recovery of walking capacity is one of the main aims in stroke rehabilitation. Being able to predict if and when a patient is going to walk after stroke is of major interest in terms of management of the patients and their family's expectations and in terms of discharge destination and timing previsions. This article reviews the recent literature regarding the predictive factors for gait recovery and the best recommendations in terms of gait rehabilitation in stroke patients. Trunk control and lower limb motor control (e.g. hip extensor muscle force) seem to be the best predictors of gait recovery as shown by the TWIST algorithm, which is a simple tool that can be applied in clinical practice at 1 week post-stroke. In terms of walking performance, the 6-min walking test is the best predictor of community ambulation. Various techniques are available for gait rehabilitation, including treadmill training with or without body weight support, robotic-assisted therapy, virtual reality, circuit class training and self-rehabilitation programmes. These techniques should be applied at specific timing during post-stroke rehabilitation, according to patient's functional status.
恢复步行能力是中风康复的主要目标之一。能够预测患者在中风后何时能够行走,对于患者及其家属的期望管理、出院目的地和时间预测都具有重要意义。本文回顾了关于步态恢复预测因素的最新文献,并就中风患者的步态康复提出了最佳建议。躯干控制和下肢运动控制(如髋伸肌力量)似乎是步态恢复的最佳预测因素,TWIST 算法就是一个简单的工具,可以在中风后 1 周的临床实践中应用。在步行表现方面,6 分钟步行测试是社区步行能力的最佳预测指标。有多种技术可用于步态康复,包括带或不带体重支持的跑步机训练、机器人辅助治疗、虚拟现实、回路训练和自我康复计划。这些技术应根据患者的功能状态,在中风后康复的特定时间应用。