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卒中后运动恢复预测的最新进展。

An update on predicting motor recovery after stroke.

作者信息

Stinear C M, Byblow W D, Ward S H

机构信息

Clinical Neuroscience Laboratory, Department of Medicine, University of Auckland, Private Bag, 92019 Auckland, New Zealand; Centre for Brain Research, University of Auckland, Private Bag, 92019 Auckland, New Zealand.

Centre for Brain Research, University of Auckland, Private Bag, 92019 Auckland, New Zealand; Movement Neuroscience Laboratory, Department of Sport and Exercise Science, University of Auckland, Private Bag, 92019 Auckland, New Zealand.

出版信息

Ann Phys Rehabil Med. 2014 Nov;57(8):489-498. doi: 10.1016/j.rehab.2014.08.006. Epub 2014 Aug 27.

Abstract

Being able to predict an individual's potential for recovery of motor function after stroke may facilitate the use of more effective targeted rehabilitation strategies, and management of patient expectations and goals. This review summarises developments since 2010 of approaches based on clinical, neurophysiological and neuroimaging measures for predicting individual patients' potential for upper limb recovery. Clinical assessments alone have low prognostic accuracy. Transcranial magnetic stimulation can be used to assess the functional integrity of the corticomotor pathway, and has some predictive value but is not superior when used in isolation due to its low negative predictive value. Neuroimaging measures can be used to assess the structural integrity of descending white matter tracts. Recent studies indicate that the integrity of corticospinal and alternate motor tracts in both hemispheres may be useful predictors of motor recovery after stroke. The PREP algorithm is currently the only sequential algorithm that combines clinical, neurophysiological and neuroimaging measures at the sub-acute stage to predict the potential for subsequent recovery of upper limb function. Future research could determine if a similar algorithmic approach may be useful for predicting the recovery of gait after stroke.

摘要

能够预测个体中风后运动功能恢复的潜力,可能有助于采用更有效的针对性康复策略,以及管理患者的期望和目标。本综述总结了自2010年以来基于临床、神经生理学和神经影像学测量方法预测个体患者上肢恢复潜力的进展。仅靠临床评估的预后准确性较低。经颅磁刺激可用于评估皮质运动通路的功能完整性,具有一定的预测价值,但由于其阴性预测值较低,单独使用时并不优越。神经影像学测量可用于评估下行白质束的结构完整性。最近的研究表明,双侧半球皮质脊髓束和其他运动束的完整性可能是中风后运动恢复的有用预测指标。PREP算法是目前唯一一种在亚急性期结合临床、神经生理学和神经影像学测量来预测上肢功能后续恢复潜力的序贯算法。未来的研究可以确定类似的算法方法是否有助于预测中风后步态的恢复。

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