多发性硬化症中的步态变异性：在低残疾患者中，它是比扩展残疾状态量表更好的跌倒预测指标。
Gait variability in multiple sclerosis: a better falls predictor than EDSS in patients with low disability.
作者信息
Allali Gilles, Laidet Magali, Herrmann Francois R, Armand Stéphane, Elsworth-Edelsten Charlotte, Assal Frédéric, Lalive Patrice H
机构信息
Division of Neurology, Department of Clinical Neurosciences, Geneva University Hospitals and Faculty of Medicine, University of Geneva, 4 Rue Gabrielle-Perret-Gentil, 1211, Geneva, Switzerland.
Department of Neurology, Albert Einstein College of Medicine, Yeshiva University, New York, USA.
出版信息
J Neural Transm (Vienna). 2016 Apr;123(4):447-50. doi: 10.1007/s00702-016-1511-z. Epub 2016 Feb 4.
This longitudinal study aims to compare the role of stride time variability (STV) and EDSS for predicting falls in 50 patients with multiple sclerosis with low disability. 21.7 % developed falls (follow-up: 22 months). STV (IRR: 1.73, 95 % CI: 1.23-2.41, p = 0.001) and EDSS (IRR: 2.29, 95 % CI: 1.35-3.90, p = 0.002) were associated with the number of falls. Adding STV to EDSS improves the predictive power of the model from 21 to 26 %, but not adding EDSS to STV.
这项纵向研究旨在比较步幅时间变异性（STV）和扩展残疾状态量表（EDSS）在预测50例轻度残疾多发性硬化症患者跌倒方面的作用。21.7%的患者出现跌倒（随访时间：22个月）。STV（发病率比：1.73，95%置信区间：1.23 - 2.41，p = 0.001）和EDSS（发病率比：2.29，95%置信区间：1.35 - 3.90，p = 0.002）与跌倒次数相关。将STV加入EDSS可使模型的预测能力从21%提高到26%，但将EDSS加入STV则不然。
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