Gait variability as digital biomarker of disease severity in Huntington's disease
Gait variability as digital biomarker of disease severity in Huntington's disease
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DOI：
10.1007/s00415-020-09725-3
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发表时间：
2020-02-11
影响因子：
6
通讯作者：
Kohl, Zacharias
中科院分区：
文献类型：
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作者：
Gassner, Heiko;Jensen, Dennis;Kohl, Zacharias
背景步态障碍对亨廷顿病（HD）患者的生活质量起着重要作用。测量HD患者的客观步态参数可能会提供对运动缺陷的无偏倚评估，以确定未来治疗的潜在有益效果。目的应用基于传感器的步态分析技术，客观识别血液透析患者的步态特征。特别是，步态参数与统一亨廷顿氏病评定量表、总运动评分（TMS）和总功能能力（TFC）相关。方法在两个德国研究中心的显性HD患者（n = 43）被纳入，并在其年度ENROLL-HD访视期间进行临床评估。此外，HD患者和年龄和性别匹配的对照队列进行了定义的步态测试（4 x 10 m步行）。步态模式由安装在两只鞋上的惯性传感器记录下来。应用机器学习算法计算时空步态参数和步态变异性，表示为方差系数（CV）。结果HD患者的步幅（-15%）和步态速度（-19%）减少，而步幅（+ 7%）和站立时间（+ 2%）增加。然而，反映步态变异性的参数在HD患者中显著改变（+17%步长CV至+41%步长CV，具有最大效应量），并且显示与TMS和TFC的强相关性（0.416
Background Impaired gait plays an important role for quality of life in patients with Huntington's disease (HD). Measuring objective gait parameters in HD might provide an unbiased assessment of motor deficits in order to determine potential beneficial effects of future treatments. Objective To objectively identify characteristic features of gait in HD patients using sensor-based gait analysis. Particularly, gait parameters were correlated to the Unified Huntington's Disease Rating Scale, total motor score (TMS), and total functional capacity (TFC). Methods Patients with manifest HD at two German sites (n = 43) were included and clinically assessed during their annual ENROLL-HD visit. In addition, patients with HD and a cohort of age- and gender-matched controls performed a defined gait test (4 x 10 m walk). Gait patterns were recorded by inertial sensors attached to both shoes. Machine learning algorithms were applied to calculate spatio-temporal gait parameters and gait variability expressed as coefficient of variance (CV). Results Stride length (- 15%) and gait velocity (- 19%) were reduced, while stride (+ 7%) and stance time (+ 2%) were increased in patients with HD. However, parameters reflecting gait variability were substantially altered in HD patients (+ 17% stride length CV up to + 41% stride time CV with largest effect size) and showed strong correlations to TMS and TFC (0.416