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神经源性步态障碍的跌倒预测:临床评估、步态分析和日常生活活动监测的不同贡献。

Fall prediction in neurological gait disorders: differential contributions from clinical assessment, gait analysis, and daily-life mobility monitoring.

机构信息

Department of Neurology, Ludwig-Maximilians University of Munich, Marchioninistrasse 15, 81377, Munich, Germany.

German Center for Vertigo and Balance Disorders, Ludwig-Maximilians University of Munich, Munich, Germany.

出版信息

J Neurol. 2021 Sep;268(9):3421-3434. doi: 10.1007/s00415-021-10504-x. Epub 2021 Mar 13.

Abstract

OBJECTIVE

To evaluate the predictive validity of multimodal clinical assessment outcomes and quantitative measures of in- and off-laboratory mobility for fall-risk estimation in patients with different forms of neurological gait disorders.

METHODS

The occurrence, severity, and consequences of falls were prospectively assessed for 6 months in 333 patients with early stage gait disorders due to vestibular, cerebellar, hypokinetic, vascular, functional, or other neurological diseases and 63 healthy controls. At inclusion, participants completed a comprehensive multimodal clinical and functional fall-risk assessment, an in-laboratory gait examination, and an inertial-sensor-based daily mobility monitoring for 14 days. Multivariate logistic regression analyses were performed to identify explanatory characteristics for predicting the (1) the fall status (non-faller vs. faller), (2) the fall frequency (occasional vs. frequent falls), and (3) the fall severity (benign vs. injurious fall) of patients.

RESULTS

40% of patients experienced one or frequent falls and 21% severe fall-related injuries during prospective fall assessment. Fall status and frequency could be reliably predicted (accuracy of 78 and 91%, respectively) primarily based on patients' retrospective fall status. Instrumented-based gait and mobility measures further improved prediction and provided independent, unique information for predicting the severity of fall-related consequences.

INTERPRETATION

Falls- and fall-related injuries are a relevant health problem already in early stage neurological gait disorders. Multivariate regression analysis encourages a stepwise approach for fall assessment in these patients: fall history taking readily informs the clinician about patients' general fall risk. In patients at risk of falling, instrument-based measures of gait and mobility provide critical information on the likelihood of severe fall-related injuries.

摘要

目的

评估多模态临床评估结果和实验室内外移动性的定量测量对不同类型神经步态障碍患者跌倒风险评估的预测效度。

方法

前瞻性评估 333 例因前庭、小脑、运动减少、血管、功能性或其他神经疾病导致早期步态障碍的患者和 63 例健康对照者 6 个月内的跌倒发生、严重程度和后果。纳入时,参与者完成了全面的多模态临床和功能性跌倒风险评估、实验室步态检查和基于惯性传感器的 14 天日常移动性监测。进行多变量逻辑回归分析,以确定预测(1)患者的(非跌倒者与跌倒者)跌倒状态、(2)跌倒频率(偶发性与频繁跌倒)和(3)跌倒严重程度(良性与损伤性跌倒)的解释特征。

结果

40%的患者在前瞻性跌倒评估中经历了一次或多次频繁跌倒,21%的患者发生了严重的跌倒相关损伤。跌倒状态和频率主要基于患者的回顾性跌倒状态可以可靠地预测(准确性分别为 78%和 91%)。基于仪器的步态和移动性测量进一步提高了预测能力,并为预测跌倒相关后果的严重程度提供了独立的、独特的信息。

解释

在早期神经步态障碍中,跌倒和跌倒相关损伤是一个相关的健康问题。多元回归分析鼓励对这些患者进行逐步的跌倒评估:跌倒病史可让临床医生了解患者的一般跌倒风险。对于有跌倒风险的患者,基于仪器的步态和移动性测量可提供有关严重跌倒相关损伤可能性的关键信息。

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