Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne NE1 8ST, UK.
Department of Physical Therapy, State University of Londrina, Londrina 86057-970, Brazil.
Sensors (Basel). 2023 May 11;23(10):4651. doi: 10.3390/s23104651.
Although the multifactorial nature of falls in Parkinson's disease (PD) is well described, optimal assessment for the identification of fallers remains unclear. Thus, we aimed to identify clinical and objective gait measures that best discriminate fallers from non-fallers in PD, with suggestions of optimal cutoff scores.
Individuals with mild-to-moderate PD were classified as fallers (n = 31) or non-fallers (n = 96) based on the previous 12 months' falls. Clinical measures (demographic, motor, cognitive and patient-reported outcomes) were assessed with standard scales/tests, and gait parameters were derived from wearable inertial sensors (Mobility Lab v2); participants walked overground, at a self-selected speed, for 2 min under single and dual-task walking conditions (maximum forward digit span). Receiver operating characteristic curve analysis identified measures (separately and in combination) that best discriminate fallers from non-fallers; we calculated the area under the curve (AUC) and identified optimal cutoff scores (i.e., point closest-to-(0,1) corner).
Single gait and clinical measures that best classified fallers were foot strike angle (AUC = 0.728; cutoff = 14.07°) and the Falls Efficacy Scale International (FES-I; AUC = 0.716, cutoff = 25.5), respectively. Combinations of clinical + gait measures had higher AUCs than combinations of clinical-only or gait-only measures. The best performing combination included the FES-I score, New Freezing of Gait Questionnaire score, foot strike angle and trunk transverse range of motion (AUC = 0.85).
Multiple clinical and gait aspects must be considered for the classification of fallers and non-fallers in PD.
目的：尽管帕金森病（PD）患者跌倒的多因素性质已得到充分描述，但用于识别跌倒患者的最佳评估方法仍不清楚。因此，我们旨在确定最佳区分 PD 跌倒者和非跌倒者的临床和客观步态测量指标，并提出最佳截断评分建议。
方法：根据过去 12 个月的跌倒情况，将轻度至中度 PD 患者分为跌倒者（n = 31）和非跌倒者（n = 96）。使用标准量表/测试评估临床指标（人口统计学、运动、认知和患者报告的结果），并从可穿戴惯性传感器（Mobility Lab v2）中得出步态参数；参与者在单任务和双任务步行条件下（最大正向数字跨度）以自我选择的速度在地面上行走 2 分钟。接受者操作特征曲线分析确定了（单独和组合）最佳区分跌倒者和非跌倒者的指标；我们计算了曲线下面积（AUC）并确定了最佳截断评分（即最接近（0,1）角的点）。
结果：最佳区分跌倒者和非跌倒者的单一步态和临床指标分别为足触地角度（AUC = 0.728；截断值 = 14.07°）和国际跌倒效能量表（FES-I；AUC = 0.716，截断值 = 25.5）。临床+步态指标的组合比临床或步态指标的组合具有更高的 AUC。表现最佳的组合包括 FES-I 评分、新冻结步态问卷评分、足触地角度和躯干横向运动范围（AUC = 0.85）。
结论：必须考虑多个临床和步态方面来对 PD 患者中的跌倒者和非跌倒者进行分类。
