Calcagni Michael, Kosa Peter, Bielekova Bibi
Neuroimmunological Diseases Section, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
medRxiv. 2024 Nov 21:2024.11.20.24317196. doi: 10.1101/2024.11.20.24317196.
The COVID-19 pandemic and increased demands for neurologists have inspired the creation of remote, digitalized tests of neurological functions. This study investigates two tests from the Neurological Functional Tests Suite (NeuFun-TS) smartphone application, the "Postural Sway" and "Pronator Drift" tests. These tests capture different domains of postural control and motoric dysfunction in healthy volunteers (n=13) and people with neurological disorders (n=68 relapsing-remitting multiple sclerosis [MS]; n=21 secondary progressive MS; n=23 primary progressive MS; n=13 other inflammatory neurological diseases; n=21 non-inflammatory neurological diseases; n=4 clinically isolated syndrome; n=1 radiologically isolated syndrome). Smartphone accelerometer data was transformed into digital biomarkers, which were filtered in the training cohort (80% of subjects) for test-retest reproducibility and correlations with subdomains of neurological examinations and validated imaging biomarkers. The independent validation cohort (20%) determined whether biomarker models outperformed the best single digital biomarkers. Postural sway acceleration magnitude in the eyes closed and feet together stance demonstrated the highest reliability (ICC=.706), strongest correlations with age (Pearson r<=.82) and clinical and imaging outcomes (r<=.65, p<0.001) and stronger predictive value for sway-relevant neurological disability outcomes than models that aggregated multiple biomarkers (coefficient of determination R=.46 vs .38). The pronator drift test only captured cerebellar dysfunction, had less reproducible biomarkers, but provided additive value when combined with postural sway biomarkers into models predicting global scales of neurological disability. In conclusion, a simple 1-minute postural sway test accurately measures body oscillations that increase with natural aging and differentiates them from abnormally increased body oscillations in people with neurological disabilities.
新冠疫情以及对神经科医生需求的增加，促使人们开发了远程、数字化的神经功能测试。本研究调查了神经功能测试套件（NeuFun-TS）智能手机应用程序中的两项测试，即“姿势摆动”和“旋前圆肌漂移”测试。这些测试在健康志愿者（n = 13）以及患有神经疾病的人群（n = 68复发缓解型多发性硬化症[MS]；n = 21继发进展型MS；n = 23原发进展型MS；n = 13其他炎性神经疾病；n = 21非炎性神经疾病；n = 4临床孤立综合征；n = 1放射学孤立综合征）中捕捉姿势控制和运动功能障碍的不同方面。智能手机加速度计数据被转化为数字生物标志物，这些生物标志物在训练队列（约80%的受试者）中进行筛选，以评估重测信度以及与神经检查子领域和经过验证的影像学生物标志物的相关性。独立验证队列（约20%）确定生物标志物模型是否优于最佳单一数字生物标志物。闭眼双脚并拢站立时的姿势摆动加速度幅度显示出最高的可靠性（组内相关系数ICC = 0.706），与年龄（皮尔逊r <= 0.82）、临床和影像结果（r <= 0.65，p < 0.001）的相关性最强，并且与摆动相关的神经功能残疾结果的预测价值比整合多个生物标志物的模型更强（决定系数R = 0.46对0.38）。旋前圆肌漂移测试仅能捕捉小脑功能障碍，生物标志物的可重复性较低，但与姿势摆动生物标志物结合用于预测神经功能残疾整体量表的模型时，能提供附加价值。总之，一项简单的1分钟姿势摆动测试能够准确测量随自然衰老而增加的身体摆动，并将其与神经功能残疾患者异常增加的身体摆动区分开来。
