Kraus Moritz, Saller Maximilian Michael, Baumbach Sebastian Felix, Neuerburg Carl, Stumpf Ulla Cordula, Böcker Wolfgang, Keppler Alexander Martin
Department of Orthopaedics and Trauma Surgery, Musculoskeletal University Center Munich, Ludwig-Maximilians Universität Munich, Munich, Germany.
JMIR Med Inform. 2022 Jan 5;10(1):e32724. doi: 10.2196/32724.
Assessment of the physical frailty of older patients is of great importance in many medical disciplines to be able to implement individualized therapies. For physical tests, time is usually used as the only objective measure. To record other objective factors, modern wearables offer great potential for generating valid data and integrating the data into medical decision-making.
The aim of this study was to compare the predictive value of insole data, which were collected during the Timed-Up-and-Go (TUG) test, to the benchmark standard questionnaire for sarcopenia (SARC-F: strength, assistance with walking, rising from a chair, climbing stairs, and falls) and physical assessment (TUG test) for evaluating physical frailty, defined by the Short Physical Performance Battery (SPPB), using machine learning algorithms.
This cross-sectional study included patients aged >60 years with independent ambulation and no mental or neurological impairment. A comprehensive set of parameters associated with physical frailty were assessed, including body composition, questionnaires (European Quality of Life 5-dimension [EQ 5D 5L], SARC-F), and physical performance tests (SPPB, TUG), along with digital sensor insole gait parameters collected during the TUG test. Physical frailty was defined as an SPPB score≤8. Advanced statistics, including random forest (RF) feature selection and machine learning algorithms (K-nearest neighbor [KNN] and RF) were used to compare the diagnostic value of these parameters to identify patients with physical frailty.
Classified by the SPPB, 23 of the 57 eligible patients were defined as having physical frailty. Several gait parameters were significantly different between the two groups (with and without physical frailty). The area under the receiver operating characteristic curve (AUROC) of the TUG test was superior to that of the SARC-F (0.862 vs 0.639). The recursive feature elimination algorithm identified 9 parameters, 8 of which were digital insole gait parameters. Both the KNN and RF algorithms trained with these parameters resulted in excellent results (AUROC of 0.801 and 0.919, respectively).
A gait analysis based on machine learning algorithms using sensor soles is superior to the SARC-F and the TUG test to identify physical frailty in orthogeriatric patients.
在许多医学学科中，评估老年患者的身体虚弱状况对于实施个体化治疗至关重要。对于身体测试，时间通常是唯一的客观衡量标准。为了记录其他客观因素，现代可穿戴设备在生成有效数据并将数据整合到医疗决策中具有巨大潜力。
本研究旨在使用机器学习算法，比较在定时起立行走（TUG）测试期间收集的鞋垫数据与评估身体虚弱的肌肉减少症基准标准问卷（SARC-F：力量、行走辅助、从椅子上起身、爬楼梯和跌倒）及身体评估（TUG测试）的预测价值，身体虚弱由简短身体机能测试电池（SPPB）定义。
这项横断面研究纳入了年龄大于60岁、能够独立行走且无精神或神经功能障碍的患者。评估了与身体虚弱相关的一系列综合参数，包括身体成分、问卷（欧洲五维健康量表[EQ 5D 5L]、SARC-F）和身体机能测试（SPPB、TUG），以及在TUG测试期间收集的数字传感器鞋垫步态参数。身体虚弱定义为SPPB评分≤8。使用包括随机森林（RF）特征选择和机器学习算法（K近邻算法[KNN]和RF）在内的高级统计方法，比较这些参数的诊断价值，以识别身体虚弱的患者。
根据SPPB分类，57名符合条件的患者中有23名被定义为身体虚弱。两组（有和没有身体虚弱）之间的几个步态参数有显著差异。TUG测试的受试者工作特征曲线下面积（AUROC）优于SARC-F（0.862对0.639）。递归特征消除算法确定了9个参数，其中8个是数字鞋垫步态参数。使用这些参数训练的KNN和RF算法均产生了优异的结果（AUROC分别为0.801和0.919）。
基于使用传感器鞋垫的机器学习算法的步态分析在识别老年骨科患者身体虚弱方面优于SARC-F和TUG测试。
