Forth Katharine E, Wirfel Kelly L, Adams Sasha D, Rianon Nahid J, Lieberman Aiden Erez, Madansingh Stefan I
Zibrio, Inc. Houston, TX, United States.
Department of Internal Medicine, Division of Diabetes, Endocrinology and Metabolism, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States.
Front Med (Lausanne). 2020 Dec 4;7:591517. doi: 10.3389/fmed.2020.591517. eCollection 2020.
Falls are the leading cause of accidental death in older adults. Each year, 28.7% of US adults over 65 years experience a fall resulting in over 300,000 hip fractures and $50 billion in medical costs. Annual fall risk assessments have become part of the standard care plan for older adults. However, the effectiveness of these assessments in identifying at-risk individuals remains limited. This study characterizes the performance of a commercially available, automated method, for assessing fall risk using machine learning. Participants ( = 209) were recruited from eight senior living facilities and from adults living in the community (five local community centers in Houston, TX) to participate in a 12-month retrospective and a 12-month prospective cohort study. Upon enrollment, each participant stood for 60 s, with eyes open, on a commercial balance measurement platform which uses force-plate technology to capture center-of-pressure (60 Hz frequency). Linear and non-linear components of the center-of-pressure were analyzed using a machine-learning algorithm resulting in a postural stability (PS) score (range 1-10). A higher PS score indicated greater stability. Participants were contacted monthly for a year to track fall events and determine fall circumstances. Reliability among repeated trials, past and future fall prediction, as well as survival analyses, were assessed. Measurement reliability was found to be high (ICC(2,1) [95% CI]=0.78 [0.76-0.81]). Individuals in the high-risk range (1-3) were three times more likely to fall within a year than those in low-risk (7-10). They were also an order of magnitude more likely (12/104 vs. 1/105) to suffer a spontaneous fall i.e., a fall where no cause was self-reported. Survival analyses suggests a fall event within 9 months (median) for high risk individuals. We demonstrate that an easy-to-use, automated method for assessing fall risk can reliably predict falls a year in advance. Objective identification of at-risk patients will aid clinicians in providing individualized fall prevention care.
跌倒是老年人意外死亡的主要原因。每年，美国65岁以上的成年人中有28.7%经历过跌倒，导致超过30万例髋部骨折，医疗费用高达500亿美元。年度跌倒风险评估已成为老年人标准护理计划的一部分。然而，这些评估在识别高危个体方面的有效性仍然有限。本研究描述了一种使用机器学习评估跌倒风险的商用自动化方法的性能。参与者（n = 209）从八个老年生活设施和居住在社区的成年人（德克萨斯州休斯顿的五个当地社区中心）中招募，参与为期12个月的回顾性和为期12个月的前瞻性队列研究。入组时，每位参与者在一个使用测力板技术捕捉压力中心（60赫兹频率）的商用平衡测量平台上睁眼站立60秒。使用机器学习算法分析压力中心的线性和非线性成分，得出姿势稳定性（PS）评分（范围为1 - 10）。PS评分越高表明稳定性越好。一年中每月与参与者联系，跟踪跌倒事件并确定跌倒情况。评估重复试验之间的可靠性、过去和未来跌倒预测以及生存分析。发现测量可靠性很高（ICC(2,1) [95% CI]=0.78 [0.76 - 0.81]）。高风险范围（1 - 3）内的个体在一年内跌倒的可能性是低风险（7 - 10）个体的三倍。他们自发跌倒（即自我报告无原因的跌倒）的可能性也高一个数量级（12/104对1/105）。生存分析表明高风险个体在9个月（中位数）内会发生跌倒事件。我们证明，一种易于使用的自动化跌倒风险评估方法可以可靠地提前一年预测跌倒。对高危患者的客观识别将有助于临床医生提供个性化的跌倒预防护理。
