A Postural Assessment Utilizing Machine Learning Prospectively Identifies Older Adults at a High Risk of Falling

Frontiers in Medicine

article

Katharine E. Forth¹  Kelly L. Wirfel²  Sasha D. Adams³  Nahid J. Rianon⁴  Erez Lieberman Aiden¹  Stefan I. Madansingh¹ 

[1] Inc., United States;Department of Internal Medicine, Division of Diabetes, McGovern Medical School, University of Texas Health Science Center at Houston, United States;Department of Surgery, McGovern Medical School, University of Texas Health Science Center at Houston, United States;Department of Family and Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, United States;Department of Internal Medicine, Division of Geriatric and Palliative Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, United States

关键词: balance;    stability;    postural stability;    fall risk;    aging;    fall prediction;    machine learning;   

DOI  :  10.3389/fmed.2020.591517

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

Introduction: 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. Methods: Participants ( N = 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. Results: 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. Conclusions: 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.

【 授权许可】

CC BY