Robbins A S, Rubenstein L Z, Josephson K R, Schulman B L, Osterweil D, Fine G
Department of Medicine and Geriatric Research Education, Veterans Administration Medical Center, Sepulveda, CA 91348.
Arch Intern Med. 1989 Jul;149(7):1628-33.
A study was performed to identify and rank risk factors for falling among populations of institutionalized (fallers, N = 79, nonfallers, N = 70) and noninstitutionalized (fallers, N = 34, nonfallers, N = 34) elderly persons. Fallers were matched by age, sex, and living location to nonfaller control subjects. A nurse practitioner performed a comprehensive physical assessment in all subjects using a standardized protocol and physician consultation. Fallers in both populations were significantly more physically and functionally impaired than control subjects. Logistic regression identified hip weakness, poor balance, and number of prescribed medications as factors most strongly associated with falling among institutionalized subjects. A fall prediction model was developed from these findings yielding 76% overall predictive accuracy (89% sensitivity, 60% specificity). Using the model, the predicted 1-year risk of falling ranged from 12% for persons with none of the three risk factors to 100% for persons with all three risk factors. Findings among noninstitutionalized subjects were similar. These data support the concept of performing focused fall risk assessments to identify elderly patients at high risk for falling.
一项研究旨在确定并对机构化老人群体（跌倒者，N = 79；未跌倒者，N = 70）和非机构化老人群体（跌倒者，N = 34；未跌倒者，N = 34）中的跌倒风险因素进行排序。根据年龄、性别和居住地点将跌倒者与未跌倒的对照对象进行匹配。一名执业护士使用标准化方案并咨询医生，对所有受试者进行了全面的身体评估。这两类人群中的跌倒者在身体和功能方面的受损程度均显著高于对照对象。逻辑回归分析确定，髋部无力、平衡能力差和所开药物数量是与机构化受试者跌倒最密切相关的因素。基于这些发现开发了一个跌倒预测模型，总体预测准确率为76%（敏感性为89%，特异性为60%）。使用该模型，预测的1年跌倒风险范围为：无这三种风险因素的人中有12%，有所有这三种风险因素的人中有100%。非机构化受试者中的结果相似。这些数据支持了进行针对性跌倒风险评估以识别跌倒高危老年患者这一理念。
