The George Institute for International Health, Missenden Road, Sydney NSW 2050, Australia.
J Rehabil Med. 2010 May;42(5):482-8. doi: 10.2340/16501977-0550.
To develop and internally validate a simple falls prediction tool for rehabilitation settings.
Prospective cohort study.
A total of 533 inpatients.
Possible predictors of falls were collected from medical records, interview and physical assessment. Falls during inpatient stays were monitored.
Fourteen percent of participants fell. A multivariate model to predict falls included: male gender (odds ratio (OR) 2.70, 95% confidence interval (CI) 1.57-4.64), central nervous system medications (OR 2.50, 95% CI 1.47-4.25), a fall in the previous 12 months (OR 2.21, 95% CI 1.07-4.56), frequent toileting (OR 2.14, 95% CI 1.27-3.62) and tandem stance inability (OR 2.00, 95% CI 1.11-3.59). The area under the curve for this model was 0.74 (95% CI 0.68-0.80). The Predict_FIRST tool is a unit weighted adaptation of this model (i.e. 1 point allocated for each predictor) and its area under the curve was 0.73 (95% CI 0.68-0.79). Predicted and actual falls risks corresponded closely.
This tool provides a simple way to quantify the probability with which an individual patient will fall during a rehabilitation stay.
开发并内部验证一种用于康复环境的简单跌倒预测工具。
前瞻性队列研究。
共 533 名住院患者。
从病历、访谈和身体评估中收集跌倒的可能预测因素。监测住院期间的跌倒情况。
14%的参与者跌倒。一个多变量模型来预测跌倒包括：男性（优势比（OR）2.70，95%置信区间（CI）1.57-4.64），中枢神经系统药物（OR 2.50，95% CI 1.47-4.25），在过去 12 个月内跌倒（OR 2.21，95% CI 1.07-4.56），频繁如厕（OR 2.14，95% CI 1.27-3.62）和并足站立不能（OR 2.00，95% CI 1.11-3.59）。该模型的曲线下面积为 0.74（95% CI 0.68-0.80）。Predict_FIRST 工具是该模型的单位加权改编（即每个预测因素分配 1 分），其曲线下面积为 0.73（95% CI 0.68-0.79）。预测和实际跌倒风险密切对应。
该工具提供了一种简单的方法来量化个体患者在康复期间跌倒的概率。
