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设计用于住院康复设施的跌倒预测模型,使用易于获得的数据
Jonathan R Wright1, Jamie D'Ausilio2, Janene M Holmberg2
1Rehabilitation Services, Intermountain Health, Murray, UT; Physical Therapy Department, Rocky Mountain University of Health Professions, Provo, UT.
Archives of physical medicine and rehabilitation
|December 9, 2023
概括
一个新的住院康复摔倒量表 (IRF量表) 有效地使用现有数据识别患有摔倒风险的患者. 这种工具在预测患者跌倒方面优于摩尔斯跌倒量表 (MFS).
科学领域:
- 康复医学 康复医学 康复医学
- 患者安全 患者安全
- 临床评估工具 临床评估工具
背景情况:
- 跌倒是住院康复设施 (IRF) 的一个重大问题,影响患者的康复和医疗费用.
- 现有的跌倒风险评估工具可能无法充分利用现有数据或在IRF设置中证明最佳预测准确性.
研究的目的:
- 开发一种新的跌倒风险评估工具,即住院康复跌倒量表 (IRF量表),利用IRF中现有的数据.
- 为了比较新开发的IRF尺度与已建立的摩尔斯落尺度 (MFS) 的预测准确度.
主要方法:
- 进行了一项回顾性队列研究,使用4个IRF的1699名患者的数据.
- 使用一个多变量后勤回归模型,将与跌倒相关的质量改进 (QI) 代码结合起来,创建了IRF尺度.
- 采用接收机操作员特征 (ROC) 曲线分析来比较IRF尺度和MFS的预测准确性 (曲线下的面积 - AUC,灵敏度,特异性).
主要成果:
- IRF尺度在识别摔倒的患者方面表现出可接受的准确性,曲线下面面积 (AUC) 为0.72.
- 确定IRF尺度的最佳切断分数为22.4,产生0.74的灵敏度和0.63.6的特异性.
- 在识别以前属于回顾性队列的患者时,IRF量表优于MFS.
结论:
- 内部风险评估尺度 (IRF Scale) 通过利用现有数据,为内部风险评估提供了一种有价值的工具,从而最大限度地降低了员工额外的评估负担.
- 与MFS相比,IRF尺度在预测基于回顾性数据的跌幅方面表现优越.
- 进一步的前性研究是必要的,以验证IRF尺度能够预测未来在IRF停留期间下降的能力.
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