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在老年护理中使用例行收集的电子健康数据开发和内部验证动态跌倒风险预测和监测工具:一个具有里程碑意义的方法
Nasir Wabe1, Isabelle Meulenbroeks1, Guogui Huang1
1Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, North Ryde, NSW 2109, Australia.
概括
为老年护理居民开发了新的动态跌倒风险模型. 这些模型与评分系统一起,为持续的跌倒风险监测和预防提供及时,以人为中心的见解.
科学领域:
- 老年学是一门学科.
- 流行病学 流行病学
- 医疗信息学 医疗信息学
背景情况:
- 跌倒是居住老年护理设施 (RACF) 的一个主要问题.
- 目前的跌落预测工具是不够的,并没有考虑到风险因素的变化.
- 需要动态风险评估才能有效预防跌倒.
研究的目的:
- 开发和验证动态跌倒风险预测模型.
- 为患有和没有痴呆症的居民创建基于积分的评分系统.
- 为了改善RACF设置下跌的预测.
主要方法:
- 在澳大利亚悉尼的27个RACF中对5492名居民进行了纵向队列研究.
- 使用电子健康记录和60个月的月度数据里程碑.
- 用地标动态预测和时间依赖的AUROCC用于模型评估.
主要成果:
- 确定了跌倒的关键预测因素:跌倒史 (HR 4.75 痴呆症,4.20 非痴呆症).
- 实现时间依赖的AUROCC通常在0.75-0.85之间,无论是痴呆症还是非痴呆症队列.
- 开发了基于点的风险评分,范围从-2到57 (痴呆症) 和0到52 (非痴呆症).
结论:
- 新的动态风险预测模型和评分系统提供及时,以人为中心的跌倒风险监测.
- 这些工具可以支持主动的,有针对性的干预措施,以预防RACF的跌倒.
- 集成到电子健康记录可以提高实施和居民安全.
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