开发一个预测模型的功能和认知能力的长期护理家庭居民的损失:一个协议
Christina Y Yin1,2, Robert Talarico3, Mary M Scott2,4
1Bruyere Research Institute, Ottawa, Ontario, Canada.
BMJ open
|January 9, 2025
概括
这项研究开发了预测模型,以预测长期护理居民的功能衰退和认知障碍,帮助个性化医疗决策并改善残疾人的生活质量.
科学领域:
- 老年学是指老年学的学科.
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 长期护理 (LTC) 居民经常经历身体和认知障碍,需要大量的日常帮助.
- 对于LTC居民来说,如果医疗干预不符合个人愿望,可能会导致痛苦而没有明显的益处.
- 预测模型可以通过指导治疗建议来支持以人为中心的护理.
研究的目的:
- 为LTC居民推导和验证时间到事件的预测模型.
- 预测 (1) 永久失去身体独立, (2) 永久严重的认知障碍,以及 (3) 完全依赖的生存时间.
- 为长期护理提供知情,以人为中心的医疗决策提供工具.
主要方法:
- 利用来自加拿大安大略省的基于人口的行政健康数据进行导出和内部验证.
- 雇佣了来自加拿大阿尔伯塔省的外部验证队列,使用了从文献,患者顾问和专家意见中识别的预测因素.
- 开发了时间到事件模型,包括完整和缩小版本,评估预测准确性,歧视和校准,并创建了外部验证的风险组.
主要成果:
- 确定了50个物理功能损失的变量,58个认知障碍的变量和36个依赖时间的变量.
- 模型被开发和验证,以预测失去独立的时间和被残疾人所花费的时间.
- 创建了风险组,并进行了外部验证,以提供中位时间到事件估计.
结论:
- 该研究协议概述了为长期护理中的关键结果创建强大的预测模型.
- 这些模型旨在加强临床决策,并支持为住户提供个性化护理.
- 开发的预测算法将公开使用,以促进更广泛的应用.
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