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使用时间到事件分析优先考虑恶化的患者:预测模型开发和内部-外部验证.
Robin Blythe1, Rex Parsons2, Adrian G Barnett2
1Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, Qld, 4059, Australia. robin.blythe@qut.edu.au.
Critical care (London, England)
|July 17, 2024
概括
使用生命体征数据的时间到事件模型可以比传统的二进制模型更好地预测急性护理患者的临床恶化. 这种方法有助于优先考虑患者评估,以便更有效地提供医疗保健.
科学领域:
- 临床信息学是一种临床信息学.
- 生物统计学 生物统计学
- 医疗保健系统工程 医疗保健系统工程
背景情况:
- 对于临床恶化的传统二进制分类模型忽略了事件时间.
- 时间到事件模型通过结合时间数据提供了一个有希望的替代方案.
- 这些模型可以通过患者风险分层来增强临床工作流程.
研究的目的:
- 调查时间到事件建模对生命体征数据的有用性,以优先考虑患者恶化评估.
- 开发和验证一个预测模型,通过临床恶化风险对急性护理住院患者进行分层.
主要方法:
- 开发和验证可克斯回归模型的时间到住院死亡率使用时间变化的共变量.
- 利用了来自澳大利亚5家医院 (2019-2020) 的成人住院医疗记录.
- 将考克斯回归与离散时间逻辑回归模型进行比较,以预测性能.
主要成果:
- 考克斯回归显示出比逻辑回归更高的歧视 (AUC为0.96对比0.93的24小时死亡率).
- 在1周后,Cox模型的歧视仍然更高 (AUC为0.93对0.88).
- 校准因医院而异,但可以通过根据预测风险对患者进行排名来改进.
结论:
- 时间变化的共变Cox模型对于急性护理环境中的患者分拣是有效的.
- 这些模型可以在具有可变观察时间的环境中提高护理效率.
- 使用时间到事件分析进行风险分层增强了病情恶化的患者的临床决策.
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