更新重症监护室的死亡风险估计,使用高维电子健康记录,不完整的数据
Bertrand Bouvarel1, Fabrice Carrat2,3, Nathanael Lapidus2,3
1Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique IPLESP, Paris, F75012, France. bouvarel.bertrand@gmail.com.
BMC medical informatics and decision making
|August 30, 2023
概括
这项研究使用纵向患者数据开发了重症监护室 (ICU) 中短期死亡率的预测模型. 这些模型提供了一个强大的监控工具,用于早期检测死亡风险,即使缺少电子健康记录信息.
科学领域:
- 医疗信息学 医疗信息学
- 临床预测模型临床预测模型
- 健康 数据科学 数据科学
背景情况:
- 目前的重症监护病房 (ICU) 死亡率预测依赖于入院数据,对长期住院的患者可能效果不佳.
- 现有的模型通常在预测长时间住院患者的死亡率方面存在局限性.
- 需要使用纵向数据进行持续患者监测的预测工具.
研究的目的:
- 利用纵向患者数据,开发和评估ICU中短期死亡率的预测模型.
- 为了比较各种统计学习方法对ICU死亡率预测的性能.
- 评估数据归算方法对预测模型准确性和通用性的影响.
主要方法:
- 利用了来自MIMIC-III数据库的纵向数据,用于至少48小时停留的患者.
- 比较统计学习方法,包括深度神经网络和惩罚性回归.
- 使用完整案例分析和多重归算技术解决缺失的数据.
主要成果:
- 用19个预测因子进行完整病例分析,可以预测12-24小时内死亡的良好歧视 (AUC>0.77),但排除了75%的患者.
- 多重归算使70个预测因素的纳入成为可能,并保留了95%的具有可比预测性能的患者.
- 开发的模型证明了数据归算方法的稳定性.
结论:
- 使用纵向数据对电子健康记录的自动化分析可以在患者在重症监护室逗留期间作为一种有价值的监控工具.
- 尽管需要许多预测因素,但拟议的框架对于数据归算是可靠的,并且对于早期风险预测是有效的.
- 这项概念验证研究突出了先进分析的潜力,以改善ICU患者监测和结果.
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