针对重症患者的细菌感染预测模型的可移植性
Garrett Eickelberg1, Lazaro Nelson Sanchez-Pinto1,2, Adrienne Sarah Kline1
1Department of Preventive Medicine (Health & Biomedical Informatics), Feinberg School of Medicine, Chicago, IL 60611, United States.
Journal of the American Medical Informatics Association : JAMIA
|August 30, 2023
概括
细菌感染 (BI) 风险模型显示在不同重症监护病房 (ICU) 中具有良好的可转移性. 在在不同患者群体中实施BI风险模型之前,外部验证至关重要.
科学领域:
- 关键护理医学 关键护理医学
- 医疗信息学 医疗信息学
- 流行病学 流行病学
背景情况:
- 细菌感染 (BI) 在重症监护中存在重大风险,往往导致长期使用抗生素.
- 一个以前开发的BI风险模型旨在优化抗生素持续时间并改善患者的治疗结果.
- 评估模型的可运输性对于现实世界的临床应用至关重要.
研究的目的:
- 评估细菌感染 (BI) 风险模型在各种重症监护室 (ICU) 设置中的可转移性.
- 评估多站点学习对模型性能和可转移性的影响.
- 确定BI风险模型在外部临床队列中的预测效用.
主要方法:
- 一个BI风险模型被开发和验证,使用来自密集护理III (MIMIC) 和西北医学三级 (NM-T) ICU的医疗信息中心的数据.
- 通过对外部验证数据集 (包括社区ICU) 的性能进行评估,评估了该模型的可运输性.
- 探索了多站点学习技术,以提高模型的可运输性.
主要成果:
- 内部验证显示了强的性能,AUROC为0.78 (MIMIC) 和0.81 (NM-T).
- NM-T模型显示出强大的可转移到社区ICU环境 (AUROC 0.81),而MIMIC模型显示出不太有利的可转移 (AUROC 0.74).
- 多站点学习并没有显著改善歧视,但在数据集中增强了模型稳定性.
结论:
- 细菌感染 (BI) 风险模型在应用于外部患者队伍时保持预测效用.
- 在临床实践中实施风险预测模型之前,跨不同人群的外部验证至关重要.
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