在ICU入院时,使用机器学习预测碳烯酶生成肠杆菌的殖民
Ji Hun Kim1, Eunmi Yang2, Yun Woo Lee2
1Department of Emergency Medicine, College of Medicine, Uijeongbu St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea.
Scientific reports
|January 30, 2026
概括
预测在重症监护室 (ICU) 入院时产生卡巴酶的肠细菌 (CPE) 殖民对于感染控制至关重要. 机器学习模型可以帮助早期识别载体,改善患者安全和医院卫生.
科学领域:
- 传染性疾病 传染性疾病
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 在重症监护室 (ICU) 入院时,产生卡巴酶的肠道细菌 (CPE) 的殖民形成了严重的感染控制挑战.
- 早期发现CPE携带者至关重要,但在临床实践中很困难.
- 开发预测模型可以帮助及时隔离CPE殖民患者.
研究的目的:
- 开发和验证可解释的机器学习模型,用于预测ICU入院时的CPE殖民.
- 支持临床决策,为早期隔离患有CPE的患者提供支持.
- 创建一个实用的工具,用于对CPE运输的风险评估.
主要方法:
- 4,915名成年人ICU入院的回顾性队列研究 (2022年1月至2023年12月).
- 在入院后48小时内通过直肠抽样培养确定CPE定居点.
- 使用电子病历中的42个候选变量对10个机器学习算法的评估.
主要成果:
- 9.2%的入学者 (453/4,915) 感染了CPE.
- 一个物流回归模型确定了12个关键预测因素 (例如,抗生素暴露,设备使用).
- 最好的模型实现了0.73的灵敏度,0.77的ROC-AUC和0.96的负预测值.
结论:
- 机器学习模型可以有效地预测ICU入院时的CPE殖民.
- 使用这些模型的基于网络的工具可以帮助临床医生识别有风险的患者.
- 开发的工具显示出有希望的帮助排除CPE携带,增强感染控制策略.
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