开发和验证基于机器学习的预测模型,用于重症监护室重症肺炎死亡率:双中心回顾性研究
JunYing Niu1, XiaoJie Lv2, Lin Gao1
1Department of Critical Care Medicine (East Campus), Yantaishan Hospital, No. 10087 Keji Avenue, Laishan District, Yantai 264003 Shandong Province, China.
International journal of medical informatics
|August 10, 2025
概括
使用临床数据的机器学习模型可以预测ICU中的严重肺炎 (SP) 死亡率. 后勤回归显示出最准确的准确性,帮助预防策略.
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
- 生物统计学 生物统计学
背景情况:
- 严重的肺炎 (SP) 在重症监护室 (ICU) 构成严重的死亡风险.
- 目前用于预测SP的ICU死亡率的工具是有限的.
- 本研究旨在开发和验证用于SP在ICU死亡率预测的机器学习 (ML) 模型.
研究的目的:
- 开发和外部验证基于ML的模型,用于预测严重肺炎患者的ICU死亡率.
- 确定进入ICU的SP患者死亡率的关键临床预测因素.
- 为指导临床实践中的预防策略提供一个工具.
主要方法:
- 来自两个中心 (培训和外部验证) 的成年SP患者的回顾性分析.
- 通过LASSO回归来识别关键预测因素.
- 开发和评估五种ML模型:物流回归 (LR),随机森林 (RF),RBF-SVM,线性SVM和XGBoost.
- 使用ROC曲线下的面积 (AUC),灵敏度,特异性,校准和临床实用性的性能评估.
主要成果:
- 包括501名患者;44%的ICU死亡率.
- 确定了关键预测因素:年龄,血管压力剂,化疗,SpO2,D-二次数,血小板,NT-proBNP,侵入性机械通风.
- 外部验证显示LR具有AUC=0.76,RF具有AUC=0.77,RBF-SVM具有AUC=0.746,线性SVM具有AUC=0.741,XGBoost具有AUC=0.76.
- 后勤回归在最佳值时表现出最佳的临床准确性.
结论:
- 使用共同临床特征的ML模型在预测SP的ICU死亡率方面表现有希望.
- 开发的物流回归模型和名图可以指导预防策略.
- 需要进一步改进,建议在未来的研究中纳入额外的生物标志物.
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