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由败血症复杂的心力衰竭的生存预测:基于机器学习方法
Qitian Zhang1, Lizhen Xu2, Weibin He1
1Department of Cardiology, Zhangzhou Affiliated Hospital of Fujian Medical University, Zhangzhou, Fujian, China.
Frontiers in medicine
|October 18, 2024
概括
一种新的后勤回归 (LR) 模型有效预测心力衰竭和败血症患者的28天死亡率. 这种工具有助于临床医生识别高风险个体,以便更好地分配资源和改善患者的治疗结果.
科学领域:
- 心脏病学 心脏病学
- 关键护理医学 关键护理医学
- 医疗信息学 医疗信息学
背景情况:
- 心力衰竭和败血症显著增加死亡风险.
- 早期识别高风险患者对于资源分配至关重要.
研究的目的:
- 为患有并发性心力衰竭和败血症的患者开发和验证生存预测模型.
- 在这个患者群体中确定28天全因死亡率的关键预测因素.
主要方法:
- 使用eICU-CRD数据库进行模型开发 (3891名患者) 和MIMIC-IV数据库进行外部验证 (2928名患者).
- 采用Boruta方法进行特征选择和XGBoost进行特征排名.
- 进行对比后勤回归 (LR),XGBoost,AdaBoost和高斯天真贝叶斯 (GNB) 模型.
- 使用AUC,精度,灵敏度和特异性评估模型性能;使用SHAP进行解释性.
主要成果:
- 该LR模型表现出卓越的性能,验证AUC为0.746.
- 关键预测因素包括年龄,通风,北上腺素,白细胞计数,总 bilirubin,温度,烯,呼吸速率,中性粒细胞计数和静脉血压.
- 使用MIMIC-IV进行外部验证时,AUC值为0.699.
结论:
- 一个强大的基于LR的模型准确地预测了败血症心力衰竭患者的28天死亡率.
- 该模型为临床医生提供了一种有价值的工具,以识别高风险患者并指导临床实践.
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