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在心脏骤停后6个月内死亡的预测模型的开发和验证

Jianping Lu^1,2, Yuqi Zeng^1,2, Nan Lin³

¹Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, China.

Frontiers in cardiovascular medicine

|December 13, 2024

概括

预测心脏骤停 (CA) 后的死亡至关重要. 这项研究开发了使用早期逮捕后因素预测6个月死亡率的模型,随机森林模型显示了最好的预测性能.

科学领域:

心脏病学心脏病学
关键护理医学关键护理医学
在医疗保健中的数据科学.

背景情况:

心脏骤停 (CA) 恢复自发循环 (ROSC) 后的结果仍然很差.
对于在CA后出现ROSC的患者,需要对预后进行早期评估.
这项研究旨在开发和验证CA后6个月死亡率的预测模型.

研究的目的:

使用早期心脏骤停后的因素,开发心脏骤停 (CA) 后6个月死亡率的预测模型.
通过外部验证这些模型,并比较它们的预测效率.
确定影响ROSC后长期结果的关键风险因素.

主要方法:

追溯队列研究包括199名患者 (培训组) 和42名患者 (验证组),他们在CA后获得ROSC.
后勤回归分析以确定风险因素 (P < 0.1).
使用随机森林,支持矢量机 (SVM) 和名ogram方法开发预测模型.
使用ROC,决策和校准曲线评估模型性能.

主要成果:

六个月死亡率的关键风险因素包括CA的持续时间,ROSC后的乳酸盐水平,二次感染,住院和呼吸机支持.
随机森林模型表现出优异的性能,AUC为0.991 (培训) 和0.703 (验证).
随机森林模型显示了最好的净效益和与实际结果一致的校准.

结论:

早期心脏骤停后的因素可以有效地预测心脏骤停后6个月的预后.
随机森林模型提供了优越的预测效率,与SVM和nomogram模型相比,用于后CA死亡率.
这些模型可以帮助临床医生评估患者的预后,并指导治疗决策.

关键词:

心脏骤停是因为心脏停止了.机器学习是机器学习.预测模型预测模型预后预后预后恢复了自发的循环.

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