基于机器学习的STEMI患者的长期死亡率预测,使用临床,实验室和炎症代谢指数
Gökhan Keskin1, Abdulkadir Çakmak1, Mehmet Uğur Çalışkan1
1Department of Cardiology, Faculty of Medicine, Amasya University, Amasya 05200, Türkiye.
Journal of clinical medicine
|March 14, 2026
概括
机器学习模型XGBoost准确地预测了ST段升高心肌梗塞 (STEMI) 患者的长期死亡风险,这些患者正在接受一次性皮肤穿冠状动脉干预 (pPCI). 新的炎症代谢指数改善了风险分层.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- ST段升高心肌梗塞 (STEMI) 具有显著的长期死亡风险.
- 初级穿皮冠状动脉干预 (pPCI) 是STEMI的标准治疗方法.
- 准确预测死亡风险对于患者管理和治疗优化至关重要.
研究的目的:
- 为了比较各种机器学习 (ML) 模型在预测pPCI后STEMI患者的长期死亡率方面的表现.
- 在这个患者队列中评估新型炎症代谢指数的预后价值.
- 确定死亡率的关键预测因子,以改善风险分层.
主要方法:
- 对接受pPCI的329名STEMI患者进行了回顾性分析.
- 开发和比较五个ML算法:物流回归 (LR),随机森林 (RF),极端梯度增强 (XGBoost),支持向量机器 (SVM) 和人工神经网络 (ANN).
- 使用精度,灵敏度,特异性和ROC-AUC的性能评估;用于模型解释的SHAP分析.
主要成果:
- XGBoost模型表现出卓越的性能,准确度为98.99%,ROC-AUC为0.999,灵敏度为100%.
- 较高的门到气球时间 (DTBT),全身炎症反应指数 (SIRI) 和泛免疫炎症值 (PIV) 与死亡率有关.
- 在死亡率组中观察到下体质指数 (BMI),预后营养指数 (PNI) 和高级肺癌炎症指数 (ALI).
- SHAP分析确定了DTBT,白蛋白和ALI作为最强的死亡率预测因素.
结论:
- XGBoost算法是预测STEMI患者长期死亡率的高度准确和可靠的工具.
- 将新的炎症代谢指数 (如ALI和TyG) 与DTBT整合到ML模型中,可以提高早期风险识别和分层.
- 这些发现表明,通过先进的预测建模,可以改善临床决策和患者的治疗结果.
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