在冠状动脉旁路移植后基于机器学习的短期死亡率预测:回顾性队列研究
Islam Salikhanov1, Volker Roth2, Brigitta Gahl1,3
1Department of Cardiac Surgery, University Hospital Basel, University of Basel, 4031 Basel, Switzerland.
Biomedicines
|August 28, 2025
概括
机器学习模型准确地预测了冠状动脉旁路移植 (CABG) 后的30天死亡率. 与EuroSCORE II模型相比,整合手术前和手术后的数据显著改善了预测.
科学领域:
- 心血管外科
- 医疗信息学
- 机器学习
背景情况:
- 冠状动脉旁路移植 (CABG) 是一种常见的心脏手术.
- 准确预测短期死亡率对于患者的管理至关重要.
- 像EuroSCORE II这样的现有风险模型有其局限性.
研究的目的:
- 开发和验证机器学习 (ML) 算法,以预测隔离CABG后的30天死亡率.
- 将ML模型性能与EuroSCORE II风险预测模型进行比较.
主要方法:
- 对3483名接受隔离CABG的成年患者进行了回顾性分析 (2009-2022年).
- 对比了三个模型:EuroSCORE II变量,EuroSCORE II+手术前变量,以及EuroSCORE II+手术前和手术后变量.
- 使用后勤回归,随机森林和神经网络模型.
- 使用曲线下的面积 (AUC) 和在85%的灵敏度下评估预测准确性.
主要成果:
- 整体30天死亡率为2. 5%.
- 与基线EuroSCORE II相比,使用手术前变量的ML模型提高了特异性.
- 包括手术前和手术后数据的模型在所有ML方法中实现了约70%的特异性.
- 关键的术后预测因素包括功能衰竭,肺部并发症和心肌梗塞.
结论:
- 机器学习模型在预测隔离CABG后的短期死亡率方面明显优于EuroSCORE II.
- 包括手术前和手术后的数据提高了预测准确度.
- 在心脏手术患者中改善风险分层提供了一个有前途的工具.
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