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基于机器学习的非心脏手术心血管风险计算器

Nour Al Khatib¹, Ali Chehab¹, Hani Tamim^2,3

¹Department of Electrical and Computer Engineering, American University of Beirut Maroun Semaan Faculty of Engineering and Architecture, Beirut, Lebanon.

|January 7, 2026

概括

使用LightGBM的机器学习模型准确地预测了50岁以上接受非心脏手术的患者的心血管风险. 这种工具有助于识别高风险个体,以获得更好的外科手术结果.

科学领域:

心血管医学心血管医学
机器学习在医疗保健中的应用.
手术风险评估手术风险评估

背景情况:

全球4%的人口每年接受非心脏手术.
30%的这些患者有心血管风险因素,30天死亡率为0.5%-2%.
在这个人群中需要准确的心血管风险预测.

研究的目的:

开发一个可解释的机器学习模型用于心血管风险评分.
预测50岁以上接受非心脏手术的患者的风险.
从手术日期到手术后30天评估风险.

主要方法:

使用了NSQIP 2022数据集 (4,970,011名患者).
主要终点定义为30天死亡,心肌梗塞,心脏骤停或中风.
使用AUROC.训练和评估多个机器学习算法 (逻辑回归,天真贝叶斯,随机森林,提升树木).

主要成果:

轻GBM实现了最高的AUROC,即0.9009 (95%CI:0.8889-0.9126).
最好的模型确定了六个关键预测因素:手术类型,ASA分类,BUN,败血症,紧急手术和机械通风.
该模型表现出强大的预测准确性和概括性.

结论:

在这种情况下,LightGBM分类器是心血管风险评分的最佳选择.
该模型有效地平衡了预测准确性和概括性.
确定了非心脏手术患者心血管风险评估的关键因素.

关键词:

生物统计学生物统计学心肌梗塞的心脏病发作风险因素风险因素这是一次中风.

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