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使用机器学习来预测胸部和复杂的内血管大动脉动脉瘤修复后的结果
Ben Li1,2,3,4, Naomi Eisenberg5, Derek Beaton6
1Department of Surgery University of Toronto Toronto Canada.
Journal of the American Heart Association
|March 3, 2025
概括
机器学习准确地预测了复杂的大动脉动脉瘤修复后的结果. 这些先进的算法优于传统方法,改善了对胸内血管大动脉修复 (TEVAR) 和内血管动脉瘤修复 (EVAR) 的患者风险评估.
科学领域:
- 血管外科 血管外科
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 胸内血管大动脉修复 (TEVAR) 和复杂内血管动脉瘤修复 (EVAR) 是高风险的程序.
- 对于这些干预措施,现有的风险预测工具存在局限性.
- 需要改进的方法来预测患者的结果.
研究的目的:
- 开发和评估机器学习算法,用于预测TEVAR和复杂EVAR后的一年不良结果.
- 将机器学习模型的性能与传统的物流回归进行比较.
主要方法:
- 使用了血管质量倡议数据库 (2012-2023) 对接受选择性TEVAR/EVAR的患者.
- 提取了172个手术前,手术内和手术后的特征.
- 训练了六个机器学习模型,包括极端梯度提升,使用70%的数据,并对30%进行了验证.
主要成果:
- 极端梯度增强模型在术前预测中表现出优异的预测性能 (AUC 0.96),与后勤回归 (AUC 0.70) 相比.
- 该模型在手术内 (AUC 0.97) 和术后 (AUC 0.98) 阶段保持了高准确度.
- 优秀的校准和低的布里尔分数表明预测和观察结果之间的良好一致.
结论:
- 机器学习模型,特别是极端梯度提升,可以准确地预测TEVAR和复杂的EVAR后的一年改变生活的事件.
- 这些模型在结果预测方面比传统的逻辑回归提供了更好的性能.
- 这些发现支持在复杂的大动脉修复程序的临床决策中使用机器学习.
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