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使用机器学习预测腹腔大动脉动脉瘤开放修复后的结果

Ben Li^1,2,3,4, Badr Aljabri⁵, Derek Beaton⁶

¹Department of Surgery, University of Toronto, Toronto, Canada.

Scientific reports

|April 24, 2025

概括

机器学习准确地预测30天的主要不良心血管事件 (MACE) 后开放腹腔大动脉瘤 (AAA) 修复. 这个工具可以指导风险评估,并改善患者在AAA手术的结果.

科学领域:

心血管外科心血管外科
医疗信息学医疗信息学
医疗保健中的机器学习

背景情况:

腹腔大动脉动脉瘤 (AAA) 的开放性手术修复具有显著的术后并发症风险.
目前,在接受开放AAA修复的患者中,缺乏有效的工具来预测手术风险.

研究的目的:

开发和验证自动机器学习 (ML) 算法,用于预测开放AAA修复后的30天不良结果.
在这个患者队列中确定主要心血管不良事件 (MACE) 的术前预测因素.

主要方法:

利用国家外科质量改进计划的血管数据库 (2011-2021) 对接受选择性,非破裂的开放AAA修复的患者.
训练了6个ML模型,使用35个手术前变量,以后勤回归作为基线.
使用10倍交叉验证评估模型性能,重点关注30天的MACE (心肌梗塞,中风或死亡).

主要成果:

XGBoost ML模型表现出卓越的预测性能,实现了0.90的接收器操作特征曲线 (AUROC) 下面面积 (95%CI:0.89-0.91).
后勤回归实现了明显较低的AUROC,为0.66 (95%CI:0.64-0.68).
最好的ML模型显示了良好的校准,布赖尔得分为0.03,表明预测和观察事件概率之间存在强烈一致.

结论:

自动化的ML算法可以在开放AAA修复后有效预测30天的MACE.
这些ML工具提供了一种有希望的方法来指导手术前风险分层,并为临床决策提供信息.
实施这些算法可能有助于制定有针对性的降低风险的策略,以改善患者的治疗结果.

关键词:

机器学习是机器学习.开放腹腔大动脉动脉瘤修复结果的结果.预测预测预测

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