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使用机器学习预测内血管腹腔大动脉动脉瘤修复后的结果

Ben Li^1,2,3,4, Raj Verma⁵, Derek Beaton⁶

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

Annals of surgery

|June 30, 2023

概括

机器学习模型准确地预测了腹腔大动脉动脉瘤 (AAA) 血管内动脉瘤修复 (EVAR) 后的30天结果. 这些先进的算法优于传统方法,有助于对接受EVAR的患者进行风险分层.

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:

腹腔大动脉动脉瘤 (AAA) 的内血管动脉瘤修复 (EVAR) 有风险,但缺乏确定的结果预测工具.
准确预测术后风险对于患者管理至关重要.

研究的目的:

开发和评估机器学习 (ML) 模型,用于预测EVAR后的30天结果.
将ML模型的性能与传统的物流回归进行比较.

主要方法:

利用国家外科质量改进计划数据库 (2011-2021) 对接受EVAR治疗的患者进行了下AAA.
使用36个手术前变量训练了6ML模型,并使用接收器操作特征曲线 (AUC) 下的面积进行评估.
通过校准图表和布里尔分数评估模型的稳定性,包括子组分析.

主要成果:

XGBoost ML模型实现了0.95 (0.94-0.96) 的最高AUC,显著超过了物流回归 (0.72 [0.70-0.74]).
最好的模型表现出良好的校准 (布赖尔分数为0.06) 和在各种患者子组中强大的性能.
在16,282名患者中,观察到30天主要不良心血管事件的发生率为2.4%.

结论:

开发了准确的ML模型,使用手术前数据预测30天EVAR后的结果.
这些模型为EVAR风险预测提供了与后勤回归相比更高的性能.
自动化算法可以为被考虑接受EVAR的患者提供风险减轻策略.

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