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机器学习可以预测血管内腹腔大动脉动脉瘤修复后的结果

Ben Li^1,2,3,4, Badr Aljabri⁵, Raj Verma⁶

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

The British journal of surgery

|September 15, 2023

概括

机器学习使用手术前数据准确预测内血管动脉瘤修复 (EVAR) 后的一年死亡率. 这种使用XGBoost的先进方法显著优于腹腔大动脉动脉瘤 (AAA) 患者的传统后勤回归.

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) 具有外科手术期间的风险.
目前,没有广泛采用的工具可以预测EVAR后的结果.
这项研究解决了对EVAR.可靠结果预测的需求.

研究的目的:

开发和验证机器学习 (ML) 算法,用于预测EVAR后的一年死亡率.
将ML模型的性能与传统的物流回归进行比较.
确定EVAR患者死亡率的关键手术前预测因素.

主要方法:

使用血管质量倡议数据库 (2003-2023) 选择性EVAR病例.
包括47个手术前的人口和临床变量.
训练有素的6ML模型和使用70%数据的后勤回归,对30%的数据进行验证,并进行了10倍的交叉验证.

主要成果:

包括63,655名患者;4.9%的人经历了一年死亡率.
XGBoost模型实现了最高的预测性能,AUROC为0.96 (0.95-0.97).
最重要的预测因素包括不适合开放性修复,功能状态和手术前透析.

结论:

机器学习模型有效地使用手术前数据预测EVAR后1年死亡率.
在这个群体中,ML模型的性能明显超过了后勤回归.
开发的算法为EVAR患者的风险分层提供了一个有希望的工具.

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