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在单个中心使用随机森林分类算法预测腹腔大动脉动脉瘤选择性修复后的两年生存率

Daniel C Thompson¹, Rhiannon Hackett², Peng F Wong¹

¹Department of Vascular Surgery, James Cook University Hospital, Middlesbrough, UK.

European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery

|December 5, 2024

概括

机器学习准确地预测了选择性腹腔大动脉瘤 (AAA) 修复后的两年生存期. 这个工具有助于评估考虑AAA手术的患者的风险与益处.

科学领域:

心血管外科心血管外科
机器学习应用机器学习应用
医疗信息学医疗信息学

背景情况:

腹腔大动脉动脉瘤 (AAA) 的选择性修复需要平衡破裂风险与术前死亡率和预期寿命.
随机森林分类器 (RFC) 是先进的机器学习算法,具有临床预测的潜力.

研究的目的:

开发和验证一个RFC工具,用于预测选择性AAA修复后的两年生存期.
评估机器学习在AAA手术风险分层中的实用性.

主要方法:

分析了2008年至2021年期间接受选择性AAA修复 (开放或内血管) 的925名患者队列.
在70%的数据上训练了一个RFC模型,并在剩余的30%上进行验证,以预测两年生存率.
患者数据包括心肺运动测试,CT扫描和多学科评估.

主要成果:

该RFC模型在预测两年生存率方面取得了高准确性 (92.6%),灵敏度为96.7%和特异性为67.1%.
生存的关键预测因素包括无氧值,手术前血红蛋白,最大O2消耗量,BMI,风险类别和FEV1/FVC比率.
在ROC曲线下的面积为0.88,表明强大的预测性能.

结论:

使用随时可用的临床数据的RFC可以有效地预测选择性AAA修复后两年内生存率.
这种预测工具可以增强接受选择性AAA修复的患者的风险益处评估.

关键词:

腹腔大动脉动脉瘤是什么机器学习机器学习死亡率预测死亡率预测随机森林分类随机森林分类

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