机器学习模型预测开放手术修复腹主动脉瘤后急性肾损伤的发生

Machine learning model predicts the occurrence of acute kidney injury after open surgery for abdominal aortic aneurysm repair

目的:腹主动脉瘤是指腹主动脉扩张超过3.0 cm的一种病理状态,手术治疗方式包括开放手术(open surgical repair,OSR)和腔内修复(endovascular aneurysm repair,EVAR)。预测腹主动脉瘤患者OSR后急性肾损伤(acute kidney injury,AKI)的发生有助于术后临床决策。为找到一种更有效的预测方法,本研究对不同机器学习预测模型的效能进行测试。方法:回顾性收集2009年1月至2021年12月中南大学湘雅医院80例OSR患者的围手术期数据,手术均由血管外科医师实施。选择logistic回归、线性核支持向量机、高斯核支持向量机、随机森林4种常用的机器学习分类模型来实施预测。通过五重交叉验证来分析模型的性能。结果:33名患者出现AKI,4种分类模型的五重交叉验证结果表明:随机森林是预测AKI最精确的模型,曲线下面积为0.90±0.12。结论:机器学习模型可以精确预测术后AKI的发生,从而使血管外科医生能更早地处理并发症,并且可能有助于提高腹主动脉瘤OSR的临床疗效。

Objective: Abdominal aortic aneurysm is a pathological condition in which the abdominalaorta is dilated beyond 3.0 cm. The surgical options include open surgical repair (OSR) and endovascular aneurysm repair (EVAR). Prediction of acute kidney injury (AKI) after OSRis helpful for decision ‐ making during the postoperative phase. To find a more efficientmethod for making a prediction, this study aims to perform tests on the efficacy of differentmachine learning models.Methods: Perioperative data of 80 OSR patients were retrospectively collected fromJanuary 2009 to December 2021 at Xiangya Hospital, Central South University. Thevascular surgeon performed the surgical operation. Four commonly used machine learningclassification models (logistic regression, linear kernel support vector machine, Gaussiankernel support vector machine, and random forest) were chosen to predict AKI. Theefficacy of the models was validated by five‐fold cross‐validation.Results: AKI was identified in 33 patients. Five‐fold cross‐validation showed that amongthe 4 classification models, random forest was the most precise model for predicting AKI,with an area under the curve of 0.90±0.12.Conclusion: Machine learning models can precisely predict AKI during early stages aftersurgery, which allows vascular surgeons to address complications earlier and may helpimprove the clinical outcomes of OSR.