基于机器学习的慢性瓣膜病合并心房颤动患者行Cox迷宫IV手术后心房颤动复发风险预测模型

蒋泽楠; 宋珑; 梁春水; 张昊; 刘立明

doi:10.11817/j.issn.1672-7347.2023.230018

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基于机器学习的慢性瓣膜病合并心房颤动患者行Cox迷宫IV手术后心房颤动复发风险预测模型

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论著 | 更新时间：2023-09-19

基于机器学习的慢性瓣膜病合并心房颤动患者行Cox迷宫IV手术后心房颤动复发风险预测模型
Prediction model of atrial fibrillation recurrence after Cox-Maze IV procedure in patients with chronic valvular disease and atrial fibrillation based on machine learning algorithm
中南大学学报(医学版) 中南大学学报(医学版) 2023年48卷第7期页码：995-1007
作者机构：

1.中南大学湘雅二医院心血管外科，长沙 410011
2.陆军军医大学新桥医院心血管外科，重庆 400037
作者简介：

蒋泽楠，Email: mapledoctor@csu.edu.cn, ORCID: 0000-0003-4443-5543
刘立明，Email: liulimingjia@csu.edu.cn, ORCID: 0000-0001-9745-0283
基金信息：

国家重点研发计划(2018YFC1311204)
DOI：10.11817/j.issn.1672-7347.2023.230018
中图分类号：
CSTR：

蒋泽楠, 宋珑, 梁春水, 等. 基于机器学习的慢性瓣膜病合并心房颤动患者行Cox迷宫IV手术后心房颤动复发风险预测模型[J]. 中南大学学报(医学版), 2023,48(7):995-1007.

JIANG Zenan, SONG Long, LIANG Chunshui, et al. Prediction model of atrial fibrillation recurrence after Cox-Maze IV procedure in patients with chronic valvular disease and atrial fibrillation based on machine learning algorithm[J]. Journal of Central South University. Medical Science, 2023,48(7):995-1007.
蒋泽楠, 宋珑, 梁春水, 等. 基于机器学习的慢性瓣膜病合并心房颤动患者行Cox迷宫IV手术后心房颤动复发风险预测模型[J]. 中南大学学报(医学版), 2023,48(7):995-1007. DOI： 10.11817/j.issn.1672-7347.2023.230018.

JIANG Zenan, SONG Long, LIANG Chunshui, et al. Prediction model of atrial fibrillation recurrence after Cox-Maze IV procedure in patients with chronic valvular disease and atrial fibrillation based on machine learning algorithm[J]. Journal of Central South University. Medical Science, 2023,48(7):995-1007. DOI： 10.11817/j.issn.1672-7347.2023.230018.

摘要

目的,2,心房颤动(以下简称“房颤”)是一种常见的心律失常，Cox迷宫IV手术是外科治疗房颤的常用手术方法，目前Cox迷宫IV手术后患者房颤复发的风险因素尚不明确。近年来，机器学习算法在提高诊断准确率、预测患者预后和个性化治疗策略方面显示出巨大潜力。本研究旨在评估Cox迷宫IV手术治疗慢性瓣膜病合并心房颤动患者的疗效，使用机器学习算法识别心房颤动复发的潜在风险因素，构建Cox迷宫IV手术后房颤复发预测模型。,方法,2,回顾性纳入2012年1月至2019年12月中南大学湘雅二医院和陆军军医大学附属新桥医院符合条件的慢性瓣膜病合并房颤且行瓣膜手术合并Cox迷宫IV手术患者555例，年龄为(57.95±7.96)岁，根据患者术后房颤复发情况分为房颤复发组(,n,=117)和房颤未复发组(,n,=438)。采用Kaplan-Meier法分析窦性心律维持率，构建9个机器学习模型，包括随机森林、梯度提升决策树(gradient boosting decision tree，GBDT)、极限梯度提升(extreme gradient boosting，XGBoost)、引导聚集算法、logistic回归、类别提升(categorical boosting，CatBoost)、支持向量机、自适应增强和多层感知机。使用五折交叉验证和模型评估指标评估模型性能，评估指标包括准确度、精确度、召回率、F1分数和曲线下面积(area under the curve，AUC)，筛选出2个表现最佳的模型进行进一步分析[包括特征重要性和沙普利加和解释(Shapley additive explanations，SHAP)]来识别房颤复发风险因素，以此构建房颤复发风险预测模型。,结果,2,患者术后5年窦性心律维持率为82.13%(95% ,CI ,78.51%~85.93%)。9个机器学习模型中，XGBoost和CatBoost模型表现最好，AUC分别为0.768(95% ,CI, 0.742~0.786)和0.762(95% ,CI, 0.723~0.801)，且在9个模型中有较高的准确率、精确率、召回率和F1值。特征重要性和SHAP分析显示房颤病史时长、术前左室射血分数、术后心律、术前左心房内径、术前中性粒细胞与淋巴细胞比值、术前心率和术前白细胞计数等是房颤复发的重要因素。,结论,2,Cox迷宫IV手术治疗房颤具有良好的窦性心律维持率，本研究通过机器学习算法成功识别多种Cox迷宫IV手术后房颤复发风险因素，成功构建2个房颤复发风险预测模型，可能有助于临床决策和优化房颤的个体化手术管理。

Abstract

Objective,2,Atrial fibrillation (AF) is a prevalent cardiac arrhythmia, and Cox-maze IV procedure (CMP-IV) is a commonly employed surgical technique for its treatment. Currently, the risk factors for atrial fibrillation recurrence following CMP-IV remain relatively unclear. In recent years, machine learning algorithms have demonstrated immense potential in enhancing diagnostic accuracy, predicting patient outcomes, and devising personalized treatment strategies. This study aims to evaluate the efficacy of CMP-IV on treating chronic valvular disease with AF, utilize machine learning algorithms to identify potential risk factors for AF recurrence, construct a CMP-IV postoperative AF recurrence prediction model.,Methods,2,A total of 555 patients with AF combined with chronic valvular disease, who met the criteria, were enrolled from January 2012 to December 2019 from the Second Xiangya Hospital of Central South University and the Affiliated Xinqiao Hospital of the Army Medical University, with an average age of (57.95±7.96) years, including an AF recurrence group (,n,=117) and an AF non-recurrence group (,n,=438). Kaplan-Meier method was used to analyze the sinus rhythm maintenance rate, and 9 machine learning models were developed including random forest, gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), bootstrap aggregating, logistic regression, categorical boosting (CatBoost), support vector machine, adaptive boosting, and multi-layer perceptron. Five-fold cross-validation and model evaluation indicators [including F1 score, accuracy, precision, recall, and area under the curve (AUC)] were used to evaluate the performance of the models. The 2 best-performing models were selected for further analyze, including feature importance evaluation and Shapley additive explanations (SHAP) analysis, identifying AF recurrence risk factors, and building an AF recurrence risk prediction model.,Results,2,The 5-year sinus rhythm maintenance rate for the patients was 82.13% (95% ,CI, 78.51% to 85.93%). Among the 9 machine learning models, XGBoost and CatBoost models performed best, with the AUC of 0.768 (95% ,CI, 0.742 to 0.786) and 0.762 (95% ,CI, 0.723 to 0.801), respectively. Feature importance and SHAP analysis showed that duration of AF, preoperative left ventricular ejection fraction, postoperative heart rhythm, preoperative neutrophil-to-lymphocyte ratio, preoperative left atrial diameter, preoperative heart rate, and preoperative white blood cell were important factors for AF recurrence. ,Conclusion:, Machine learning algorithms can be effectively used to identify potential risk factors for AF recurrence after CMP-IV. This study successfuly constructs 2 prediction model which may enhance individualized treatment plans.

关键词

Cox迷宫IV手术心房颤动机器学习风险因素预测模型

Keywords

Cox-maze IV procedureatrial fibrillationmachine learningrisk factorsprediction model

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