基于机器学习的瓣膜病心房颤动射频消融术后复发预测及风险因素分析

Prediction and risk factors of recurrence of atrial fibrillation in patients with valvular diseases after radiofrequency ablation based on machine learning

目的 通过机器学习技术对心房颤动(房颤)射频消融术后房颤复发进行预测,并试图寻找影响术后房颤复发的风险因素。方法 纳入2017年1月—2021年1月因瓣膜病房颤在四川大学华西医院及其分院(上锦分院)进行射频消融手术的300例患者,其中男129例、女171例,平均年龄52.56岁。建立5个机器学习模型对房颤复发进行预测,将其中3个表现最好的模型组合成一个投票分类器,再次进行预测。最后使用SHApley Additive exPlanations方法进行风险因素分析。结果 投票分类器得到的预测准确率为75.0%,召回率为61.0%,受试者工作特征曲线下面积为0.79。此外,还发现左心房内径、射血分数、右心房内径等因素对术后房颤复发存在影响。结论 基于机器学习的瓣膜病房颤射频消融术后复发预测可为房颤临床诊治提供一定参考,减少因无效消融给患者带来的风险;根据研究中发现的风险因素,可为患者提供精准的治疗。

Objective To use machine learning technology to predict the recurrence of atrial fibrillation(AF) after radiofrequency ablation, and try to find the risk factors affecting postoperative recurrence. Methods A total of 300 patients with valvular AF who underwent radiofrequency ablation in West China Hospital and its branch(Shangjin Hospital) from January 2017 to January 2021 were enrolled, including 129 males and 171 females with a mean age of 52.56years. We built 5 machine learning models to predict AF recurrence, combined the 3 best performing models into a voting classifier, and made prediction again. Finally, risk factor analysis was performed using the SHApley Additive exPlanations method. Results The voting classifier yielded a prediction accuracy rate of 75.0%, a recall rate of 61.0%, and an area under the receiver operating characteristic curve of 0.79. In addition, factors such as left atrial diameter, ejection fraction,and right atrial diameter were found to have an influence on postoperative recurrence. Conclusion Machine learningbased prediction of recurrence of valvular AF after radiofrequency ablation can provide a certain reference for the clinical diagnosis of AF, and reduce the risk to patients due to ineffective ablation. According to the risk factors found in the study,it can provide patients with more personalized treatment.