Prediction and risk factors of recurrence of atrial fibrillation in patients with valvular diseases after radiofrequency ablation based on machine learning
- VernacularTitle: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
- Author:
Huanxu SHI
1
;
Peiyu HE
1
;
Qi TONG
2
;
Zhengjie WANG
2
;
Tao LI
2
;
Yongjun QIAN
2
;
Qijun ZHAO
3
;
Fan PAN
1
Author Information
1. School of Electronic Information, Sichuan University, Chengdu, 610065, P. R. China
2. Department of Cardiovascular Surgery, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China
3. School of Computer Science (School of Software), Sichuan University, Chengdu, 610065, P. R. China
- Publication Type:Journal Article
- Keywords:
Atrial fibrillation;
machine learning;
recurrence prediction;
risk factors
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2022;29(07):840-847
- CountryChina
- Language:Chinese
-
Abstract:
bjective 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 52.56 years. 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 learning-based 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.
