Machine Learning-Predicted Progression to Permanent Atrial Fibrillation After Catheter Ablation

Frontiers in Cardiovascular Medicine

article

Je-Wook Park¹  Oh-Seok Kwon¹  Jaemin Shim²  Inseok Hwang¹  Yun Gi Kim²  Hee Tae Yu¹  Tae-Hoon Kim¹  Jae-Sun Uhm¹  Jong-Youn Kim¹  Jong Il Choi²  Boyoung Joung¹  Moon-Hyoung Lee¹  Young-Hoon Kim²  Hui-Nam Pak¹ 

[1] Division of Cardiology, Yonsei University Health System;Department of Internal Medicine, Korea University Cardiovascular Center

关键词: atrial fibrillation;    catheter ablation;    progression;    machine learning;    risk score;   

DOI  :  10.3389/fcvm.2022.813914

学科分类：地球科学(综合)

来源: Frontiers

【 摘 要 】

Introduction We developed a prediction model for atrial fibrillation (AF) progression and tested whether machine learning (ML) could reproduce the prediction power in an independent cohort using pre-procedural non-invasive variables alone. Methods Cohort 1 included 1,214 patients and cohort 2, 658, and all underwent AF catheter ablation (AFCA). AF progression to permanent AF was defined as sustained AF despite repeat AFCA or cardioversion under antiarrhythmic drugs. We developed a risk stratification model for AF progression (STAAR score) and stratified cohort 1 into three groups. We also developed an ML-prediction model to classify three STAAR risk groups without invasive parameters and validated the risk score in cohort 2. Results The STAAR score consisted of a stroke (2 points, p = 0.003), persistent AF (1 point, p = 0.049), left atrial (LA) dimension ≥43 mm (1 point, p = 0.010), LA voltage <1.109 mV (2 points, p = 0.004), and PR interval ≥196 ms (1 point, p = 0.001), based on multivariate Cox analyses, and it had a good discriminative power for progression to permanent AF [area under curve (AUC) 0.796, 95% confidence interval (CI): 0.753–0.838]. The ML prediction model calculated the risk for AF progression without invasive variables and achieved excellent risk stratification: AUC 0.935 for low-risk groups (score = 0), AUC 0.855 for intermediate-risk groups (score 1–3), and AUC 0.965 for high-risk groups (score ≥ 4) in cohort 1. The ML model successfully predicted the high-risk group for AF progression in cohort 2 (log-rank p < 0.001). Conclusions The ML-prediction model successfully classified the high-risk patients who will progress to permanent AF after AFCA without invasive variables but has a limited discrimination power for the intermediate-risk group.

【 授权许可】

CC BY