Machine learning for outcome prediction of atrial fibrillation recurrence after catheter ablation - Knecht, S; Cyriac, J; Badertscher, P; Krisai, P; Schlageter, V; Osswald, S; Zellweger, M; Kuhne, M; Sticherling, C (Europace (London, England), 2024)
Abstract Background Outcome prediction after catheter ablation for atrial fibrillation (AF) based on electronic health records (EHR) using machine learning (ML) is not yet established. Purpose The aim of the study was to assess the value of ML methods to predict the risk of AF recurrence after catheter ablation based on easily accessible EHR. Methods We analyzed 1362 patients from our prospective registry. Only patients undergoing a first AF ablation were studied. Follow-up was performed at 3, 6 and 12 months after the ablation including 24-hour and 7-day Holter electrocardiogram (ECG). Four different analyses with increasing complexity out of a set of overall 22 features using seven simple and three ensemble method ML algorithms were performed: model 1 (including age, sex, height, weight, and established anamnestic risk factors: AF-type, coronary artery disease, myocardial infarction, stroke, heart failure, hypertension, current smoker, diabetes, renal insufficiency), model 2: model 1 plus additional AF-history features (duration of AF in months, previous typical atrial flutter, number of previous electrical cardioversions, number of failed antiarrhythmic drugs), model 3: model 2 plus simple echocardiographic parameters (left ventricular ejection fraction, LA diameter), model 4: model 3 plus biomarkers (CRP, creatinine, NTproBNP). To evaluate the performance of the prediction, we report C-statistics, accuracy, and recall (sensitivity). The C-statistics of five risk scores for AF recurrence (APPLE, DR-FLASH, BASE-AF2, ATLAS, CHA2DS2-Vasc) and a logistic regression with a forward selection were calculated for comparison. Results Of the 1362 patients, 996 (73%) were male, mean age was 62±10 years and 60% presented with paroxysmal AF. Recurrence of AF during 1-year follow-up was observed in 458 patients (34%). The results for the four models using the eleven different ML algorithms are summarized in the Figure. In detail, the C-statistics were modest with a maximal value of 0.602 for the random forest (RF) classifier for the most complex model 4. The performance for the risk scores was maximal for the APPLE score (C-statistics 0.572) and comparable for the forward-selection logistic regression including five features with a C-statistics of 0.607 (95% CI 0.573-0.641). Conclusion In conclusion, outcome prediction of AF recurrence after catheter ablation with easily accessible EHR data remains challenging and cannot be improved simply by applying machine learning methods. Whether a more comprehensive feature selection, such as more complex ECG parameters instead of the selection of easily available features from the EHR, a combination with deep neural network-based feature extraction, or unsupervised ML might improve the result in a clinically relevant way needs further investigation. Results of the eleven ML algorithms