Predicting atrial fibrillation recurrence after catheter ablation using an artificial intelligence-enabled electrocardiogram algorithm

Abstract

Abstract Introduction Predicting atrial fibrillation (AF) recurrence post catheter ablation may help to assess procedural eligibility and determine AF management. While several predictors of AF recurrence post ablation have been established and numerous clinical risk scores have been proposed, their performance remained underwhelming and clinical utility was limited. Hence, new predictors are needed. Artificial intelligence (AI) algorithms using deep neural networks (DNN) to analyze biometrical data might generate such predictors. DNN algorithms have been developed to identify patients with AF based on a 12-lead electrocardiogram (ECG) in sinus rhythm. Whether these algorithms can function as a predictor of AF recurrence after AF ablation remains unknown. Purpose To evaluate the prediction of AF recurrence after ablation using an AI-enabled ECG algorithm trained to predict AF on an ECG in sinus rhythm. Methods This study retrospectively analyzed observational data from the DIGITOTAL study, that monitored AF recurrence with a PPG-based smartphone application in 96 subjects after AF ablation. Patients with a 12-lead ECG in SR available within a timeframe of 3 months before the ablation procedure were included in the analysis. Although all patients had a history of AF, an AF-risk score was calculated by the DNN described elsewhere.1 Results An ECG in sinus rhythm was available in 53 patients (14 women [26.4%]; mean [SD] age, 62.0 [9.7] years) out of the 96 patients followed-up in the DIGITOTAL study. Testing the DNN on the last ECG before the ablation procedure resulted in an area under the receiver operating curve (AUC) of 0.65 (95% CI, 0.49 - 0.80), and an area under the precision recall curve (AUPRC) of 0.56 (95% CI, 0.34 - 0.78). The optimal cutoff score resulted in a sensitivity of 60.0% (95% CI, 36.1% - 80.9%), specificity of 66.7% 66.7% (95% CI, 48.2% - 82.0%), accuracy of 0.64 (95% CI, 0.50 - 0.77), F1-score of 55.8% (95% CI, 38.5% - 74.4%), positive predictive value 52.2% (95% CI, 30.6% - 73.2%) and negative predictive value 73.3% (95% CI, 54.1% - 87.7%). Patients classified in the high-risk group versus low-risk group were more likely to exhibit AF recurrence up to one year after AF ablation (hazard ratio, 2.6; 95% CI, 1.1 - 6.5; P-value = 0.037). Conclusions The AI-enabled ECG algorithm, trained to predict AF on a sinus rhythm ECG, was able to predict AF recurrence after ablation with an accuracy comparable to the existing clinical risk scores. Further studies are needed to determine whether the DNN score can be used as an independent predictor and improve existing risk scores.