Background and Aims: In 15-40% of patients undergoing repeat ablation for AF recurrence, all pulmonary veins (PVs) are durably isolated. Currently, there is limited evidence on the appropriate treatment strategy for these patients. Our aim was to characterize and compare the effectiveness of different re-ablation strategies. Methods: All patients referred for repeat AF ablation with all PVs durably isolated at 8 hospitals in the Netherlands were included [Netherlands-Heart-Registration (NHR); 2016-2019]. NHR data was used to determine the presence of PV-reconnection, ablation strategy used, and the outcome of ablation (atrial arrhythmia recurrence > 30 sec.). Effectiveness of ablation strategies were assessed with multivariable Cox models. Results: Of 2311 repeat AF ablations performed, 274 (11.9%) patients had all PVs durably isolated. Median age was 66 (IQR:58-70) years, 44.2% women, 45.6% had persistent/long-standing-persistent AF. In 33 (12.0%) patients no ablation was performed. Single ablation strategy was performed most often (41.2%). Posterior wall ablation (58.4%) was performed most often, followed by PV-antralization (26.3%). Over 2.0 (1.0-3.3) years, 147 (59.8%) patients had an atrial arrhythmia recurrence and 30 (12.7%) patients had another repeat AF ablation within 1 year. After multivariable adjustment, no difference in atrial-arrhythmia recurrences was detected between individual ablation strategies, number of strategies performed, and type of atrial-arrhythmia (p>0.05 for all). Left-atrial-size was associated with a higher recurrence-risk [aHR 1.03(95%CI 1.01-1.05)]. Conclusion: In patients with durably isolated PVs, a high proportion experienced recurrence of atrial-arrhythmias, with no difference in recurrence rates between different re-ablation strategies.

Objective The aim of the present study was to develop a neural network to characterize the effect of aging on the ECG in healthy volunteers. Moreover, the impact of the various ECG features on aging was evaluated. Methods & Results A total of 6228 healthy subjects without structural heart disease were included in this study. A neural network regression model was created to predict age of the subjects based on their ECG; 577 parameters derived from a 12-lead ECG of each subject were used to develop and validate the neural network; A ten fold cross-validation was performed, using 118 subjects for validation eacht fold. Using SHapley Additive exPlanations values the impact of the individual features on the prediction of age was determined. Of 6228 subjects tested, 1808 (29%) were females and mean age was 34 years, range 18 – 75 years. Physiologic age was estimated as a continuous variable with an average error of 6.9±5.6 years (R2= 0.72 ± 0.04) . The correlation was slightly stronger for men (R2= 0.74) than for women (R2= 0.66). The most important features on the prediction of physiologic age were T wave morphology indices in leads V4 and V5, and P wave amplitude in leads AVR and II. Conclusion The application of artificial intelligence to the ECG using a neural network regression model, allows accurate estimation of physiologic cardiac age. This technique could be used to pick up subtle age-related cardiac changes, but also estimate the reversing of these age-associated effects by administered treatments.