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INTRODUCTION: Pulmonary vein isolation (PVI) is well established as a primary treatment for atrial fibrillation (AF). The POLAR ICE study was designed to collect prospective real world data on the safety and effectiveness of the POLARxTM cryoballoon for PVI to treat paroxysmal AF. METHODS: POLAR ICE, a prospective, non-randomized, multicenter (international) registry (NCT04250714), enrolled 399 patients across 19 European centers. Procedural characteristics, such as time to isolation, cryoablations per pulmonary vein (PV), balloon nadir temperature, and occlusion grade were recorded. PVI was confirmed with entrance block testing. RESULTS: Data on 372 de novo PVI procedures (n = 2190 ablations) were collected. Complete PVI was achieved in 96.8% of PVs. Procedure and fluoroscopy times were 68.2 ± 24.6 and 15.6 ± 9.6 min, respectively. Left atrial dwell time was 46.6 ± 18.3 min. Grade 3 or 4 occlusion was achieved in 98.2% of PVs reported and 71.2% of PVs isolation required only a single cryoablation. Of 2190 cryoapplications, 83% had a duration of at least 120 s; nadir temperature of these ablations averaged -56.3 ± 6.5°C. There were 6 phrenic nerve palsy events, 2 of which resolved within 3 months of the procedure. CONCLUSION: This real-world usage data on a novel cryoballoon suggests this device is effective, safe, and relatively fast in centers with cryoballoon experience. These data are comparable to prior POLARx reports and in keeping with reported data on other cryoballoons. Future studies should examine the long-term outcomes and the relationship between biophysical parameters and outcomes for this novel cryoballoon.

Background: Atrial fibrillation (AF) is the most common supraventricular arrhythmia, characterized by disorganized atrial electrical activity, maintained by localized arrhythmogenic atrial drivers. Pulmonary vein isolation (PVI) allows to exclude PV-related drivers. However, PVI is less effective in patients with additional extra-PV arrhythmogenic drivers. Objectives: To discriminate whether AF drivers are located near the PVs vs extra-PV regions using the noninvasive 12-lead electrocardiogram (ECG) in a computational and clinical framework, and to computationally predict the acute success of PVI in these cohorts of data. Methods: AF drivers were induced in 2 computerized atrial models and combined with 8 torso models, resulting in 1128 12-lead ECGs (80 ECGs with AF drivers located in the PVs and 1048 in extra-PV areas). A total of 103 features were extracted from the signals. Binary decision tree classifier was trained on the simulated data and evaluated using hold-out cross-validation. The PVs were subsequently isolated in the models to assess PVI success. Finally, the classifier was tested on a clinical dataset (46 patients: 23 PV-dependent AF and 23 with additional extra-PV sources). Results: The classifier yielded 82.6% specificity and 73.9% sensitivity for detecting PV drivers on the clinical data. Consistency analysis on the 46 patients resulted in 93.5% results match. Applying PVI on the simulated AF cases terminated AF in 100% of the cases in the PV class. Conclusion: Machine learning–based classification of 12-lead-ECG allows discrimination between patients with PV drivers vs those with extra-PV drivers of AF. The novel algorithm may aid to identify patients with high acute success rates to PVI.