Does radiofrequency ablation procedural data improve the accuracy of identifying atrial fibrillation recurrence?

• Published in: PloS one Vol. 19; no. 4; p. e0300309
• Main Authors: Peng, Mingkai, Doshi, Amit, Amos, Yariv, Tsoref, Liat, Amit, Mati, Yungher, Don, Khanna, Rahul, Coplan, Paul M
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 05.04.2024; Public Library of Science (PLoS)
• Subjects: Ablation (Surgery); Ablation Techniques; Analysis; Atrial fibrillation; Atrial Fibrillation - diagnosis; Atrial Fibrillation - surgery; Care and treatment; Catheter Ablation - methods; Complications and side effects; Humans; Machine learning; Medical records; Patient outcomes; Pulmonary Veins - surgery; Radiofrequency Ablation; Recurrence; Time Factors; Treatment Outcome; United States
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0300309

Radiofrequency ablation (RFA) using the CARTO 3D mapping system is a common approach for pulmonary vein isolation to treat atrial fibrillation (AF). Linkage between CARTO procedural data and patients' electronical health records (EHR) provides an opportunity to identify the ablation-related parameters that would predict AF recurrence. The objective of this study is to assess the incremental accuracy of RFA procedural data to predict post-ablation AF recurrence using machine learning model. Procedural data generated during RFA procedure were downloaded from CARTONET and linked to deidentified Mercy Health EHR data. Data were divided into train (70%) and test (30%) data for model development and validation. Automate machine learning (AutoML) was used to predict 1 year AF recurrence, defined as a composite of repeat ablation, electrical cardioversion, and AF hospitalization. At first, AutoML model only included Patients' demographic and clinical characteristics. Second, an AutoML model with procedural variables and demographical/clinical variables was developed. Area under receiver operating characteristic curve (AUROC) and net reclassification improvement (NRI) were used to compare model performances using test data. Among 306 patients, 67 (21.9%) patients experienced 1-year AF recurrence. AUROC increased from 0.66 to 0.78 after adding procedural data in the AutoML model based on test data. For patients with AF recurrence, NRI was 32% for model with procedural data. Nine of 10 important predictive features were CARTO procedural data. From CARTO procedural data, patients with lower contact force in right inferior site, long ablation duration, and low number of left inferior and right roof lesions had a higher risk of AF recurrence. Patients with persistent AF were more likely to have AF recurrence. The machine learning model with procedural data better predicted 1-year AF recurrence than the model without procedural data. The model could be used for identification of patients with high risk of AF recurrence post ablation.