Atrial Fibrillation Treatment Stratification Based on Artificial Intelligence‐Driven Analysis of the Electrophysiological Complexity

• Published in: Journal of cardiovascular electrophysiology Vol. 36; no. 8; pp. 1903 - 1912
• Main Authors: Nava, Ana María Sánchez, Ros, Santiago, Carta, Alejandro, González‐Torrecilla, Esteban, Mansilla, Ana González, Bermejo, Javier, Arenal, Ángel, Climent, Andreu M., Guillem, María S., Atienza, Felipe
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.08.2025; John Wiley and Sons Inc
• Subjects: Ablation; Action Potentials; Aged; Algorithms; Anti-Arrhythmia Agents - adverse effects; Anti-Arrhythmia Agents - therapeutic use; Artificial Intelligence; atrial fibrillation; Atrial Fibrillation - diagnosis; Atrial Fibrillation - physiopathology; Atrial Fibrillation - therapy; Biomarkers; Cardiac arrhythmia; Catheter Ablation - adverse effects; Classification; Clinical Decision-Making; Cluster analysis; Decision Support Techniques; ECGI; Electrocardiography; Electrophysiologic Techniques, Cardiac; Female; Fibrillation; Heart Rate - drug effects; Humans; Male; Middle Aged; Original; Patients; Predictions; Predictive Value of Tests; Signal Processing, Computer-Assisted; stratification; Time Factors; Treatment Outcome; ECGI; atrial fibrillation; stratification; artificial intelligence
• ISSN: 1045-3873; 1540-8167; 1540-8167
• DOI: 10.1111/jce.16754

ABSTRACT Background Atrial Fibrillation (AF) treatment strategies are suboptimal and clinical predictors of success are limited. Artificial Intelligence (AI) has arisen as a powerful tool for treatment efficacy prediction. Objective We developed an AI‐driven platform for the stratification of patients based on noninvasive Electrocardiographic Imaging (ECGI) biomarkers and clinical parameters to evaluate and predict optimal patient treatment. Methods We evaluated 204 patients treated according to clinical guidelines and characterized them at the electrophysiological level using ECGI recordings during AF. ECGI signals were calculated to obtain frequency and rotational biomarkers. Baseline clinical characteristics and treatment after inclusion were registered. Results A clustering algorithm was calibrated taking three different variables for 1 year outcome prediction: (1) AF type (paroxysmal or persistent); (2) ECGI complexity score (calculated based on highest dominant frequency, median dominant frequency, and mean rotor time); and (3) type of treatment: rhythm control (drugs, AF ablation) or rate control. The cluster analysis classified patients into five groups: Low electrophysiological complexity patterns were associated with an improved outcome after ablation, regardless of the time duration of the AF. Intermediate complexity scores in paroxysmal AF had a favourable outcome with rhythm control treatments, but not in persistent AF patients. Cluster patterns with higher electrophysiological complexity were associated with a higher probability of AF recurrence, both in paroxysmal and persistent groups. The performance of the algorithm predicting the outcome was (AUC: 0.73 (0.63–0.81)), increasing overall performance with respect to conventional persistent and paroxysmal classification (AUC: 0.58 (0.48–0.68); p < 0.05). This algorithm was evaluated on the 20% test set, obtaining 90% prediction success. Conclusions AI‐driven analysis that combined clinical information with ECGI biomarkers increased the performance of conventional classification methods for AF treatment stratification.