RT Journal Article
T1 Atrial Fibrillation Treatment Stratification Based on Artificial Intelligence-Driven Analysis of the Electrophysiological Complexity
A1 Sánchez de la Nava, Ana María
A1 Ros, Santiago
A1 Carta, Alejandro
A1 González Torrecilla, Esteban
A1 González Mansilla, Ana
A1 Bermejo Thomas, Francisco Javier
A1 Arenal, Ángel
A1 Climent, Andreu M.
A1 Guillén, María S.
A1 Atienza, Felipe
A1 Atienza Fernández, Felipe
AB 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.
PB Wiley
SN 1045-3873
YR 2025
FD 2025-06-04
LK https://hdl.handle.net/20.500.14352/132969
UL https://hdl.handle.net/20.500.14352/132969
LA eng
NO de la Nava, A. M. S., Ros, S., Carta, A., González-Torrecilla, E., Mansilla, A. G., Bermejo, J., Arenal, Á., Climent, A. M., Guillem, M. S., Atienza, F., & Other Members of the STRATIFY Study Group (2025). Atrial Fibrillation Treatment Stratification Based on Artificial Intelligence-Driven Analysis of the Electrophysiological Complexity. Journal of cardiovascular electrophysiology, 36(8), 1903–1912. https://doi.org/10.1111/jce.16754
NO 2025 Acuerdos transformativos CRUE
NO Instituto de Salud Carlos III
NO Centro de Investigación Biomédica en Red Enfermedades Cardiovasculares
NO Ministerio de Ciencia e Innovación (España)
NO Unión Europea
DS Docta Complutense
RD 15 abr 2026