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. 2025 Nov 20;14(22):8248.
doi: 10.3390/jcm14228248.

Artificial Intelligence-Based Evaluation of Post-Procedural Electrocardiographic Parameters to Identify Patients at Risk of Atrial Fibrillation Recurrence After Transcatheter Ablation

Gennaro De Rosa  1 , Marco Giuggia  2 , Mattia Peyracchia  2 , Martina Peddis  2 , Roberto Di Summa  2 , Elisa Pelissero  2 , Giuseppe Trapani  2 , Davide De Los Rios  3 , Fabio Ugliano  3 , Plinio Cirillo  1 , Gaetano Senatore  2
Affiliations

Artificial Intelligence-Based Evaluation of Post-Procedural Electrocardiographic Parameters to Identify Patients at Risk of Atrial Fibrillation Recurrence After Transcatheter Ablation

Gennaro De Rosa et al. J Clin Med. .

Abstract

Background/Objectives: Arrhythmic recurrence is a common issue affecting a significant percentage of patients undergoing transcatheter ablation (TCA) of Atrial Fibrillation (AF). The use of artificial intelligence (AI) for the identification of electrocardiographic predictors of post-ablation recurrence may offer a valuable and cost-effective approach to improve risk stratification and optimize follow-up. This study aims to investigate the relationship between post-procedural electrocardiographic (ECG) P-wave parameters, measured using AI, and AF recurrence in patients undergoing transcatheter ablation (TCA). Methods: Seventy-four patients (age 62.36 ± 10.4 years) with a diagnosis of AF were retrospectively analyzed. ECGs were processed using AI software to analyze P-wave-related variables. All patients had either an implantable loop recorder (ILR) or another form of cardiac implantable electronic device (CIED). Results: Post-procedural P-wave amplitude in lead II (PwA in lead II) showed a significant association with AF recurrence, defined as an average arrhythmic burden >6% at one-year follow-up. Conclusions: These findings underscore the potential of PwA in lead II as a biomarker for the follow-up of patients undergoing TCA and highlight the contribution of AI in the analysis of electrocardiographic parameters predictive of AF recurrence. Together, these results may contribute to the development of early risk-stratification strategies following catheter ablation.

Keywords: P-wave; artificial intelligence; atrial fibrillation; catheter ablation; electrocardiography; recurrence prediction; risk stratification.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Incremental effect of P-wave amplitude in lead DII on AF recurrence.
Figure 2
Figure 2
Boxplot illustrating P-wave amplitude in lead II stratified by AF burden.
See this image and copyright information in PMC

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