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Volume 14
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10.3390/jcm14228248
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
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,* and
Gaetano Senatore
2
1
Department of Advanced Biomedical Sciences, Division of Cardiology, University of Naples Federico II, 80131 Naples, Italy
2
Ciriè Hospital, 10073 Ciriè, Italy
3
Department of Medicine, University of Naples Federico II, 80131 Naples, Italy
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2025, 14(22), 8248; https://doi.org/10.3390/jcm14228248
Submission received: 17 October 2025 / Revised: 13 November 2025 / Accepted: 18 November 2025 / Published: 20 November 2025
(This article belongs to the Section Cardiology)
Versions Notes

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

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MDPI and ACS Style

De Rosa, G.; Giuggia, M.; Peyracchia, M.; Peddis, M.; Di Summa, R.; Pelissero, E.; Trapani, G.; De Los Rios, D.; Ugliano, F.; Cirillo, P.; et al. Artificial Intelligence-Based Evaluation of Post-Procedural Electrocardiographic Parameters to Identify Patients at Risk of Atrial Fibrillation Recurrence After Transcatheter Ablation. J. Clin. Med. 2025, 14, 8248. https://doi.org/10.3390/jcm14228248

AMA Style

De Rosa G, Giuggia M, Peyracchia M, Peddis M, Di Summa R, Pelissero E, Trapani G, De Los Rios D, Ugliano F, Cirillo P, et al. Artificial Intelligence-Based Evaluation of Post-Procedural Electrocardiographic Parameters to Identify Patients at Risk of Atrial Fibrillation Recurrence After Transcatheter Ablation. Journal of Clinical Medicine. 2025; 14(22):8248. https://doi.org/10.3390/jcm14228248

Chicago/Turabian Style

De Rosa, Gennaro, Marco Giuggia, Mattia Peyracchia, Martina Peddis, Roberto Di Summa, Elisa Pelissero, Giuseppe Trapani, Davide De Los Rios, Fabio Ugliano, Plinio Cirillo, and et al. 2025. "Artificial Intelligence-Based Evaluation of Post-Procedural Electrocardiographic Parameters to Identify Patients at Risk of Atrial Fibrillation Recurrence After Transcatheter Ablation" Journal of Clinical Medicine 14, no. 22: 8248. https://doi.org/10.3390/jcm14228248

APA Style

De Rosa, G., Giuggia, M., Peyracchia, M., Peddis, M., Di Summa, R., Pelissero, E., Trapani, G., De Los Rios, D., Ugliano, F., Cirillo, P., & Senatore, G. (2025). Artificial Intelligence-Based Evaluation of Post-Procedural Electrocardiographic Parameters to Identify Patients at Risk of Atrial Fibrillation Recurrence After Transcatheter Ablation. Journal of Clinical Medicine, 14(22), 8248. https://doi.org/10.3390/jcm14228248

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