Analysis of drug-induced and spontaneous cardioversions reveals similar patterns leading to termination of atrial fibrillation

Front Physiol. 2024 Jul 18:15:1399037. doi: 10.3389/fphys.2024.1399037. eCollection 2024.

Abstract

Introduction: The mechanisms leading to the conversion of atrial fibrillation (AF) to sinus rhythm are poorly understood. This study describes the dynamic behavior of electrophysiological parameters and conduction patterns leading to spontaneous and pharmacological AF termination.

Methods: Five independent groups of goats were investigated: (1) spontaneous termination of AF, and drug-induced terminations of AF by various potassium channel inhibitors: (2) AP14145, (3) PA-6, (4) XAF-1407, and (5) vernakalant. Bi-atrial contact mapping was performed during an open chest surgery and intervals with continuous and discrete atrial activity were determined. AF cycle length (AFCL), conduction velocity and path length were calculated for each interval, and the final conduction pattern preceding AF termination was evaluated.

Results: AF termination was preceded by a sudden episode of discrete activity both in the presence and absence of an antiarrhythmic drug. This episode was accompanied by substantial increases in AFCL and conduction velocity, resulting in prolongation of path length. In 77% ± 4% of all terminations the conduction pattern preceding AF termination involved medial to lateral conduction along Bachmann's bundle into both atria, followed by anterior to posterior conduction. This finding suggests conduction block in the interatrial septum and/or pulmonary vein area as final step of AF termination.

Conclusion: AF termination is preceded by an increased organization of fibrillatory conduction. The termination itself is a sudden process with a critical role for the interplay between spatiotemporal organization and anatomical structure.

Keywords: Bachmann’s bundle; antiarrhythmic drugs; atrial anatomy; atrial fibrillation; cardioversion.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This project received funding from the European Union’s Horizon 2020 research and innovation program (AFib-TrainNet: EU Training Network on Novel Targets and Methods in Atrial Fibrillation, No. 675351; MAESTRIA: Machine Learning Artificial Intelligence Early Detection Stroke Atrial Fibrillation, grant number 965286), from the Netherlands Heart Foundation (CVON 2014–09, RACE V Reappraisal of Atrial Fibrillation: Interaction between hyperCoagulability, Electrical remodeling, and Vascular Destabilization in the Progression of Atrial Fibrillation), from the French National Research Agency (grant number ANR-10-IAHU-04) and from Fondation Lefoulon-Delalande.