Journal
JOURNAL OF CARDIOVASCULAR ELECTROPHYSIOLOGY
Volume 29, Issue 12, Pages 1624-1634Publisher
WILEY
DOI: 10.1111/jce.13723
Keywords
atrial fibrillation; autonomic nervous system; ganglionated plexus
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Funding
- British Cardiac Trust
- British Heart Foundation [FS/13/73/30352]
- Coronary Flow Trust
- MRC [MC_PC_16046, G1000326] Funding Source: UKRI
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Introduction: The ganglionated plexuses (GPs) of the intrinsic cardiac autonomic system are implicated in arrhythmogenesis. GP localization by stimulation of the epicardial fat pads to produce atrioventricular dissociating (AVD) effects is well described. We determined the anatomical distribution of the left atrial GPs that influence atrioventricular (AV) dissociation. Methods and Results: High frequency stimulation was delivered through a Smart - Touch catheter in the left atrium of patients undergoing atrial fibrillation (AF) ablation. Three dimensional locations of points tested throughout the entire chamber were recorded on the CARTO (TM) system. Impact on the AV conduction was categorized as ventricular asystole, bradycardia, or no effect. CARTO maps were exported, registered, and transformed onto a reference left atrial geometry using a custom software, enabling data from multiple patients to be overlaid. In 28 patients, 2108 locations were tested and 283 sites (13%) demonstrated (AVD - GP) effects. There were 10 AVD - GPs (interquartile range, 11.5) per patient. Eighty percent (226) produced asystole and 20% (57) showed bradycardia. The distribution of the two groups was very similar. Highest probability of AVD - GPs (> 20%) was identified in: inferoseptal portion (41%) and right inferior pulmonary vein base (30%) of the posterior wall, right superior pulmonary vein antrum (31%). Conclusion: It is feasible to map the entire left atrium for AVD - GPs before AF ablation. Aggregated data from multiple patients, producing a distribution probability atlas of AVD - GPs, identified three regions with a higher likelihood for finding AVD - GPs and these matched the histological descriptions. This approach could be used to better characterize the autonomic network.
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