Beat-to-beat P-wave morphological variability in patients with paroxysmal atrial fibrillation: an in silico study. (23rd November 2018)

• Record Type: Journal Article
• Authors: Pezzuto, Simone; Gharaviri, Ali; Schotten, Ulrich; Potse, Mark; Conte, Giulio; Caputo, Maria Luce; Regoli, Francois; Krause, Rolf; Auricchio, Angelo
• Is Part Of: Europace. Volume 20(2018)Supplement 3
• Page Start: iii26
• Page End: iii35
• Publication Date: 2018-11-23
• Subjects: P-wave variability -- P-wave morphology -- Paroxysmal atrial fibrillation -- Inhomogeneous conduction -- Fibrosis -- Cosine distance; Arrhythmia -- Treatment -- Periodicals; Cardiac pacing -- Periodicals; Catheter ablation -- Periodicals; Heart -- Physiology -- Periodicals; Electrophysiology -- Periodicals; 617.4120645
• DOI: 10.1093/europace/euy227
• Languages: English
• ISSNs: 1099-5129

Abstract

Abstract: Aims: P-wave beat-to-beat morphological variability can identify patients prone to paroxysmal atrial fibrillation (AF). To date, no computational study has been carried out to mechanistically explain such finding. The aim of this study was to provide a pathophysiological explanation, by using a computer model of the human atria, of the correlation between P-wave beat-to-beat variability and the risk of AF. Methods and results: A physiological variability in the earliest activation site (EAS), on a beat-to-beat basis, was introduced into a computer model of the human atria by randomizing the EAS location. A methodology for generating multi-scale, spatially-correlated regions of heterogeneous conduction was developed. P-wave variability in the presence of such regions was compared with a control case. Simulations were performed with an eikonal model, for the activation map, and with the lead field approach, for P-wave computation. The methodology was eventually compared with a reference monodomain simulation. A total of 60 P-waves were simulated for each sinus node exit location (12 in total), and for each of the 15 patterns of heterogeneous conduction automatically generated by the model. A P-wave beat-to-beat variability was observed in all cases. Variability was significantly increased in presence of heterogeneous slow conducting regions, up to two-fold the variability in the control case. P-wave variability increased non-linearly with respect to the EAS variability and total area of slow conduction. Distribution of the heterogeneous conduction was more effective in increasing the variability when it surrounded the EAS locations and the fast conducting bundles. P-waves simulated by the eikonal approach compared excellently with the monodomain-based ones. Conclusion: P-wave variability in patients with paroxysmal AF could be explained by a variability in sinoatrial node exit location in combination with slow conducting regions.