Atrial substrate identification in patients with chronic atrial fibrillation using multivariate statistical models and multiple attributes from atrial electrograms.

Abstract

Atrial fibrillation (AF) is the most common sustained arrhythmia found in the clinical practice and is one of the main causes of stroke. It is known that the pulmonary veins (PVs) participate in AF initiation and perpetuation, and the PV isolation (PVI) performed by radiofrequency catheter ablation is effective in the therapy of the paroxysmal form of AF. PVI, however, is insufficient for persistent AF (persAF) therapy due to its complex underlying pathophysiology. Atrial regions hosting electrograms with fractionated activity (CFAEs) have been introduced as possible targets for persAF ablation, but inconsistent results have led to intense debate on the efficacy of CFAE-guided ablation as therapy for persAF.Consequently, the development of efficient methods for persAF treatment is of great interestfor both clinical and scientific community. More specifically, the precise identification ofatrial tissues responsible for the perpetuation of this arrhythmia is crucial for the developmentof new persAF ablative therapy. Recent studies have shown that not all atrial electrogram (AEG) fractionation correlates with AF perpetuation. However, these investigations have considered few, if not only one, attribute measured from the AEGs, which can be a limiting factor when investigating complex phenomenon such as AF. The present work will i) investigate multiple attributes measured from AEGs collected from persAF patients todiscriminate different types of AEG fractionation and; ii) correlate those AEGs with the underlying mechanisms of AF perpetuation using multivariate statistical models.