Characterizing persistent atrial fibrillation dynamics using computational models and recurrence quantification analysis – towards novel biomarkers for guiding therapy
Atrial fibrillation (AF) is the most common sustained arrhythmia found in the clinical practice and is one of the main causes of stroke. 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 and spatiotemporal behaviour. Recurrence analysis has been used to explore the underlying AF dynamics during persAF with promising initial results. Recurrence plots (RPs) and recurrence quantification analysis (RQA) represent a powerful set of tools for the investigation of dynamic systems. We have recently proposed rigorous steps to properly derive the RPs and for the estimation of RQA-based attributes extracted from persAF atrial electrograms (AEGs). This investigation, however, was conducted in sequential point-by-point AEGs, which fails to provide a global atrial mapping, imposing clear limitations in the validation of the proposed method. Computational intracardiac models that simulate both atrial electrical activity and ablation procedures during AF provide an ideal tool to continue this investigation. In this work, we will further investigate the persAF underlying dynamics through the combination of computational models – provided by the Institute of Biomedical Engineering, Karlsruhe Institute of Technology – and RQA- based attributes. We expect to validate the proposed method and investigate whether these attributes can help to identify atrial regions responsible for AF perpetuation, and to and accelerate its potential integration in clinical tools to guide AF ablation in future clinical studies.