Identifying locations of re-entrant drivers from patient-specific distribution of fibrosis in the left atrium.

Clinical evidence suggests a link between fibrosis in the left atrium (LA) and atrial fibrillation (AF), the most common sustained arrhythmia. Image-derived fibrosis is increasingly used for patient stratification and therapy guidance. However, locations of re-entrant drivers (RDs) sustaining AF are unknown and therapy success rates remain suboptimal. This study used image-derived LA models to explore the dynamics of RD stabilization in fibrotic regions and generate maps of RD locations. LA models with patient-specific geometry and fibrosis distribution were derived from late gadolinium enhanced magnetic resonance imaging of 6 AF patients. In each model, RDs were initiated at multiple locations, and their trajectories were tracked and overlaid on the LA fibrosis distributions to identify the most likely regions where the RDs stabilized. The simulations showed that the RD dynamics were strongly influenced by the amount and spatial distribution of fibrosis. In patients with fibrosis burden greater than 25%, RDs anchored to specific locations near large fibrotic patches. In patients with fibrosis burden below 25%, RDs either moved near small fibrotic patches or anchored to anatomical features. The patient-specific maps of RD locations showed that areas that harboured the RDs were much smaller than the entire fibrotic areas, indicating potential targets for ablation therapy. Ablating the predicted locations and connecting them to the existing pulmonary vein ablation lesions was the most effective in-silico ablation strategy. Author summary: Atrial fibrillation (AF) is the most common cardiac arrhythmia and a huge healthcare problem, but its mechanisms are incompletely understood, and clinical therapies such as catheter ablation (CA) have poor long-term outcomes. This is due to the empirical nature of the procedure and lack of mechanistic knowledge of optimal ablation sites and strategies in patients whose atria is altered by AF-induced structural remodelling. In this study, we developed 3D atrial models with patient-specific geometry and distribution of fibrosis obtained from AF patient imaging and applied the models to explore the mechanisms of re-entrant drivers (RD) sustaining AF. Moreover, we used the novel mechanistic knowledge to simulate CA based on the model predictions and compared its success with existing clinical CA strategies. We discovered that the RD dynamics were strongly influenced by the spatial distribution of fibrosis, and RDs typically anchored to specific locations near large fibrotic patches. Virtual ablations of such anchoring locations by connecting them with linear lesions to the nearest pulmonary veins (PV) had superior efficacy compared to clinically used strategies such as the PV isolation. After incorporating further patient-specific information and careful validation, the proposed in-silico approach can help evaluate and potentially guide CA therapy in the clinic. [ABSTRACT FROM AUTHOR]