Identifying Locations of Re-entrant Drivers from Patient-Specific Distribution of Fibrosis in the Left Atrium

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)
43 Downloads (Pure)


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.
Original languageEnglish
Article number1008086
JournalPLoS Computational Biology
Issue number9
Publication statusPublished - 23 Sept 2020


Dive into the research topics of 'Identifying Locations of Re-entrant Drivers from Patient-Specific Distribution of Fibrosis in the Left Atrium'. Together they form a unique fingerprint.

Cite this