Biatrial Modelling for In Silico Prediction of Atrial Fibrillation Inducibility

Atrial fibrillation (AF) is a cardiac disorder characterised by rapid atrial contractions. Current treatments, including ablation, vary in effectiveness. Recent mechanistic modelling studies have highlighted the significance of the right atrium (RA) in predicting AF outcomes, although its role remains unclear. This study employs a novel open-source biatrial modelling pipeline to assess AF inducibility and monitor AF dynamics on clinical timescales. Patient-specific models were created from late gadolinium enhancement MRI (LGE-MRI) scans of 20 patients. Manual RA and left atrial (LA) segmentation, fibrosis mapping in pre-processing, and calculation of atrial coordinates to incorporate atrial structures and fibres were performed. These personalised models were simulated and post-processed to assess the AF wavefront patterns. RA integration significantly increased rotor activity and total phase singularities (PS) within the LA posterior walls and reduced conduction velocity, indicating greater potential for AF sustainability. LA exhibited a higher mean PS density (3.8 rotors/cm
²
) than RA (2.1 rotors/cm
²
), indicating regions prone to re-entry or wavefront break-up. The modelling pipeline highlights the potential of biatrial models to efficiently predict AF outcomes, enabling personalised therapies and comparisons of ablation approaches and anti-arrhythmic drug therapies.