Introduction: Current Atrial Fibrillation (AF) treatments often follow a one-size-fits-all approach, overlooking the effect of patient-specific fibrosis on therapeutic outcomes. A computational framework integrating patient-specific anatomical and fibrotic data is needed to personalise therapy selection at scale.
Objective: To assess the effect of patient-specific fibrosis on the success rates of anti-arrhythmic drugs (AADs) and pulmonary vein isolation (PVI) using personalised biatrial models derived from imaging data.
Methods: Patient-specific biatrial models (n=69) were constructed from late gadolinium-enhanced MRI scans by manual segmentation. Pre-processing included fibrosis quantification and integration of atrial fibre data from a fibre atlas. PVI was simulated as non-conductive lesions around the pulmonary veins. Three AADs (vernakalant, amiodarone [acute, chronic], and flecainide) were virtually administered via ionic modulation in OpenCarp software. Finite element simulations were performed and post-processed to compare AF termination rates of therapies in fibrotic and non-fibrotic anatomies.
Results: The success rates of AADs in fibrotic anatomies closely matched with the European Society of Cardiology (ESC) 2024 guideline acute success rates: flecainide had the highest rate (67%, ESC 50%-60%), followed by vernakalant (58%, ESC 50%) and amiodarone (35% [acute], 45% [chronic]; ESC 44%). The combination of PVI and AADs did not significantly improve success rates compared to AADs alone in fibrotic anatomies. A negative correlation between right atrial (RA) surface area and AAD success (ρ=-0.42, p<0.05) suggests that larger RA surface areas reduce AAD efficacy. Increased PS localisation in the RA following AAD administration further supports RA surface area as a potential biomarker for treatment response.
Conclusion: This work highlights the added value of fibrosis-driven personalisation in guiding therapeutic decisions and tailoring treatment strategies.