Comparing Inducibility of Re-Entrant Arrhythmia in Patient-Specific Computational Models to Clinical Atrial Fibrillation Phenotypes

BACKGROUND Computational models of fibrosis-mediated, re-entrant left atrial (LA) arrhythmia can identify possible substrate for persistent atrial fibrillation (AF) ablation. Contemporary models use a 1-size-fits-all approach to represent electrophysiological properties, limiting agreement between simulations and patient outcomes. OBJECTIVES The goal of this study was to test the hypothesis that conduction velocity (W) modulation in persistent AF models can improve simulation agreement with clinical arrhythmias. METHODS Patients with persistent AF (n 1/4 37) underwent ablation and were followed up for $2 years to determine post-ablation outcomes: AF, atrial flutter (AFL), or no recurrence. Patient-specific LA models (n 1/4 74) were constructed using pre-ablation and $90 days' post-ablation magnetic resonance imaging data. Simulated pacing gauged in silico arrhythmia inducibility due to AF-like rotors or AFL-like macro re-entrant tachycardias. A physiologically plausible range of W values (similar to 10 or 20% vs. baseline) was tested, and model/clinical agreement was assessed. RESULTS Fifteen (41%) patients had a recurrence with AF and 6 (16%) with AFL. Arrhythmia was induced in 1,078 of 5,550 simulations. Using baseline W, model/clinical agreement was 46% (34 of 74 models), improving to 65% (48 of 74) when any possible W value was used (McNemar's test, P 1/4 0.014). W modulation improved model/clinical agreement in both pre-ablation and post-ablation models. Pre-ablation model/clinical agreement was significantly greater for patients with extensive LA fibrosis (>17.2%) and an elevated body mass index (>32.0 kg/m2). CONCLUSIONS Simulations in persistent AF models show a 41% relative improvement in model/clinical agreement when W is modulated. Patient-specific calibration of W values could improve model/clinical agreement and model usefulness, especially in patients with higher body mass index or LA fibrosis burden. This could ultimately facilitate better personalized modeling, with immediate clinical implications. (J Am Coll Cardiol EP 2023;9:2149-2162) (c) 2023 by the American College of Cardiology Foundation.

Automated summary

This study aims to test whether modulation of conduction velocity in persistent atrial fibrillation (AF) models can improve the agreement between simulation and clinical arrhythmias. The results show that modulation of conduction velocity significantly improves the agreement between models and clinical outcomes, especially in patients with higher body mass index or left atrial fibrosis burden. This finding contributes to better personalized modeling and immediate clinical implications.