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

Abstract

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 (ϴ) modulation in persistent AF models can improve simulation agreement with clinical arrhythmias.

Methods: Patients with persistent AF (n = 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 = 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 ϴ values (±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 ϴ, model/clinical agreement was 46% (34 of 74 models), improving to 65% (48 of 74) when any possible ϴ value was used (McNemar's test, P = 0.014). ϴ 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 ϴ is modulated. Patient-specific calibration of ϴ 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.

Keywords: MRI; atrial fibrillation; computational modeling; conduction velocity; fibrosis.

Figure 1:

Panel A shows the total number of simulations in which pacing induced a rotor or MT in pre- and post-ablation models. Panel B shows the percentage of simulations that demonstrated rotors or MTs separated into pre- (left) and post-ablation (right). Panels C-E contain box-and-whisker plots showing the number (per model) of total inducible simulations, inducible rotors, and inducible MTs, respectively. All panels are stratified by ϴ, increasing from left to right. MT, macroreentrant tachycardia, ϴ, conduction velocity.

Figure 2:

Panels A-D show four models that were not inducible using baseline ϴ that demonstrated rotors or MTs and agreement with clinical phenotype using the designated ϴ. The left side of each panel shows the non-inducible baseline model. The right side of each panel shows the corresponding activation maps that demonstrate simulation inducibility at each of the specified ϴ values. The top row (A-B) shows the effect of exacerbated ϴ (0.8 and 0.9×ϴ) and the bottom row (C-D) shows attenuated ϴ (1.1 and 1.2×ϴ). AF, atrial fibrillation, LAA, left atrial appendage, L/RIPV, left/right inferior pulmonary vein, L/RSPV, right/left superior pulmonary vein, MT, macroreentrant tachycardia, MV, mitral valve, ϴ, conduction velocity.

Figure 3:

Panel A shows model/phenotype agreement using baseline ϴ (left column in each set of columns) vs using any ϴ (right column). The Y-axis shows the number of models in agreement divided by the total number of models multiplied by 100. The total cohort is the left most set of columns and then each phenotype follows. Panel B shows the number of models for each phenotype and the total cohort that achieved model/phenotype agreement when different ϴ values were used, with increasing ϴ from left to right. Panel C shows model/phenotype agreement in the total cohort using each specific ϴ value, increasing from left to right. Panel D shows model/phenotype agreement using each specific ϴ value for each clinical phenotype. AF, atrial fibrillation, AFL, atrial flutter, ϴ, conduction velocity.

Figure 4:

Panels A-D show model/phenotype agreement for patients separated by the presence of pre-ablation clinical characteristics: BMI, percentage LGE on pre-ablation MRI, OSA, and hypertension. Panels E-F show model/phenotype agreement for patients taking amiodarone and metoprolol, respectively. Each panel is stratified by ϴ, increasing from left to right. BMI, body mass index, LGE, late gadolinium enhancement, OSA, obstructive sleep apnea, ϴ, conduction velocity.

Central Illustration:

Current protocols for simulating reentrant atrial arrhythmia use the same conduction velocity for all models despite known differences between patients. This study evaluated how model/phenotype agreement would be affected by allowing a wider range of potential conduction velocities in simulations. Models were created from pre- and post-ablation clinical imaging of the left atrium. Simulations assessed for inducibility rotor-driven reentry or macroreentrant tachycardia (MT). Patients were monitored for recurrence for 24 months with ambulatory ECG monitoring. Model/phenotype agreement was defined for each of the four clinical phenotypes. In the overall cohort, conduction velocity modulation improved model/phenotype agreement.