Key result
In an in-silico model, AF defragmentation rates after 10% highest dominant frequency ablation were lowest at a conduction velocity of 0.4 m/s (37.8%) and highest at 0.5 and 0.6 m/s (100%, p<0.001).
Why the study?
Does conduction velocity affect the efficacy of dominant frequency ablation in an in-silico model of persistent atrial fibrillation?
Does conduction velocity affect the efficacy of dominant frequency ablation in an in-silico model of persistent atrial fibrillation?
In-silico modeling demonstrates that the efficacy of dominant frequency ablation for atrial fibrillation is highly dependent on conduction velocity, with lower success rates in conditions favoring sustained AF.
Answers are grounded in this paper and cite their sources.
May influence AF ablation targeting by tissue properties; hypothesis-generating and requires clinical validation.
Background: We previously reported that stable rotors are observed in in-silico human atrial fibrillation (AF) models, and are well represented by a dominant frequency (DF). In the current study, we hypothesized that the outcome of DF ablation is affected by conduction velocity (CV) conditions and examined this hypothesis using in-silico 3D-AF modeling. Methods: We integrated 3D CT images of left atrium obtained from 10 patients with persistent AF (80% male, 61.8±13.5 years old) into in-silico AF model. We compared AF maintenance durations (max 300s), spatiotemporal stabilities of DF, phase singularity (PS) number, life-span of PS, and AF termination or defragmentation rates after virtual DF ablation with 5 different CV conditions (0.2, 0.3, 0.4, 0.5, and 0.6m/s). Results: 1. AF maintenance duration (p<0.001), spatiotemporal mean variance of DF (p<0.001), and the number of PS (p=0.023) showed CV dependent bimodal patterns (highest at CV0.4m/s and lowest at CV0.6m/s) consistently. 2. After 10% highest DF ablation, AF defragmentation rates were the lowest at CV0.4m/s (37.8%), but highest at CV0.5 and 0.6m/s (all 100%, p<0.001). 3. In the episodes with AF termination or defragmentation followed by 10% highest DF ablation, baseline AF maintenance duration was shorter (p<0.001), spatiotemporal mean variance of DF was lower (p=0.014), and the number of PS was lower (p=0.004) than those with failed AF defragmentation after DF ablation. Conclusion: DF ablation was more likely to terminate or defragment AF under conditions with easily terminating AF, but not under long-lasting and sustained AF conditions, depending on CV.
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Lim et al. (2018) studied persistent atrial fibrillation (n=10). Virtual dominant frequency (DF) ablation under different conduction velocity conditions vs. Different conduction velocities (0.2, 0.3, 0.4, 0.5, and 0.6 m/s) was evaluated on AF defragmentation rates after virtual DF ablation (p=<0.001). In an in-silico model, AF defragmentation rates after 10% highest dominant frequency ablation were lowest at a conduction velocity of 0.4 m/s (37.8%) and highest at 0.5 and 0.6 m/s (100%, p<0.001).
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