A patient-specific model of virtual ablation for atrial fibrillation

Kwon, Soon-Sung; Yun, Yong Hyun; Hong, Seung-Bae; Pak, Hui Nam; Shim, Eun Bo

doi:10.1109/embc.2013.6609802

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…The biggest obstacle to the clinical use of a sophisticated AF computational modeling so far has been the long computational speed of a complex simulation. However, recent innovations in the hardware and software have opened the gate for the clinical utilization of AF computational modeling (Kwon et al, 2013 ). Lim et al ( 2020b ) used a graphic process unit to analyze the AF modeling and a wave-dynamics analysis within 45 min while considering the patient's anatomy (cardiac computed tomogram), electrophysiology (3D-electroanatomical map), fibrosis (voltage map), and fiber orientation (LAT map).…”

Section: Discussionmentioning

confidence: 99%

Computational Modeling for Antiarrhythmic Drugs for Atrial Fibrillation According to Genotype

et al. 2021

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Background: The efficacy of antiarrhythmic drugs (AAD) can vary in patients with atrial fibrillation (AF), and the PITX2 gene affects the responsiveness of AADs. We explored the virtual AAD (V-AAD) responses between wild-type and PITX2+/−-deficient AF conditions by realistic in silico AF modeling.Methods: We tested the V-AADs in AF modeling integrated with patients' 3D-computed tomography and 3D-electroanatomical mapping, acquired in 25 patients (68% male, 59.8 ± 9.8 years old, 32.0% paroxysmal type). The ion currents for the PITX2+/− deficiency and each AAD (amiodarone, sotalol, dronedarone, flecainide, and propafenone) were defined based on previous publications.Results: We compared the wild-type and PITX2+/− deficiency in terms of the action potential duration (APD90), conduction velocity (CV), maximal slope of restitution (Smax), and wave-dynamic parameters, such as the dominant frequency (DF), phase singularities (PS), and AF termination rates according to the V-AADs. The PITX2+/−-deficient model exhibited a shorter APD90 (p < 0.001), a lower Smax (p < 0.001), mean DF (p = 0.012), PS number (p < 0.001), and a longer AF cycle length (AFCL, p = 0.011). Five V-AADs changed the electrophysiology in a dose-dependent manner. AAD-induced AFCL lengthening (p < 0.001) and reductions in the CV (p = 0.033), peak DF (p < 0.001), and PS number (p < 0.001) were more significant in PITX2+/−-deficient than wild-type AF. PITX2+/−-deficient AF was easier to terminate with class IC AADs than the wild-type AF (p = 0.018).Conclusions: The computational modeling-guided AAD test was feasible for evaluating the efficacy of multiple AADs in patients with AF. AF wave-dynamic and electrophysiological characteristics are different among the PITX2-deficient and the wild-type genotype models.

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Section: Discussionmentioning

confidence: 99%

Computational Modeling for Antiarrhythmic Drugs for Atrial Fibrillation According to Genotype

et al. 2021

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“…The simulation results were confirmed with clinical data. The identification of the optimal ablation line is the subject of several studies which point out the need for tailored ablation lines for each patient (Rotter et al, 2007; Ruchat et al, 2007a, 2007b, 2007c; Reumann et al, 2008; Kwon et al, 2013; Hwang et al, 2014; Zhu et al, 2014; Rappel et al, 2015; Bayer et al, 2016). During the ablation procedure, in most cases, the PVs are isolated.…”

Section: Resultsmentioning

confidence: 99%

Understanding the Beat-to-Beat Variations of P-Waves Morphologies in AF Patients During Sinus Rhythm: A Scoping Review of the Atrial Simulation Studies

et al. 2019

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The remarkable advances in high-performance computing and the resulting increase of the computational power have the potential to leverage computational cardiology toward improving our understanding of the pathophysiological mechanisms of arrhythmias, such as Atrial Fibrillation (AF). In AF, a complex interaction between various triggers and the atrial substrate is considered to be the leading cause of AF initiation and perpetuation. In electrocardiography (ECG), P-wave is supposed to reflect atrial depolarization. It has been found that even during sinus rhythm (SR), multiple P-wave morphologies are present in AF patients with a history of AF, suggesting a higher dispersion of the conduction route in this population. In this scoping review, we focused on the mechanisms which modify the electrical substrate of the atria in AF patients, while investigating the existence of computational models that simulate the propagation of the electrical signal through different routes. The adopted review methodology is based on a structured analytical framework which includes the extraction of the keywords based on an initial limited bibliographic search, the extensive literature search and finally the identification of relevant articles based on the reference list of the studies. The leading mechanisms identified were classified according to their scale, spanning from mechanisms in the cell, tissue or organ level, and the produced outputs. The computational modeling approaches for each of the factors that influence the initiation and the perpetuation of AF are presented here to provide a clear overview of the existing literature. Several levels of categorization were adopted while the studies which aim to translate their findings to ECG phenotyping are highlighted. The results denote the availability of multiple models, which are appropriate under specific conditions. However, the consideration of complex scenarios taking into account multiple spatiotemporal scales, personalization of electrophysiological and anatomical models and the reproducibility in terms of ECG phenotyping has only partially been tackled so far.

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“…Nevertheless, the biggest obstacle that exists in applying AF computational modeling to clinical medicine has been the high computational burden. Recently, however, this limitation has been overcome by a parallel computing technology ( 30 ), and MRI-based computational modeling has become technically feasible for arrhythmia interventions ( 29 ). Simulation-guided AFCA provides precision medical care that reflects the patient's personalized anatomy, histology, and electrophysiological characteristics ( 31 ).…”

Section: Discussionmentioning

confidence: 99%

Clinical Outcomes of Computational Virtual Mapping-Guided Catheter Ablation in Patients With Persistent Atrial Fibrillation: A Multicenter Prospective Randomized Clinical Trial

Baek

Kwon

Lim

et al. 2021

Front. Cardiovasc. Med.

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Background: Clinical recurrence after atrial fibrillation catheter ablation (AFCA) still remains high in patients with persistent AF (PeAF). We investigated whether an extra-pulmonary vein (PV) ablation targeting the dominant frequency (DF) extracted from electroanatomical map–integrated AF computational modeling improves the AFCA rhythm outcome in patients with PeAF.Methods: In this open-label, randomized, multi-center, controlled trial, 170 patients with PeAF were randomized at a 1:1 ratio to the computational modeling-guided virtual DF (V-DF) ablation and empirical PV isolation (E-PVI) groups. We generated a virtual dominant frequency (DF) map based on the atrial substrate map obtained during the clinical AF ablation procedure using computational modeling. This simulation was possible within the time of the PVI procedure. V-DF group underwent extra-PV V-DF ablation in addition to PVI, but DF information was not notified to the operators from the core lab in the E-PVI group.Results: After a mean follow-up period of 16.3 ± 5.3 months, the clinical recurrence rate was significantly lower in the V-DF than with E-PVI group (P = 0.018, log-rank). Recurrences appearing as atrial tachycardias (P = 0.145) and the cardioversion rates (P = 0.362) did not significantly differ between the groups. At the final follow-up, sinus rhythm was maintained without any AADs in 74.7% in the V-DF group and 48.2% in the E-PVI group (P < 0.001). No significant difference was found in the major complication rates (P = 0.489) or total procedure time (P = 0.513) between the groups. The V-DF ablation was independently associated with a reduced AF recurrence after AFCA [hazard ratio: 0.51 (95% confidence interval: 0.30–0.88); P = 0.016].Conclusions: The computational modeling-guided V-DF ablation improved the rhythm outcome of AFCA in patients with PeAF.Clinical Trial Registration: Clinical Research Information Service, CRIS identifier: KCT0003613.

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A patient-specific model of virtual ablation for atrial fibrillation

Cited by 5 publications

References 11 publications

Computational Modeling for Antiarrhythmic Drugs for Atrial Fibrillation According to Genotype

Computational Modeling for Antiarrhythmic Drugs for Atrial Fibrillation According to Genotype

Understanding the Beat-to-Beat Variations of P-Waves Morphologies in AF Patients During Sinus Rhythm: A Scoping Review of the Atrial Simulation Studies

Clinical Outcomes of Computational Virtual Mapping-Guided Catheter Ablation in Patients With Persistent Atrial Fibrillation: A Multicenter Prospective Randomized Clinical Trial

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