Personalized ablation vs. conventional ablation strategies to terminate atrial fibrillation and prevent recurrence

Abstract

Aims The long-term success rate of ablation therapy is still sub-optimal in patients with persistent atrial fibrillation (AF), mostly due to arrhythmia recurrence originating from arrhythmogenic sites outside the pulmonary veins. Computational modelling provides a framework to integrate and augment clinical data, potentially enabling the patient-specific identification of AF mechanisms and of the optimal ablation sites. We developed a technology to tailor ablations in anatomical and functional digital atrial twins of patients with persistent AF aiming to identify the most successful ablation strategy. Methods and results Twenty-nine patient-specific computational models integrating clinical information from tomographic imaging and electro-anatomical activation time and voltage maps were generated. Areas sustaining AF were identified by a personalized induction protocol at multiple locations. State-of-the-art anatomical and substrate ablation strategies were compared with our proposed Personalized Ablation Lines (PersonAL) plan, which consists of iteratively targeting emergent high dominant frequency (HDF) regions, to identify the optimal ablation strategy. Localized ablations were connected to the closest non-conductive barrier to prevent recurrence of AF or atrial tachycardia. The first application of the HDF strategy had a success of >98% and isolated only 5–6% of the left atrial myocardium. In contrast, conventional ablation strategies targeting anatomical or structural substrate resulted in isolation of up to 20% of left atrial myocardium. After a second iteration of the HDF strategy, no further arrhythmia episode could be induced in any of the patient-specific models. Conclusion The novel PersonAL in silico technology allows to unveil all AF-perpetuating areas and personalize ablation by leveraging atrial digital twins.

Atrial fibrillation: Insights from animal models, computational modeling, and clinical studies

“…used different combinations of imaging (LGE and CT) and mapping (EAM) data from 29 perAF patients to create patient-specific atrial models that were used to determine personalized ablation targets. 75 The results from this study showed that PVI had the lowest acute success rate and that personalized fibrosis-targeted ablation resulted in success rates as high as 90.9%, although it required EAM data for model construction. 75 Lastly, the results from these mechanistic models may also be used to prevent AF recurrence, where the features of a patient's LGE MRI scan and personalized simulations are used to predict the patient's risk of AF recurrence.…”

Effects of hydroxychloroquine on atrial electrophysiology in in silico wild-type and PITX2+/- atrial cardiomyocytes

“…Thus, interruption of autophagy flux by HCQ may prevent the electrical and structural remodeling of AF. Cardiac computational modeling approaches can be used to quantitatively understand complex subcellular or spatiotemporal dynamics of AF [23,25,33] and to perform personalized AF ablation as shown in the OPTIMA [22] and PersonAL [23] studies. Recently, these computational models have also been utilized for in-silico antiarrhythmic drug trials and drug toxicity screening [18,19,24,25].…”

“…The copyright holder for this preprint this version posted December 11, 2022. ; https://doi.org/10.1101/2022.12.07.519115 doi: bioRxiv preprint models have been developed based on quantitative patch-clamp data of human cardiomyocytes [20,21] and recently utilized to perform computational modeling-guided ablation of atrial fibrillation (AF) [22,23] or in-silico antiarrhythmic drug trials [24,25]. However, most recent preclinical studies have focused on the HCQ-associated TdP risk stratification in ventricular cells.…”

“…To examine the potential proarrhythmogenic risk of non-cardiovascular drugs such as HCQ, computational modeling-based drug toxicity screening frameworks (e.g., CiPA [18, 19]) have been proposed and tested [15, 16]. These computational electrophysiology models have been developed based on quantitative patch-clamp data of human cardiomyocytes [20, 21] and recently utilized to perform computational modeling-guided ablation of atrial fibrillation (AF) [22, 23] or in-silico antiarrhythmic drug trials [24, 25]. However, most recent preclinical studies have focused on the HCQ-associated TdP risk stratification in ventricular cells.…”

How synergy between mechanistic and statistical models is impacting research in atrial fibrillation

“…They used the Pacing at the End of the Effective Refractory Period (PEERP) protocol to localize emergent AF episodes, and then connected localized ablations to the closest non-conductive barrier to prevent recurrence of AF ( Azzolin et al, 2021b ). This study found that the proposed Personalized Ablation Lines (PersonAL) plan, consisting of iteratively targeting emergent high dominant frequency regions, outperformed state-of-the-art anatomical and substrate ablation strategies ( Azzolin et al, 2022b ).…”