P2846Focal impulse and rotor modulation ablation versus pulmonary vein isolation for the treatment of paroxysmal atrial fibrillation (FIRMAP AF study)

Tilz, R; Lenz, Corinna; Sommer, Philipp; Sawan, Noureddin; Meyer‐Saraei, Roza; Shpun, Shlomo; Sarver, Anne E.; Heeger, Christian-H.; Hindricks, Gerhard; Vogler, J; Eitel, C

doi:10.1093/eurheartj/ehz748.1156

Cited by 2 publications

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“…However, the study provides no direct evidence that their approach targeted fibrosis, as they targeted low-voltage areas that are known to have poor spatial correlation with fibrotic areas. In regard to rotor-based ablation, Tilz et al (2020) have shown that PVI had similar effectiveness to FIRM-guided ablation in paroxysmal AF patients. However, (i) their study provided no evidence that the same applies to persistent AF patients and (ii) FIRM is known for its limitations in identifying rotors, and therefore the study points to the limitations of the FIRM approach specifically, rather than of the rotor-based approach in general.…”

Section: Discussionmentioning

confidence: 99%

Toward Patient-Specific Prediction of Ablation Strategies for Atrial Fibrillation Using Deep Learning

et al. 2021

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Atrial fibrillation (AF) is a common cardiac arrhythmia that affects 1% of the population worldwide and is associated with high levels of morbidity and mortality. Catheter ablation (CA) has become one of the first line treatments for AF, but its success rates are suboptimal, especially in the case of persistent AF. Computational approaches have shown promise in predicting the CA strategy using simulations of atrial models, as well as applying deep learning to atrial images. We propose a novel approach that combines image-based computational modelling of the atria with deep learning classifiers trained on patient-specific atrial models, which can be used to assist in CA therapy selection. Therefore, we trained a deep convolutional neural network (CNN) using a combination of (i) 122 atrial tissue images obtained by unfolding patient LGE-MRI datasets, (ii) 157 additional synthetic images derived from the patient data to enhance the training dataset, and (iii) the outcomes of 558 CA simulations to terminate several AF scenarios in the corresponding image-based atrial models. Four CNN classifiers were trained on this patient-specific dataset balanced using several techniques to predict three common CA strategies from the patient atrial images: pulmonary vein isolation (PVI), rotor-based ablation (Rotor) and fibrosis-based ablation (Fibro). The training accuracy for these classifiers ranged from 96.22 to 97.69%, while the validation accuracy was from 78.68 to 86.50%. After training, the classifiers were applied to predict CA strategies for an unseen holdout test set of atrial images, and the results were compared to outcomes of the respective image-based simulations. The highest success rate was observed in the correct prediction of the Rotor and Fibro strategies (100%), whereas the PVI class was predicted in 33.33% of the cases. In conclusion, this study provides a proof-of-concept that deep neural networks can learn from patient-specific MRI datasets and image-derived models of AF, providing a novel technology to assist in tailoring CA therapy to a patient.

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Section: Discussionmentioning

confidence: 99%

Toward Patient-Specific Prediction of Ablation Strategies for Atrial Fibrillation Using Deep Learning

et al. 2021

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“…Interestingly, a significant proportion of rotors have been described in the right atrium (an area not normally targeted during AF ablation) (42), making focal impulse and rotor modulation (FIRM) an appealing adjunctive strategy (43). Although the CONFIRM (Conventional Ablation for Atrial Fibrillation With or Without Focal Impulse and Rotor Modulation) study reported a high degree of freedom from AF with FIRM ( 42), other studies have demonstrated that, despite the association with a more organized fibrillatory activation within the LA, it does not correlate with an increase in freedom from AF, even after redo procedures (43)(44)(45)(46). Furthermore, a head-to-head meta-analysis of six studies including 716 patients (88.5% non-PAF) concluded that FIRM did not add any benefit in improving all-atrial arrhythmia recurrence (47).…”

Section: Rotorsmentioning

confidence: 99%

Contemporary Ablation Techniques for Atrial Fibrillation: An Evidence-Based Analysis

Purkayastha

Romero

Lakkireddy

et al. 2022

Preprint

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Since the advent of catheter ablation (CA) for atrial fibrillation (AF) in 1998, our understanding of the pathophysiology, as well as our capacity to target triggers and effectively achieve freedom from AF, has come a long way. However, as the average age of the population and the prevalence of chronic diseases increases, the progression of AF from paroxysmal (PAF) into persistent (PersAF) and long-standing persistent AF (LSPAF) is on the rise. Given the complex mechanisms involved and the presence of multiple trigger sites leading to recurrences, managing non-PAF is a challenge for electrophysiologists around the globe. Multiple ablation techniques have been investigated, some of which have shown promising results, but a standardized technique is yet to be elucidated. In this review, we outline the evidence behind contemporary ablation techniques in the treatment of AF.

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P2846Focal impulse and rotor modulation ablation versus pulmonary vein isolation for the treatment of paroxysmal atrial fibrillation (FIRMAP AF study)

Cited by 2 publications

References 0 publications

Toward Patient-Specific Prediction of Ablation Strategies for Atrial Fibrillation Using Deep Learning

Toward Patient-Specific Prediction of Ablation Strategies for Atrial Fibrillation Using Deep Learning

Contemporary Ablation Techniques for Atrial Fibrillation: An Evidence-Based Analysis

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