No abstract

Identification of atrial sites that perpetuate atrial fibrillation (AF), and ablation thereof terminates AF, is challenging. We hypothesized that specific electrogram (EGM) characteristics identify AF-termination sites (AFTS). Twenty-one patients in whom low-voltage-guided ablation after pulmonary vein isolation terminated clinical persistent AF were included. Patients were included if short RF-delivery for <8sec at a given atrial site was associated with acute termination of clinical persistent AF. EGM-characteristics at 21 AFTS, 105 targeted sites without termination and 105 non-targeted control sites were analyzed. Alteration of EGM-characteristics by local fibrosis was evaluated in a three-dimensional high resolution (100 µm)-computational AF model. AFTS demonstrated lower EGM-voltage, higher EGM-cycle-length-coverage, shorter AF-cycle-length and higher pattern consistency than control sites (0.49 ± 0.39 mV vs. 0.83 ± 0.76 mV, p < 0.0001; 79 ± 16% vs. 59 ± 22%, p = 0.0022; 173 ± 49 ms vs. 198 ± 34 ms, p = 0.047; 80% vs. 30%, p < 0.01). Among targeted sites, AFTS had higher EGM-cycle-length coverage, shorter local AF-cycle-length and higher pattern consistency than targeted sites without AF-termination (79 ± 16% vs. 63 ± 23%, p = 0.02; 173 ± 49 ms vs. 210 ± 44 ms, p = 0.002; 80% vs. 40%, p = 0.01). Low voltage (0.52 ± 0.3 mV) fractionated EGMs (79 ± 24 ms) with delayed components in sinus rhythm (‘atrial late potentials’, respectively ‘ALP’) were observed at 71% of AFTS. EGMs recorded from fibrotic areas in computational models demonstrated comparable EGM-characteristics both in simulated AF and sinus rhythm. AFTS may therefore be identified by locally consistent, fractionated low-voltage EGMs with high cycle-length-coverage and rapid activity in AF, with low-voltage, fractionated EGMs with delayed components/ ‘atrial late potentials’ (ALP) persisting in sinus rhythm.

In patients with atrial fibrillation, intracardiac electrogram signal amplitude is known to decrease with increased structural tissue remodeling, referred to as fibrosis. In addition to the isolation of the pulmonary veins, fibrotic sites are considered a suitable target for catheter ablation. However, it remains an open challenge to find fibrotic areas and to differentiate their density and transmurality. This study aims to identify the volume fraction and transmurality of fibrosis in the atrial substrate. Simulated cardiac electrograms, combined with a generalized model of clinical noise, reproduce clinically measured signals. Our hybrid dataset approach combines in silico and clinical electrograms to train a decision tree classifier to characterize the fibrotic atrial substrate. This approach captures different in vivo dynamics of the electrical propagation reflected on healthy electrogram morphology and synergistically combines it with synthetic fibrotic electrograms from in silico experiments. The machine learning algorithm was tested on five patients and compared against clinical voltage maps as a proof of concept, distinguishing non-fibrotic from fibrotic tissue and characterizing the patient’s fibrotic tissue in terms of density and transmurality. The proposed approach can be used to overcome a single voltage cut-off value to identify fibrotic tissue and guide ablation targeting fibrotic areas.

Under persistent atrial fibrillation (peAF), cardiac tissue experiences electrophysiological and structural remodeling. Fibrosis in the atrial tissue has an important impact on the myocyte action potential and its propagation. The objective of this work is to explore the effect of heterogeneities present in the fibrotic tissue and their impact on the intracardiac electrogram (EGM). Human atrial myocyte and fibroblast electrophysiology was simulated using mathematical models proposed by Koivumäki et al. to represent electrical remodeling under peAF and the paracrine effect of the transforming grow factor 1 (TGF-1). 2D tissue simulations were computed varying the density of fibrosis (10%, 20% and 40%), myofibroblasts and collagen were randomly distributed with different ratios (0%-100%, 50%-50% and 100%-0%). Results show that increasing the fibrosis density changes the re-entry dynamics from functional to anatomical due to a block in conduction in regions with high fibrosis density (40%). EGM morphology was affected by different ratios of myofibroblasts-collagen. For low myofibroblast densities (below 50%) the duration of active segments was shorter compared to higher myofibroblasts densities (above 50%). Our results show that fibrosis heterogeneities can alter the dynamics of the re-entry and the morphology of the EGM. Figure 3. A)Mean duration of the active segment for different fibrosis densities and myofibroblasts vs. collagen ratios inside and outside the fibrotic region. As fibrosis density increases, the duration of the detected segment also increases. At 40% fibrosis density voltage becomes very small and block can occur. B) Mean Shannon Entropy for inside and outside of the fibrotic region. Different densities of fibrosis increase the Shannon Entropy, it is not significantly affected by the ratio of myofibroblasts vs. collagen.

In patients with atrial fibrillation, intracardiac electrogram signal amplitude is known to decrease with increased structural tissue remodeling, referred to as fibrosis. In addition to the isolation of the pulmonary veins, fibrotic sites are considered a suitable target for catheter ablation. However, it remains an open challenge to find fibrotic areas and to differentiate their density and transmurality. This study aims to identify the volume fraction and transmurality of fibrosis in the atrial substrate. Simulated cardiac electrograms, combined with a generalized model of clinical noise, reproduce clinically measured signals. Our hybrid dataset approach combines in silico and clinical electrograms to train a decision tree classifier to characterize the fibrotic atrial substrate. This approach captures different in vivo dynamics of the electrical propagation reflected on healthy electrogram morphology and synergistically combines it with synthetic fibrotic electrograms from in silico experiments. The machine learning algorithm was tested on five patients and compared against clinical voltage maps as a proof of concept, distinguishing non-fibrotic from fibrotic tissue and characterizing the patient's fibrotic tissue in terms of density and transmurality. The proposed approach can be used to overcome a single voltage cut-off value to identify fibrotic tissue and guide ablation targeting fibrotic areas.