Dataset Information

Fibrosis and Atrial Fibrillation: Computerized and Optical Mapping; A View into the Human Atria at Submillimeter Resolution.

ABSTRACT: Recent studies strongly suggest that the majority of atrial fibrillation (AF) patients with diagnosed or subclinical cardiac diseases have established or even pre-existing fibrotic structural remodeling, which may lead to conduction abnormalities and reentrant activity that sustain AF. As conventional treatments fail to treat AF in far too many cases, an urgent need exists to identify specific structural arrhythmogenic fibrosis patterns, which may maintain AF, in order to identify effective ablation targets for AF treatment. However, the existing challenge is to define what exact structural remodeling within the complex 3D human atrial wall is arrhythmogenic, as well as linking arrhythmogenic fibrosis to an underlying mechanism of AF maintenance in the clinical setting. This review is focused on the role of 3D fibrosis architecture in the mechanisms of AF maintenance revealed by submillimeter, high-resolution ex-vivo imaging modalities directly of human atria, as well as from in-silico 3D computational techniques that can be able to overcome in-vivo clinical limitations. The systematic integration of functional and structural imaging ex-vivo may inform the necessary integration of electrode and structural mapping in-vivo. A holistic view of AF driver mechanisms may begin to identify the defining characteristics or "fingerprints" of reentrant AF drivers, such as 3D fibrotic architecture, in order to design optimal patient-specific ablation strategies.

SUBMITTER: Hansen BJ

PROVIDER: S-EPMC5693365 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Publications

Fibrosis and Atrial Fibrillation: Computerized and Optical Mapping; A View into the Human Atria at Submillimeter Resolution.

Hansen Brian J BJ Zhao Jichao J Fedorov Vadim V VV

JACC. Clinical electrophysiology 20170620 6

Recent studies strongly suggest that the majority of atrial fibrillation (AF) patients with diagnosed or subclinical cardiac diseases have established or even pre-existing fibrotic structural remodeling, which may lead to conduction abnormalities and reentrant activity that sustain AF. As conventional treatments fail to treat AF in far too many cases, an urgent need exists to identify specific structural arrhythmogenic fibrosis patterns, which may maintain AF, in order to identify effective abla ...[more]

PMID: 29159313

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Project description:AimsAtrial fibrillation (AF) is a progressive cardiac arrhythmia that increases the risk of hospitalization and adverse cardiovascular events. There is a clear demand for more inclusive and large-scale approaches to understand the molecular drivers responsible for AF, as well as the fundamental mechanisms governing the transition from paroxysmal to persistent and permanent forms. In this study, we aimed to create a molecular map of AF and find the distinct molecular programmes underlying cell type-specific atrial remodelling and AF progression.Methods and resultsWe used a sheep model of long-standing, tachypacing-induced AF, sampled right and left atrial tissue, and isolated cardiomyocytes (CMs) from control, intermediate (transition), and late time points during AF progression, and performed transcriptomic and proteome profiling. We have merged all these layers of information into a meaningful three-component space in which we explored the genes and proteins detected and their common patterns of expression. Our data-driven analysis points at extracellular matrix remodelling, inflammation, ion channel, myofibril structure, mitochondrial complexes, chromatin remodelling, and genes related to neural function, as well as critical regulators of cell proliferation as hallmarks of AF progression. Most important, we prove that these changes occur at early transitional stages of the disease, but not at later stages, and that the left atrium undergoes significantly more profound changes than the right atrium in its expression programme. The pattern of dynamic changes in gene and protein expression replicate the electrical and structural remodelling demonstrated previously in the sheep and in humans, and uncover novel mechanisms potentially relevant for disease treatment.ConclusionsTranscriptomic and proteomic analysis of AF progression in a large animal model shows that significant changes occur at early stages, and that among others involve previously undescribed increase in mitochondria, changes to the chromatin of atrial CMs, and genes related to neural function and cell proliferation.

Project description:BackgroundAtrial fibrillation (AF) can be maintained by localized intramural reentrant drivers. However, AF driver detection by clinical surface-only multielectrode mapping (MEM) has relied on subjective interpretation of activation maps. We hypothesized that application of machine learning to electrogram frequency spectra may accurately automate driver detection by MEM and add some objectivity to the interpretation of MEM findings.MethodsTemporally and spatially stable single AF drivers were mapped simultaneously in explanted human atria (n=11) by subsurface near-infrared optical mapping (NIOM; 0.3 mm2 resolution) and 64-electrode MEM (higher density or lower density with 3 and 9 mm2 resolution, respectively). Unipolar MEM and NIOM recordings were processed by Fourier transform analysis into 28 407 total Fourier spectra. Thirty-five features for machine learning were extracted from each Fourier spectrum.ResultsTargeted driver ablation and NIOM activation maps efficiently defined the center and periphery of AF driver preferential tracks and provided validated annotations for driver versus nondriver electrodes in MEM arrays. Compared with analysis of single electrogram frequency features, averaging the features from each of the 8 neighboring electrodes, significantly improved classification of AF driver electrograms. The classification metrics increased when less strict annotation, including driver periphery electrodes, were added to driver center annotation. Notably, f1-score for the binary classification of higher-density catheter data set was significantly higher than that of lower-density catheter (0.81±0.02 versus 0.66±0.04, P<0.05). The trained algorithm correctly highlighted 86% of driver regions with higher density but only 80% with lower-density MEM arrays (81% for lower-density+higher-density arrays together).ConclusionsThe machine learning model pretrained on Fourier spectrum features allows efficient classification of electrograms recordings as AF driver or nondriver compared with the NIOM gold-standard. Future application of NIOM-validated machine learning approach may improve the accuracy of AF driver detection for targeted ablation treatment in patients.

Dataset Information

Fibrosis and Atrial Fibrillation: Computerized and Optical Mapping; A View into the Human Atria at Submillimeter Resolution.

Publications

Fibrosis and Atrial Fibrillation: Computerized and Optical Mapping; A View into the Human Atria at Submillimeter Resolution.

Similar Datasets

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