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Slow Recovery of Excitability Increases Ventricular Fibrillation Risk as Identified by Emulation

Purpose: Rotor stability and meandering are key mechanisms determining and sustaining cardiac fibrillation, with important implications for anti-arrhythmic drug development. However, little is yet known on how rotor dynamics are modulated by variability in cellular electrophysiology, particularly on...

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Autores principales: Lawson, Brodie A., Burrage, Kevin, Burrage, Pamela, Drovandi, Christopher C., Bueno-Orovio, Alfonso
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6121112/
https://www.ncbi.nlm.nih.gov/pubmed/30210355
http://dx.doi.org/10.3389/fphys.2018.01114
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author Lawson, Brodie A.
Burrage, Kevin
Burrage, Pamela
Drovandi, Christopher C.
Bueno-Orovio, Alfonso
author_facet Lawson, Brodie A.
Burrage, Kevin
Burrage, Pamela
Drovandi, Christopher C.
Bueno-Orovio, Alfonso
author_sort Lawson, Brodie A.
collection PubMed
description Purpose: Rotor stability and meandering are key mechanisms determining and sustaining cardiac fibrillation, with important implications for anti-arrhythmic drug development. However, little is yet known on how rotor dynamics are modulated by variability in cellular electrophysiology, particularly on kinetic properties of ion channel recovery. Methods: We propose a novel emulation approach, based on Gaussian process regression augmented with machine learning, for data enrichment, automatic detection, classification, and analysis of re-entrant biomarkers in cardiac tissue. More than 5,000 monodomain simulations of long-lasting arrhythmic episodes with Fenton-Karma ionic dynamics, further enriched by emulation to 80 million electrophysiological scenarios, were conducted to investigate the role of variability in ion channel densities and kinetics in modulating rotor-driven arrhythmic behavior. Results: Our methods predicted the class of excitation behavior with classification accuracy up to 96%, and emulation effectively predicted frequency, stability, and spatial biomarkers of functional re-entry. We demonstrate that the excitation wavelength interpretation of re-entrant behavior hides critical information about rotor persistence and devolution into fibrillation. In particular, whereas action potential duration directly modulates rotor frequency and meandering, critical windows of excitability are identified as the main determinants of breakup. Further novel electrophysiological insights of particular relevance for ventricular arrhythmias arise from our multivariate analysis, including the role of incomplete activation of slow inward currents in mediating tissue rate-dependence and dispersion of repolarization, and the emergence of slow recovery of excitability as a significant promoter of this mechanism of dispersion and increased arrhythmic risk. Conclusions: Our results mechanistically explain pro-arrhythmic effects of class Ic anti-arrhythmics in the ventricles despite their established role in the pharmacological management of atrial fibrillation. This is mediated by their slow recovery of excitability mode of action, promoting incomplete activation of slow inward currents and therefore increased dispersion of repolarization, given the larger influence of these currents in modulating the action potential in the ventricles compared to the atria. These results exemplify the potential of emulation techniques in elucidating novel mechanisms of arrhythmia and further application to cardiac electrophysiology.
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spelling pubmed-61211122018-09-12 Slow Recovery of Excitability Increases Ventricular Fibrillation Risk as Identified by Emulation Lawson, Brodie A. Burrage, Kevin Burrage, Pamela Drovandi, Christopher C. Bueno-Orovio, Alfonso Front Physiol Physiology Purpose: Rotor stability and meandering are key mechanisms determining and sustaining cardiac fibrillation, with important implications for anti-arrhythmic drug development. However, little is yet known on how rotor dynamics are modulated by variability in cellular electrophysiology, particularly on kinetic properties of ion channel recovery. Methods: We propose a novel emulation approach, based on Gaussian process regression augmented with machine learning, for data enrichment, automatic detection, classification, and analysis of re-entrant biomarkers in cardiac tissue. More than 5,000 monodomain simulations of long-lasting arrhythmic episodes with Fenton-Karma ionic dynamics, further enriched by emulation to 80 million electrophysiological scenarios, were conducted to investigate the role of variability in ion channel densities and kinetics in modulating rotor-driven arrhythmic behavior. Results: Our methods predicted the class of excitation behavior with classification accuracy up to 96%, and emulation effectively predicted frequency, stability, and spatial biomarkers of functional re-entry. We demonstrate that the excitation wavelength interpretation of re-entrant behavior hides critical information about rotor persistence and devolution into fibrillation. In particular, whereas action potential duration directly modulates rotor frequency and meandering, critical windows of excitability are identified as the main determinants of breakup. Further novel electrophysiological insights of particular relevance for ventricular arrhythmias arise from our multivariate analysis, including the role of incomplete activation of slow inward currents in mediating tissue rate-dependence and dispersion of repolarization, and the emergence of slow recovery of excitability as a significant promoter of this mechanism of dispersion and increased arrhythmic risk. Conclusions: Our results mechanistically explain pro-arrhythmic effects of class Ic anti-arrhythmics in the ventricles despite their established role in the pharmacological management of atrial fibrillation. This is mediated by their slow recovery of excitability mode of action, promoting incomplete activation of slow inward currents and therefore increased dispersion of repolarization, given the larger influence of these currents in modulating the action potential in the ventricles compared to the atria. These results exemplify the potential of emulation techniques in elucidating novel mechanisms of arrhythmia and further application to cardiac electrophysiology. Frontiers Media S.A. 2018-08-28 /pmc/articles/PMC6121112/ /pubmed/30210355 http://dx.doi.org/10.3389/fphys.2018.01114 Text en Copyright © 2018 Lawson, Burrage, Burrage, Drovandi and Bueno-Orovio. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Physiology
Lawson, Brodie A.
Burrage, Kevin
Burrage, Pamela
Drovandi, Christopher C.
Bueno-Orovio, Alfonso
Slow Recovery of Excitability Increases Ventricular Fibrillation Risk as Identified by Emulation
title Slow Recovery of Excitability Increases Ventricular Fibrillation Risk as Identified by Emulation
title_full Slow Recovery of Excitability Increases Ventricular Fibrillation Risk as Identified by Emulation
title_fullStr Slow Recovery of Excitability Increases Ventricular Fibrillation Risk as Identified by Emulation
title_full_unstemmed Slow Recovery of Excitability Increases Ventricular Fibrillation Risk as Identified by Emulation
title_short Slow Recovery of Excitability Increases Ventricular Fibrillation Risk as Identified by Emulation
title_sort slow recovery of excitability increases ventricular fibrillation risk as identified by emulation
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6121112/
https://www.ncbi.nlm.nih.gov/pubmed/30210355
http://dx.doi.org/10.3389/fphys.2018.01114
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