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

Brodie A Lawson1, Kevin Burrage1,2, Pamela Burrage1

  • 1ARC Centre of Excellence for Mathematical and Statistical Frontiers, School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia.

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This study reveals how ion channel recovery variability impacts cardiac rotor dynamics and fibrillation. Novel emulation methods explain pro-arrhythmic effects of certain drugs by identifying key determinants of cardiac arrhythmias.

Keywords:
Gaussian process regressionarrhythmias (cardiac)emulationexcitabilityfibrillationmachine learningrefractorinessrotors

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Area of Science:

  • Cardiac Electrophysiology
  • Computational Biology
  • Pharmacology

Background:

  • Rotor stability and meandering are crucial for cardiac fibrillation, impacting anti-arrhythmic drug development.
  • Understanding how cellular electrophysiology variability, especially ion channel recovery kinetics, affects rotor dynamics is limited.

Purpose of the Study:

  • To develop a novel emulation approach for analyzing re-entrant biomarkers in cardiac tissue.
  • To investigate the role of ion channel variability in modulating rotor-driven cardiac arrhythmias.

Main Methods:

  • Utilized Gaussian process regression and machine learning for data enrichment and analysis.
  • Conducted over 5,000 monodomain simulations, emulated to 80 million scenarios, using Fenton-Karma ionic dynamics.
  • Developed methods for automatic detection, classification, and analysis of re-entrant biomarkers.

Main Results:

  • Achieved up to 96% classification accuracy for excitation behavior prediction.
  • Emulation accurately predicted frequency, stability, and spatial biomarkers of functional re-entry.
  • Identified critical excitability windows as key determinants of rotor breakup and demonstrated the role of slow recovery of excitability in increasing arrhythmic risk.

Conclusions:

  • Mechanistically explained pro-arrhythmic effects of class Ic anti-arrhythmics in ventricles.
  • Linked drug action to slow recovery of excitability, incomplete slow inward current activation, and increased dispersion of repolarization.
  • Highlighted the potential of emulation techniques for uncovering novel arrhythmia mechanisms in cardiac electrophysiology.