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

Abstract

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.

Comprehensive Uncertainty Quantification and Sensitivity Analysis for Cardiac Action Potential Models

“…Therefore, our results provide limited insight into the challenge of running large numbers of whole heart simulations to perform UQ for whole-heart simulation-based outputs. We expect that emulators of whole-heart models will need to be developed to overcome this challenge; see ongoing research (Chang et al, 2015; Ghosh et al, 2018; Lawson et al, 2018). Note that sensitivity indices were very similar for QOIs in 0D and 1D (Figure 5), as were output distributions (results not presented), suggesting that uninfluential parameters in single-cell simulations may be uninfluential in tissue simulations.…”

Sensitivity and Uncertainty Analysis of Two Human Atrial Cardiac Cell Models Using Gaussian Process Emulators

“…A detailed and hypothesis-driven mechanistic study was beyond the scope of the present work, but will be a valuable next step. Extending the use of emulators from models of cardiac cells to models of cardiac tissue is an important future direction, and initial studies are promising (Lawson et al, 2018). At present, tissue calculations are computationally expensive, especially for personalized meshes.…”

Combining Polynomial Chaos Expansions and Genetic Algorithm for the Coupling of Electrophysiological Models

“…Furthermore, the search process has to simulate the mathematical model several times and, due to it, the computational cost associated with this process becomes expensive. Trying to minimize it, the use of Emulations [10,11] based on Polynomial Chaos Expansion was tested in order to replace the mathematical model in the evaluation process of the GA.…”

“…One of the analyses that have been used in recent studies [8,10,11] to see how the parameters are associated with the biological behavior is Uncertainty Quantification (UQ). Since this analysis considers the existence of uncertain measures associated with the studied object, it provides some methods to quantify the impacts of uncertainty in these parameters values upon model outputs.…”