Variability in electrophysiological properties and conducting obstacles controls re-entry risk in heterogeneous ischaemic tissue

Abstract

Ischaemia, in which inadequate blood supply compromises and eventually kills regions of cardiac tissue, can cause many types of arrhythmia, some life-threatening. A significant component of this is the effects of the resulting hypoxia, and concomitant hyperklaemia and acidosis, on the electrophysiological properties of myocytes. Clinical and experimental data have also shown that regions of structural heterogeneity (fibrosis, necrosis, fibro-fatty infiltration) can act as triggers for arrhythmias under acute ischaemic conditions. Mechanistic models have successfully captured these effects in silico . However, the relative significance of these separate facets of the condition, and how sensitive arrhythmic risk is to the extents of each, is far less explored. In this work, we use partitioned Gaussian process emulation and new metrics for source-sink mismatch that rely on simulations of bifurcating cardiac fibres to interrogate a model of heterogeneous ischaemic tissue. Re-entries were most sensitive to the level of hypoxia and the fraction of non-excitable tissue. In addition, our results reveal both protective and pro-arrhythmic effects of hyperklaemia, and present the levels of hyperklaemia, hypoxia and percentage of non-excitable tissue that pose the highest arrhythmic risks. This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.

Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators

“…Gaussian process (GP) emulators, which provide a prediction and corresponding prediction uncertainty, can be effective emulators of complex computer models (Conti and O'Hagan, 2010). GP emulators have been used for sensitivity analysis (Chang et al, 2015;Coveney and Clayton, 2020) and history matching (Coveney and Clayton, 2018) of cardiac cell models, and for models of cardiac tissue (Dhamala et al, 2020;Lawson et al, 2020) and mechanics (Longobardi et al, 2020). Emulators are conditioned on precalculated simulator data, but since they can make predictions at new inputs they are ideal tools for MCMC.…”

Machine Learning Identification of Pro-arrhythmic Structures in Cardiac Fibrosis

“…As the focus of this study was purely on how much fibrotic structure itself can inform the risk of re-entry, we have not considered the importance of specific electrophysiological conditions for the initiation and sustainment of re-entrant activation patterns. Some examination of the effects of parameter variability in this context has already been carried out (Lawson et al, 2020), but it is a limitation of this study that we have not explicitly considered how different electrophysiological conditions impact the importance of structure vs. activation sequence or the ability to predict structures that selectively block. We suspect that if the conductivity of unobstructed tissue was adjusted, or a different cell model (or parameter values for the BOCF model) was used, the general conclusions we have drawn here would remain valid, but of course classifier models would need to be retrained.…”

Computational analysis of vulnerability to reentry in acute myocardial ischemia

“…This result agrees with Ferrero et al [16], who suggested that hypoxia could be the most proarrhythmic component of ischemia. In addition, Lawson et al [17] reported that the most critical factor in the onset of reentry is the wavelength (product between APD and CV), with hypoxia the primary determinant of this factor.…”