Uncertainty Quantification in Simulations of Myocardial Ischemia

Jake Bergquist, Brian Zenger, Lindsay Rupp, Akil Narayan, Rob MacLeod
University of Utah


Myocardial ischemia is a complex pathophysiological event that can lead to fatal complications such as sudden cardiac death within minutes of an ischemic episode. Computational models of myocardial ischemia used to better understand its development are parametrized using assumptions of tissue properties and physiological values such as conductivity ratios in cardiac tissue, and conductivity changes between healthy and ischemic tissues. Understanding the effect of uncertainty in these parameter selections would provide useful insight in the performance and variability of the modeling outputs. Recently developed uncertainty quantification tools allow for the application of polynomial chaos expansion uncertainty quantification to such bioelectric models in order to parsimoniously examine the model response to input uncertainty. We applied uncertainty quantification to examine reconstructed extracellular potentials from the cardiac passive bidomain based on variation in the conductivity values for the ischemic tissue. We investigated the model response in both a synthetic dataset with simulated ischemic regions, as well as a dataset with ischemic regions derived from experimental recordings. We found that extracellular longitudinal and intracellular longitudinal conductivities had the predominant effect on simulation output, with the highest standard deviations in regions of extracellular potential elevations. We found that transverse conductivity values had almost no effect on model output. These findings suggest that uncertainty in the transverse conductivity will not affect model output, while changes in longitudinal conductivities do affect the solution. The results of this study may help inform more accurate and compact models of myocardial ischemia.