Sensitivity analysis of a strongly-coupled human-basedelectromechanical cardiac model: Effect of mechanical parameters onphysiologically relevant biomarkers

Abstract

The human heart beats as a result of multiscale nonlinear dynamics coupling subcellular to whole organ processes, achievingelectrophysiologically-driven mechanical contraction. Computational cardiac modelling and simulation have achieved a greatdegree of maturity, both in terms of mathematical models of underlying biophysical processes and the development of simulationsoftware.In this study, we present the detailed description of a human-based physiologically-based, and fully-coupled ventricularelectromechanical modelling and simulation framework, and a sensitivity analysis focused on its mechanical properties. Thebiophysical detail of the model, from ionic to whole-organ, is crucial to enable future simulations of disease and drug action. Keynovelties include the coupling of state-of-the-art human-based electrophysiology membrane kinetics, excitation–contraction andactive contraction models, and the incorporation of a pre-stress model to allow for pre-stressing and pre-loading the ventricles ina dynamical regime. Through high performance computing simulations, we demonstrate that 50% to 200%−1000% variationsin key parameters result in changes in clinically-relevant mechanical biomarkers ranging from diseased to healthy values inclinical studies. Furthermore mechanical biomarkers are primarily affected by only one or two parameters. Specifically, ejectionfraction is dominated by the scaling parameter of the active tension model and its scaling parameter in the normal direction(kort 2); the end systolic pressure is dominated by the pressure at which the ejection phase is triggered (Pej) and the complianceof the Windkessel fluid model (C); and the longitudinal fractional shortening is dominated by the fibre angle (φ) andkort 2.The wall thickening does not seem to be clearly dominated by any of the considered input parameters. In summary, this study presents in detail the description and implementation of a human-based coupled electromechanicalmodelling and simulation framework, and a high performance computing study on the sensitivity of mechanical biomarkers tokey model parameters. The tools and knowledge generated enable future investigations into disease and drug action on humanventricles