Abstract
Cardiac contraction is the result of integrated cellular, tissue and organ function. Biophysical in silico cardiac models offer a systematic approach for studying these multi-scale interactions. The computational cost of such models is high, due to their multi-parametric and nonlinear nature. This has so far made it difficult to perform model fitting and prevented global sensitivity analysis (GSA) studies. We propose a machine learning approach based on Gaussian process emulation of model simulations using probabilistic surrogate models, which enables model parameter inference via a Bayesian history matching (HM) technique and GSA on whole-organ mechanics. This framework is applied to model healthy and aortic-banded hypertensive rats, a commonly used animal model of heart failure disease. The obtained probabilistic surrogate models accurately predicted the left ventricular pump function (R2 = 0.92 for ejection fraction). The HM technique allowed us to fit both the control and diseased virtual bi-ventricular rat heart models to magnetic resonance imaging and literature data, with model outputs from the constrained parameter space falling within 2 SD of the respective experimental values. The GSA identified Troponin C and cross-bridge kinetics as key parameters in determining both systolic and diastolic ventricular function.This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.
Highlights
With each beat, cardiac myocytes generate tension and relax
We propose a machine learning approach based on Gaussian process emulation of model simulations using probabilistic surrogate models, which enables model parameter inference via a Bayesian history matching (HM) technique and Global sensitivity analysis (GSA) on whole-organ mechanics
By providing bounds on model parameters, the HM technique is compatible with verification, validation and uncertainty quantification (VVUQ) methods [26] supported by the ASME V&V40 standards and the FDA. 1
Summary
Cardiac myocytes generate tension and relax. Cellular tension is transduced into a coordinated, global whole-heart deformation resulting in an effective, system-level pump function. Different techniques have been employed for global parameter inference on cardiac cell models, including gradient descent [2], genetic algorithms [3], multivariate regression [4] and Markov chain Monte Carlo (MCMC) [5]. Most of these approaches are computationally intensive, and the computation burden is even higher when going from single to multiple scales. Additional details can be found in the electronic supplementary material
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