Predicting left ventricular contractile function via Gaussian process emulation in aortic-banded rats

• Published in: Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences Vol. 378; no. 2173; p. 20190334
• Main Authors: Longobardi, S, Lewalle, A, Coveney, S, Sjaastad, I, Espe, E K S, Louch, W E, Musante, C J, Sher, A, Niederer, S A
• Format: Journal Article
• Language: English
• Published: England Royal Society Publishing 12.06.2020; The Royal Society Publishing
• Subjects: three-dimensional bi-ventricular model; Gaussian process; global sensitivity analysis; aortic-banded rat; history matching
• ISSN: 1364-503X; 1471-2962
• DOI: 10.1098/rsta.2019.0334

Cardiac contraction is the result of integrated cellular, tissue and organ function. Biophysical 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 (  = 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'.