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Article

An Integrated Workflow for Building Digital Twins of Cardiac Electromechanics—A Multi-Fidelity Approach for Personalising Active Mechanics

1
Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging—Division of Biophysics, Medical University Graz, 8010 Graz, Austria
2
NAWI Graz, Institute of Mathematics and Scientific Computing, University of Graz, 8010 Graz, Austria
3
BioTechMed-Graz, 8010 Graz, Austria
*
Author to whom correspondence should be addressed.
Academic Editor: Fernando Simoes
Mathematics 2022, 10(5), 823; https://doi.org/10.3390/math10050823
Received: 31 January 2022 / Revised: 24 February 2022 / Accepted: 28 February 2022 / Published: 4 March 2022
Personalised computer models of cardiac function, referred to as cardiac digital twins, are envisioned to play an important role in clinical precision therapies of cardiovascular diseases. A major obstacle hampering clinical translation involves the significant computational costs involved in the personalisation of biophysically detailed mechanistic models that require the identification of high-dimensional parameter vectors. An important aspect to identify in electromechanics (EM) models are active mechanics parameters that govern cardiac contraction and relaxation. In this study, we present a novel, fully automated, and efficient approach for personalising biophysically detailed active mechanics models using a two-step multi-fidelity solution. In the first step, active mechanical behaviour in a given 3D EM model is represented by a purely phenomenological, low-fidelity model, which is personalised at the organ scale by calibration to clinical cavity pressure data. Then, in the second step, median traces of nodal cellular active stress, intracellular calcium concentration, and fibre stretch are generated and utilised to personalise the desired high-fidelity model at the cellular scale using a 0D model of cardiac EM. Our novel approach was tested on a cohort of seven human left ventricular (LV) EM models, created from patients treated for aortic coarctation (CoA). Goodness of fit, computational cost, and robustness of the algorithm against uncertainty in the clinical data and variations of initial guesses were evaluated. We demonstrate that our multi-fidelity approach facilitates the personalisation of a biophysically detailed active stress model within only a few (2 to 4) expensive 3D organ-scale simulations—a computational effort compatible with clinical model applications. View Full-Text
Keywords: patient-specific modelling; human left ventricular function; cardiac mechanics; precision medicine; parameter estimation; global sensitivity analysis patient-specific modelling; human left ventricular function; cardiac mechanics; precision medicine; parameter estimation; global sensitivity analysis
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MDPI and ACS Style

Jung, A.; Gsell, M.A.F.; Augustin, C.M.; Plank, G. An Integrated Workflow for Building Digital Twins of Cardiac Electromechanics—A Multi-Fidelity Approach for Personalising Active Mechanics. Mathematics 2022, 10, 823. https://doi.org/10.3390/math10050823

AMA Style

Jung A, Gsell MAF, Augustin CM, Plank G. An Integrated Workflow for Building Digital Twins of Cardiac Electromechanics—A Multi-Fidelity Approach for Personalising Active Mechanics. Mathematics. 2022; 10(5):823. https://doi.org/10.3390/math10050823

Chicago/Turabian Style

Jung, Alexander, Matthias A.F. Gsell, Christoph M. Augustin, and Gernot Plank. 2022. "An Integrated Workflow for Building Digital Twins of Cardiac Electromechanics—A Multi-Fidelity Approach for Personalising Active Mechanics" Mathematics 10, no. 5: 823. https://doi.org/10.3390/math10050823

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