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Lookup NU author(s): Professor Chris OatesORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Copyright © 2026 by ASME; reuse license CC-BY 4.0.Cardiovascular diseases are the leading cause of death. Clinical data used to decide treatment are hard to integrate and interpret, making optimal treatment selection difficult. Personalized models can be used to integrate clinical data into a physics and physiology-constrained framework, but their clinical application faces limitations due to complex calibration and validation. In this study, we present a novel systematic calibration method for a whole-heart, multiscale, electromechanics model using emulators, sensitivity analysis, and history matching. Using cardiac motion derived from ECG-gated computed tomography (CT) and invasive left ventricular (LV) pressure data, we calibrated 25 model parameters to match the LV end-diastolic (ED) and peak pressure, ED and end-systolic (ES) volumes (EDV and ESV), right ventricle EDV, and the left atrium EDV, ESV, and the maximum volume during venous return. After calibration, all features were fit within [0.8, 10.8]% of the mean target value, and fell within 1.4 experimental standard deviations from the target values. We validated the model by comparing CT-derived and simulated atrioventricular plane displacement (AVPD) (8.2 versus 8.1 mm) and the ED and ES configurations against the CT images. The model replicated the measured acute hemodynamic response to biventricular (BIV) pacing (simulated: 222 mmHg/s versus clinical: 213±65 mmHg/s). This study provides a systematic method to integrate clinical data into a whole-heart, multiscale electromechanics framework. The validation shows that the model replicates local heart motion and response to therapy, demonstrating potential in assisting clinical decision-making.
Author(s): Strocchi M, Augustin CM, Gsell MAF, Rinaldi CA, Vigmond EJ, Plank G, Oates CJ, Wilkinson RD, Niederer SA
Publication type: Article
Publication status: Published
Journal: Journal of Biomechanical Engineering
Year: 2026
Volume: 148
Issue: 5
Print publication date: 01/05/2026
Online publication date: 27/11/2025
Acceptance date: 08/08/2025
Date deposited: 09/12/2025
ISSN (print): 0148-0731
ISSN (electronic): 1528-8951
Publisher: The American Society of Mechanical Engineers
URL: https://doi.org/10.1115/1.4069497
DOI: 10.1115/1.4069497
Data Access Statement: The datasets generated and supporting the findings of this article are obtainable from the corresponding author upon reasonable request.
PubMed id: 40847594
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