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Selected Publication:
SHR Neuro Cancer Cardio Lipid Metab Microb
Strocchi, M; Augustin, CM; Gsell, MAF; Rinaldi, CA; Vigmond, EJ; Plank, G; Oates, CJ; Wilkinson, RD; Niederer, SA.
Integrating imaging and invasive pressure data into a multi-scale whole-heart model.
J Biomech Eng. 2025; 1-52.
Doi: 10.1115/1.4069497
PubMed
FullText
FullText_MUG
- Co-authors Med Uni Graz
- Augustin Christoph
- Gsell Matthias
- Plank Gernot
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- Abstract:
- 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. Personalised 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, multi-scale, 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 (8.2 vs 8.1mm) and the ED and ES configurations against the CT images. The model replicated the measured acute haemodynamic response to biventricular pacing (simulated: 222mmHg/s vs clinical: 213+/-65mmHg/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.