1. Parameter subset reduction for imaging-based digital twin generation of patients with left ventricular mechanical discoordination
- Author
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Tijmen Koopsen, Nick van Osta, Tim van Loon, Roel Meiburg, Wouter Huberts, Ahmed S. Beela, Feddo P. Kirkels, Bas R. van Klarenbosch, Arco J. Teske, Maarten J. Cramer, Geertruida P. Bijvoet, Antonius van Stipdonk, Kevin Vernooy, Tammo Delhaas, and Joost Lumens
- Subjects
Digital twin ,Mechanical discoordination ,Left bundle branch block ,Myocardial infarction ,Myocardial strain ,Sensitivity analysis ,Medical technology ,R855-855.5 - Abstract
Abstract Background Integration of a patient’s non-invasive imaging data in a digital twin (DT) of the heart can provide valuable insight into the myocardial disease substrates underlying left ventricular (LV) mechanical discoordination. However, when generating a DT, model parameters should be identifiable to obtain robust parameter estimations. In this study, we used the CircAdapt model of the human heart and circulation to find a subset of parameters which were identifiable from LV cavity volume and regional strain measurements of patients with different substrates of left bundle branch block (LBBB) and myocardial infarction (MI). To this end, we included seven patients with heart failure with reduced ejection fraction (HFrEF) and LBBB (study ID: 2018-0863, registration date: 2019–10–07), of which four were non-ischemic (LBBB-only) and three had previous MI (LBBB-MI), and six narrow QRS patients with MI (MI-only) (study ID: NL45241.041.13, registration date: 2013–11–12). Morris screening method (MSM) was applied first to find parameters which were important for LV volume, regional strain, and strain rate indices. Second, this parameter subset was iteratively reduced based on parameter identifiability and reproducibility. Parameter identifiability was based on the diaphony calculated from quasi-Monte Carlo simulations and reproducibility was based on the intraclass correlation coefficient ( $${ICC}$$ ICC ) obtained from repeated parameter estimation using dynamic multi-swarm particle swarm optimization. Goodness-of-fit was defined as the mean squared error ( $${{{\chi}}}^{{2}}$$ χ 2 ) of LV myocardial strain, strain rate, and cavity volume. Results A subset of 270 parameters remained after MSM which produced high-quality DTs of all patients ( $${{{\chi}}}^{{2}}$$ χ 2
- Published
- 2024
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