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Published on: January 16, 2019
Parameter subset reduction for patient-specific modelling of arrhythmogenic cardiomyopathy-related mutation carriers
Nick van Osta1, Aurore Lyon1, Feddo Kirkels2
1Department of Biomedical Engineering, Maastricht University CARIM School for Cardiovascular Diseases, Maastricht, Limburg, The Netherlands.
Insights
This study developed a computational framework to identify specific tissue abnormalities in arrhythmogenic cardiomyopathy (AC) patients. By reducing model parameters, it enables personalized identification of right ventricular issues for better clinical decision-making.
Area of Science:
- Computational biology
- Cardiovascular modeling
- Systems medicine
Background:
- Arrhythmogenic cardiomyopathy (AC) is an inherited heart disease causing dangerous arrhythmias and heart dysfunction.
- Patient-specific computational models offer potential for understanding AC progression and guiding clinical decisions.
- Personalizing models is difficult due to numerous interacting parameters.
Purpose of the Study:
- To create a framework for parameter reduction and estimation in computational models of AC.
- To identify patient-specific regional tissue abnormalities in the right ventricle (RV) of AC subjects.
- To link clinical measurements of RV deformation to underlying myocardial disease substrates.
Main Methods:
- Utilized an inverse modeling approach with the CircAdapt computational model.
- Combined Morris screening, quasi-Monte Carlo (qMC) simulations, and particle swarm optimization (PSO) for parameter reduction.
- Applied the framework to 15 genotype-positive AC subjects using clinical RV deformation data.
Main Results:
- Reduced 110 model parameters to a subset of 48 using Morris screening.
- Further reduced the parameter subset to 16 key properties using qMC and PSO.
- Identified parameters including regional contractility, passive stiffness, activation delay, and wall reference area.
Conclusions:
- The developed framework effectively reduces complexity for patient-specific AC modeling.
- This approach enables the identification of specific RV tissue abnormalities in AC patients.
- Facilitates a deeper understanding of AC pathophysiology and supports personalized treatment strategies.
Abstract:
Arrhythmogenic cardiomyopathy (AC) is an inherited cardiac disease, clinically characterized by life-threatening ventricular arrhythmias and progressive cardiac dysfunction. Patient-specific computational models could help understand the disease progression and may help in clinical decision-making. We propose an inverse modelling approach using the CircAdapt model to estimate patient-specific regional abnormalities in tissue properties in AC subjects. However, the number of parameters (n = 110) and their complex interactions make personalized parameter estimation challenging. The goal of this study is to develop a framework for parameter reduction and estimation combining Morris screening, quasi-Monte Carlo (qMC) simulations and particle swarm optimization (PSO). This framework identifies the best subset of tissue properties based on clinical measurements allowing patient-specific identification of right ventricular tissue abnormalities. We applied this framework on 15 AC genotype-positive subjects with varying degrees of myocardial disease. Cohort studies have shown that atypical regional right ventricular (RV) deformation patterns reveal an early-stage AC disease. The CircAdapt model of cardiovascular mechanics and haemodynamics has already demonstrated its ability to capture typical deformation patterns of AC subjects. We, therefore, use clinically measured cardiac deformation patterns to estimate model parameters describing myocardial disease substrates underlying these AC-related RV deformation abnormalities. Morris screening reduced the subset to 48 parameters. qMC and PSO further reduced the subset to a final selection of 16 parameters, including regional tissue contractility, passive stiffness, activation delay and wall reference area. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.
