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’.
Highlights
Arrhythmogenic cardiomyopathy (AC) is an inherited cardiomyopathy, clinically characterized by the occurrence of ventricular arrhythmias, sudden cardiac death (SCD) and predominantly right ventricular (RV) dysfunction [1,2]
Though this approach is more widely applicable, we focus on the identification of the best subset of model parameters essential to accurately simulate patient-specific RV deformation using the CircAdapt model
Using quasi-Monte Carlo (qMC) and Particle swarm optimization (PSO), the subset was further reduced to 16 parameters, including local contractility, passive stiffness, activation delay and wall reference area of the RV free wall (RVfw), as well as cardiac output, heart rate, AV-delay and relative systole duration
Summary
Arrhythmogenic cardiomyopathy (AC) is an inherited cardiomyopathy, clinically characterized by the occurrence of ventricular arrhythmias, sudden cardiac death (SCD) and predominantly right ventricular (RV) dysfunction [1,2]. A pathogenic genetic mutation, mostly affecting desmosomal genes, is found in up to 60% of probands This mutation may result in the fibro-fatty replacement of the myocardium, which can be a substrate for life-threatening arrhythmias and may already occur in an early stage without overt signs of disease using conventional screening tools [3]. The diagnosis of AC is based on a set of criteria described in the revised 2010 Task Force Criteria (TFC), with the electrocardiogram (ECG) and cardiac imaging as central elements [4] By using these conventional screening tools, mutation carriers can be classified into three categories: (i) a concealed stage with no abnormalities, (ii) an electrical stage with electrical abnormalities, but no structural abnormalities, and (iii) a structural stage with both electrical and structural abnormalities. Conventional electrocardiographic and structural imaging methods as described in the 2010 TFC are specific but may lack sensitivity to detect the early-stage AC-related myocardial disease in genotype-positive AC patients and family members [5]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
