188 | Chapter 9 to assemble a genotypically homogeneous cohort of sufficient size, in order to perform statistically powered studies. Characterization of deformation patterns and subsequent riskprediction in a genotype-specific approach will only be feasible for disease causing variants with high prevalence and requires global multi-center collaborations, on a much larger scale than currently seen. Computer modelling of the disease substrate To characterize the disease substrate in cardiomyopathies, histology should be performed on tissue obtained by myocardial biopsy, autopsy or surgery. A definite diagnosis of ARVC is based on the presence of transmural fibro-fatty replacement of RV myocardium.10 Since histology is not available in the vast majority of patients, non-invasive imaging, integrated in the TFC, guides the diagnosis. In search of alternative ways to characterize the disease substrate on a cellular level, collaboration between engineers from Maastricht University and clinicians from the University Medical Center Utrecht led to the application of a computer model of the human heart and circulation, called CircAdapt (www.circadapt.org). In 2016, it was hypothesized that this model could be used for in silico myocardial disease substrate characterization at an individual patient level, based on generic simulations of the characteristic regional RV deformation abnormalities observed in ARVC patients and family members.6 Since the existing paradigm was that electrical abnormalities precede structural abnormalities in ARVC, it was expected that local electrical activation delay would be the cause of abnormal deformation patterns in early ARVC. Surprisingly, characteristic abnormal deformation patterns (type II and III) could be simulated by solely decreasing myocardial contractility and increasing passive stiffness in the model, challenging the existing paradigm. This modelling approach could be very useful to provide a form of “non-invasive biopsy” based on myocardial deformation data. Insight into the local disease substrate might have predictive value for disease progression and might refine arrhythmic risk stratification. In order to bring the modelling approach to clinical application, we wanted to move from generic simulations based on predefined parameter manipulations to a patient-specific approach based on a patient’s Digital Twin (Figure 1). Furthermore, we added local activation delay to the model to get an impression of the contribution of electrical versus mechanical substrates in ARVC. We first performed a feasibility study, in which we evaluated whether the model could simulate RV myocardial deformation of 10 individual patients by manipulating regional myocardial contractility and passive stiffness as published in 2016.11 Since the CircAdapt model contains many parameters with complex interactions, the focus on two pre-specified parameters in the RV lateral wall disregards many other potentially relevant model parameters. Therefore, we performed a sensitivity analysis to select the most important model parameters for accurate simulation of a patient’s myocardial deformation based on objective mathematical criteria.12 Starting from a total of 110 parameters representing vascular, valvular and myocardial tissue properties, the final subset included 23 parameter representing regional tissue contractility, compliance, activation delay and wall volume. Subsequently, as reported in Chapter 4, we applied this patient-specific modelling approach in the cohort of ARVC patients and family members which was used for the general simulations in 2016. The model indeed showed reduced regional contractile function and tissue compliance in carriers of a pathogenic genetic variant. A characteristic apex-to-base heterogeneity of tissue abnormalities was found, with the basal region of the RV free wall most affected. The next step was to estimate the disease substrate in a longitudinal study, in order to test reproducibility on repeated
RkJQdWJsaXNoZXIy MTk4NDMw