Monitoring of Myocardial Involvement in ARVC | 107 Computational simulations To gain more insight into the course of myocardial disease development in individual patients, we created Digital Twins of the patient’s heart at each follow-up, using our previously developed modelling framework.23 In brief, this framework uses the patient’s imaging data to personalize the well-established closed-loop CircAdapt model of the human heart and circulation.27 This results in a series of patient-specific simulations of regional cardiac mechanics and global hemodynamics for each patient. Besides LV and RV deformation data, this framework uses LV end diastolic volume, LVEF and RV end-diastolic diameter as input. Taking measurement uncertainty into account, the estimated tissue properties are represented as a probability distribution. Three myocardial tissue properties were estimated for each RV segment: contractility, compliance, and mechanical activation delay. Given the interindividual differences in biometrics and loading conditions, we focused on regional heterogeneity of estimated myocardial tissue properties rather than on absolute values. In brief, segmental contractility was defined as the maximum rate of active stress rise, which can be seen as the local tissue level equivalent of the maximum rate of ventricular systolic pressure rise (dP/dtmax). Segmental wall compliance was defined as the slope of the end-diastolic myofiber stress–strain relationship at time before first ventricular activation and can be interpreted as the regional tissue equivalent of the slope of the enddiastolic pressure-volume relation. Mechanical activation delay was defined as the time interval between the models intrinsic time of activation and the onset of local active stress development.23 Statistical analysis Statistical analyses were performed using IBM SPSS 26.0 (IBM Corp, Armonk, NY, USA) and Stata SE 16.1 (StataCorp LLC, TX, USA). Values were expressed as mean with standard deviation (SD) or, median with interquartile range (IQR), as appropriate, and were compared by Fisher’s exact test for dichotomous variables and Kruskal Wallis test for continuous variables. After visually excluding the possibility of non-linear trends, we assessed progression in the three age-groups by entering the echocardiographic deformation parameters into a linear mixed model regression with exchangeable covariance structure and random effects at individual level. P-values were two-sided, and values <0.05 were considered significant. RESULTS Clinical characteristics We included 82 early-stage ARVC patients and genotype positive family members (57% female, age 39 ± 17 years, 10% probands, Table 1). Of 192 eligible patients, 110 were excluded due to VA or major structural TFC at or prior to first echocardiographic examination, or absence of a follow-up examination (Supplemental Figure 1). A (likely-)pathogenic variant was found in 92% of patients, mostly located in the PKP2 gene (84%). During a mean follow-up time of 6.7 ± 3.3 years, 355 echocardiograms were performed (average of 4 exams per patient, range 2 – 9). Forty-two exams were excluded due to inadequate visualization of one or more RV free wall segments (n=33), unavailability of raw echo data for deformation analysis (n=7), or irregular heart rhythm (n=2), leaving 313 exams appropriate for analysis. 6
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