Electromechanical substrate characterization in arrhythmogenic cardiomyopathy using imaging-based patient-specific computer simulations

Abstract

Aims Arrhythmogenic cardiomyopathy (AC) is an inherited cardiac disease, characterized by life-threatening ventricular arrhythmias and progressive cardiac dysfunction. The aim of this study is to use computer simulations to non-invasively estimate the individual patient’s myocardial tissue substrates underlying regional right ventricular (RV) deformation abnormalities in a cohort of AC mutation carriers. Methods and results In 68 AC mutation carriers and 20 control subjects, regional longitudinal deformation patterns of the RV free wall (RVfw), interventricular septum (IVS), and left ventricular free wall (LVfw) were obtained using speckle-tracking echocardiography. We developed and used a patient-specific parameter estimation protocol based on the multi-scale CircAdapt cardiovascular system model to create virtual AC subjects. Using the individual’s deformation data as model input, this protocol automatically estimated regional RVfw and global IVS and LVfw tissue properties. The computational model was able to reproduce clinically measured regional deformation patterns for all subjects, with highly reproducible parameter estimations. Simulations revealed that regional RVfw heterogeneity of both contractile function and compliance were increased in subjects with clinically advanced disease compared to mutation carriers without clinically established disease (17 ± 13% vs. 8 ± 4%, P = 0.01 and 18 ± 11% vs. 10 ± 7%, P < 0.01, respectively). No significant difference in activation delay was found. Conclusion Regional RV deformation abnormalities in AC mutation carriers were related to reduced regional contractile function and tissue compliance. In clinically advanced disease stages, a characteristic apex-to-base heterogeneity of tissue abnormalities was present in the majority of the subjects, with most pronounced disease in the basal region of the RVfw.

Uncertainty Quantification of Regional Cardiac Tissue Properties in Arrhythmogenic Cardiomyopathy Using Adaptive Multiple Importance Sampling

“…We used echocardiographic data of nine pathogenic AC mutation carriers which were evaluated in the University Medical Center Utrecht, Netherlands. As previously described ( van Osta et al, 2021 ), deformation analyses of these echocardiograms were performed twice by two observers to determine clinical inter- and intra-observer variability. Lastly, longitudinal datasets with >2 echocardiograms per patient at different time points were used to explore applicability of the model for follow-up of tissue properties over time.…”

“…This is typically the most affected area in AC mutation carriers ( Marcus et al, 2010 ), which is expressed in typical deformation abnormalities (delayed onset of shortening, decreased peak systolic strain, post-systolic shortening, and increased RV mechanical dispersion) ( Kirkels et al, 2021 ). Therefore, deformation patterns of three RVfw segments (apical, mid-ventricular, and basal) were used as input for our modeling framework ( Figure 1 ) ( van Osta et al, 2021 ). Additionally, LV free wall (LVfw) and interventricular septal (IVS) deformation patterns were included to ensure realistic mechanical boundary conditions for the RVfw in terms of ventricular interaction.…”

“…The parameter subset θ included for estimation was based on a previous sensitivity analysis ( van Osta et al, 2021 ) and is shown in Table 1 . Parameters included were regional parameters describing the constitutive behavior of active (SfAct) and passive stress (k1), activation delay (dT), reference wall area (AmRef), and global parameters relative systole duration (RSD), and CO. Heart rate (HR) in the model was set to match clinically measured HR to ensure equal cycle lengths in measured and modeled signals.…”

“…Various attempts to generate a digital twin of the human heart have been made ( Corral-Acero et al, 2020 ). Previously, we proposed a framework to create a digital twin ( van Osta et al, 2020 ) for quantification of the disease substrate underlying abnormal tissue deformation in patients with arrhythmogenic cardiomyopathy (AC) ( van Osta et al, 2021 ).…”

“…Therefore, early disease detection is important. We previously determined an in silico disease substrate with decreased regional RV contractility and compliance, with the potential to predict disease progression on a patient-specific level ( van Osta et al, 2021 ). This method was, however, not able to include uncertainty present in both measurement and model.…”