Design of epicardial restraints for optimized passive filling of the right ventricle

In silico trials can evaluate new therapeutic methods before clinical testing. In this study, we propose a computational pipeline to develop personalized digital twins of cardiac ventricles, enabling a robust characterization of ventricular passive behavior with a focus on the right ventricle (RV). Our framework was employed to simulate the hearts of three patients with RV failure. The estimation of biomechanical properties, coupled with finding the unloaded ventricular shapes, was performed by minimizing the deviation between simulated and target RV end-diastolic pressure-volume relationships (EDPVRs). Finite element analysis (FEA) based on medical imaging was used to model the ventricles and virtually add an epicardial restraint to the right ventricular free wall (RVFW). We defined two formulations for the target restrained RV EDPVRs, namely exponential and proportional volume reductions. Bayesian optimization was utilized for both material estimation and constraint design across a wide range of biomaterials and desired RV volume reduction ratios. Sensitivity analysis indicated that increasing restraint thickness or stiffness shifts the RV EDPVR leftward, although this effect plateaued beyond a specific curve. An excellent RV EDPVR fit was achieved with the optimal restraint, particularly for the exponentially shifted targets. The optimal thickness increased as the desired reduction ratio rose, corresponding to decreased fiber stress and strain in the RVFW. These findings indicate that patient-specific design of epicardial restraints may enable more precise RV therapies.