An in‐silico twin for epicardial augmentation of the failing heart

Advances in ventricular assist device (VAD) technology for the treatment of end- stage congestive heart failure (CHF) are needed to cope with the increasing numbers of patients that cannot be provided with donor hearts for transplantation. We develop and investigate a novel extravascular VAD technology that provides biventricular, epicardial pressure support for the failing heart. This novel VAD concept avoids blood contact that is accompanied with typical complications such as coagula- tion and infections. To date, in-vivo porcine model results with a prototype of the implant exist, further studies to improve the implant’s performance and promote its applicability in humans are needed. In this contribution we present a personalised functional digital twin of the heart, the vascular system and the novel VAD technology in terms of a calibrated, cus- tomized computational model. The calibration procedure is based on patient-specific measurements and is performed by solving an inverse problem. This in-silico model is able to (i) confirm in-vivo experimental data, (ii) predict healthy and pathologic ventricular function, and (iii) assess the beneficial impact of the novel VAD concept to a high level of fidelity. The model shows very good agreement with in-vivo data and reliably predicts increases in stroke volume as well as left ventricular pressure with increasing ven- tricular support. Furthermore, the digital twin allows insight into quantities that are poorly or not at all amenable in any experimental setup. Conclusively, the model’s ability to link integral hemodynamic variables to local tissue mechanical deformation makes it a highly valuable tool for the dimensioning of novel VAD technologies and future treatment strategies in heart failure. The presented in-silico twin enhances in-vivo studies by facilitating the accessibility and increasing the range of quantities of interest. Due to its flexibility in the assess- ment of design variants and optimization loops, it may substantially contribute to a reduction of the amount of animal experiments in this and similar settings.