An in silico twin for epicardial augmentation of the failing heart

Abstract

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 coagulation 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, customized 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 (a) confirm in vivo experimental data, (b) predict healthy and pathologic ventricular function, and (c) 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 and left ventricular pressure with increasing ventricular 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. Because of its flexibility in the assessment of design variants and optimization loops, it may substantially contribute to a reduction of the amount of animal experiments in this and similar settings.

Digital Twins for Multiple Sclerosis

“…The use of DTs in medical care is still in its infancy. So far, only in a few areas of medicine, DTs were applied, such as oncology (97-99), geriatrics (100, 101), cardiology (45,(102)(103)(104)(105)(106), epidemic outbreaks (107), genomic medicine (48, 108), internal medicine (109,110), orthopedics (111) and vascular medicine (112,113). For example, Corral-Acero et al present early steps of a DT in the field of cardiovascular medicine by describing the synergies between mechanistic and statistical models, the pillars of the DT (45).…”

“…The use of DTs in medical care is still in its infancy. So far, only in a few areas of medicine, DTs were applied, such as oncology ( 97 – 99 ), geriatrics ( 100 , 101 ), cardiology ( 45 , 102 – 106 ), epidemic outbreaks ( 107 ), genomic medicine ( 48 , 108 ), internal medicine ( 109 , 110 ), orthopedics ( 111 ) and vascular medicine ( 112 , 113 ). For example, Corral-Acero et al.…”

The Future of Telemedicine in the Management of Heart Failure Patients

“…Another future development will be the digital patient twin. [ 43 , 44 ] This is an AI construct based on the patient clinical information integrated with registries-derived big data to produce a digital representation of a patient. The resultant digital patient twin can be used to virtually test the effect of different therapeutic options and predict the potential results, to optimise treatment choices and avoid side-effects.…”

“…The resultant digital patient twin can be used to virtually test the effect of different therapeutic options and predict the potential results, to optimise treatment choices and avoid side-effects. [ 43 , 44 ] These models can be enhanced with the huge amount of clinical data derived from the HF telemonitoring databases, with the purpose of a more accurate prediction of therapeutic interventions. [ 43 , 44 ]…”

“…[ 43 , 44 ] These models can be enhanced with the huge amount of clinical data derived from the HF telemonitoring databases, with the purpose of a more accurate prediction of therapeutic interventions. [ 43 , 44 ]…”