Use of Ventricular Assist Device in Univentricular Physiology: The Role of Lumped Parameter Models

Failing single‐ventricle ( SV ) patients might benefit from ventricular assist devices ( VADs ) as a bridge to heart transplantation. Considering the complex physiopathology of SV patients and the lack of established experience, the aim of this work was to realize and test a lumped parameter model of the cardiovascular system, able to simulate SV hemodynamics and VAD implantation effects. Data of 30 SV patients (10 N orwood, 10 G lenn, and 10 F ontan) were retrospectively collected and used to simulate patients' baseline. Then, the effects of VAD implantation were simulated. Additionally, both the effects of ventricular assistance and cavopulmonary assistance were simulated in different pathologic conditions on F ontan patients, including systolic dysfunction, diastolic dysfunction, and pulmonary vascular resistance increment. The model can reproduce patients' baseline well. Simulation results suggest that the implantation of VAD : (i) increases the cardiac output ( CO ) in all the three palliation conditions ( N orwood 77.2%, G lenn 38.6%, and F ontan 17.2%); (ii) decreases the SV external work ( SVEW ) ( N orwood 55%, G lenn 35.6%, and F ontan 41%); (iii) increases the mean pulmonary arterial pressure (Pap) ( N orwood 39.7%, G lenn 12.1%, and F ontan 3%). In Fontan circulation, with systolic dysfunction, the left VAD ( LVAD ) increases CO (35%), while the right VAD ( RVAD ) determines a decrement of inferior vena cava pressure ( P vci) (39%) with 34% increment of CO . With diastolic dysfunction, the LVAD increases CO (42%) and the RVAD decreases the P vci. With pulmonary vascular resistance increment, the RVAD allows the highest CO (50%) increment with the highest decrement of P vci (53%). The single ventricular external work ( SVEW ) increases (decreases) increasing the VAD speed in cavopulmonary (ventricular) assistance. Numeric models could be helpful in this challenging and innovative field to support patients and VAD selection to optimize the clinical outcome and personalize the therapy.