We present a fully coupled, patient-specific 3D-0D computational framework for hearts supported with left ventricular assist devices (LVAD) that enables controlled in silico experimentation. The approach monolithically integrates three-dimensional CFD of the left ventricle (LV), left atrium (LA), aortic root, and LVAD cannulae with a closed-loop 0D lumped parameter network of the full circulation. Mitral and aortic valve dynamics are governed by transvalvular pressure and flow with patient-specific regurgitant orifice areas, and the LVAD is represented via a pressure-flow (H-Q) relation. This manuscript provides the complete mathematical formulation, coupling strategy, and parameterization required to build a reproducible pipeline from dynamic CT, 2D transthoracic echocardiography, and right heart catheterization. This methodology is demonstrated in a patient under long-term support of LVAD and concomitant mitral and aortic regurgitation. The personalized, fully coupled 3D-0D models reproduced available clinical targets with a mean error of 8.6%, enabling controlled in silico interrogation of valve repair strategies. In the patient-specific state, simulated mitral and aortic regurgitant volumes were 6.6 and 6.5 mL per cycle, yielding a forward cardiac output of 3.16 L/min despite an LVAD flow of 3.7 L/min. In silico isolated mitral valve (MV) repair, isolated aortic valve (AV) repair, and combined MV+AV repair increased forward output to 3.41, 3.33, and 3.55 L/min, respectively; however, aortic valve opening and increased aortic pressure pulsatility (up to 38.9 vs. 13.5 mmHg) were observed only when MV repair was involved. These left-sided improvements propagated through the cardiopulmonary circulation, reducing pulmonary pressures and right ventricular loading, with the largest benefit observed following combined repair. We show that the modeling platform presented provides a powerful means to study mechanical circulatory support, enabling patient-specific evaluation of surgical interventions in patients with LVAD and delivering quantitative insight into clinically important metrics-such as aortic pulsatility, RV afterload, and chamber-level flow patterns.