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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\u2013flow (H\u2013Q) 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\u20130D models reproduced available clinical targets with a mean error of 8.6%, enabling controlled\n                    <jats:italic>in silico<\/jats:italic>\n                    interrogation of valve repair strategies. In the patient-specific state, simulated mitral and aortic regurgitant volumes were 6.6 and 6.5\u00a0mL per cycle, yielding a forward cardiac output of 3.16\u00a0L\/min despite an LVAD flow of 3.7\u00a0L\/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\u00a0L\/min, respectively; however, aortic valve opening and increased aortic pressure pulsatility (up to 38.9 vs. 13.5\u00a0mmHg) 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\u2014such as aortic pulsatility, RV afterload, and chamber-level flow patterns.\n                  <\/jats:p>","DOI":"10.1007\/s10237-026-02063-9","type":"journal-article","created":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T07:34:14Z","timestamp":1780472054000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A monolithic patient-specific 3D\u20130D model for In silico investigation of hemodynamics in patients with left ventricular assist devices"],"prefix":"10.1007","volume":"25","author":[{"given":"Mia","family":"Bonini","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marc","family":"Hirschvogel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Ferguson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francis","family":"Pagani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul C.","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Nordsletten","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,3]]},"reference":[{"key":"2063_CR1","unstructured":"3D Slicer: (2024) A multi-platform, free and open source software package for visualization and image analysis. https:\/\/www.slicer.org\/"},{"key":"2063_CR2","doi-asserted-by":"crossref","unstructured":"Abart T, Grujic M, Aigner P, R\u00f6hrich M, Jakubek S, Zimpfer D, Granegger M (2025) Accuracy of the heartmate 3 left ventricular assist device flow estimation. 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