Personalized tricuspid valve modeling predicts regurgitation biomechanical repair and double‑orifice flow.

Tricuspid regurgitation (TR) is a prevalent and progressive condition associated with heart failure symptoms; however, numerous patients are unsuitable for surgery, and achieving a long-term outcome is challenging. This renders TR an unmet clinical problem. Computational modeling facilitates the evaluation of the biomechanics associated with regurgitation, aiming to enhance therapeutic strategies as transcatheter edge-to-edge repair. This study sought to establish a computational framework integrating a tricuspid valve (TV) model with the right ventricle (RV) wall to evaluate native valve function, TR pathology, and repair using the MitraClip device. Computed tomography angiography (CTA) from three patients was utilized to reconstruct the tricuspid valve leaflets, annulus, and RV wall. A parametric CAD pipeline for modeling the leaflet surfaces and chordal architecture was built using ex-vivo data. Annular kinematics (systole-to-diastole) were computed and then applied as a boundary condition in the finite-element analyses, with tricuspid regurgitation induced by displacing papillary positions and augmenting transvalvular loading. The MitraClip deployment was subsequently simulated whereas the post-deformation hemodynamics were assessed using a lattice-Boltzmann solver to quantify intraventricular velocity fields and vorticity. All three models demonstrated physiological leaflet coaptation and stress distributions in a healthy patient condition, with maximum stresses at end-systole quantified in the leaflet belly and chordal insertions. Post-MitraClip simulations demonstrated restored valve closure without retrograde flow during systole; however, flow exhibited a double-orifice inflow and localized vorticity near the clip during diastole. The jet orientation and downstream patterns depended on patient anatomy. This computational framework replicates TV biomechanics and post-repair hemodynamics from standard imaging, facilitating quantitative, patient-specific evaluation. The current patient-specific, image-based computational method can predict TV-related performance and post-repair hemodynamic outcomes, facilitating personalized planning to minimize residual TR and enhance device placement.