ArticleAnnals of biomedical engineering2026
A Whole-Heart Fluid-Structure Interaction Model for Transcatheter Mitral Valve Repair in Primary Mitral Regurgitation.
Article in Annals of biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposePrimary mitral regurgitation (MR) resulting from chordal rupture leads to acute volume overload and elevated left atrial pressure. Transcatheter edge-to-edge mitral valve repair (TMVr) devices can effectively reduce MR, but their impact on global cardiac mechanics and diastolic filling remains difficult to predict. This study develops a whole-heart FSI framework for TMVr by extending a previously developed whole-heart model to represent chordal rupture and edge-to-edge repair and to quantify their structural and hemodynamic consequences.
methodsA three-dimensional FSI model of the heart derived from cardiac CT imaging was used to simulate three conditions: healthy baseline, chordal rupture-induced MR, and post-TMVr. The immersed finite element-difference method enables fully coupled simulation of blood flow, valve dynamics, and myocardial contraction. Hemodynamic metrics, including regurgitant fraction (RF), left atrial pressure (LAP), stroke volume (SV), stroke work (SW), and pressure-volume relationships, were compared with clinical reference data.
resultsChordal rupture produced moderate-to-severe MR (RF = 38%) and doubled left atrial V-wave amplitude, consistent with clinical observations. After TMVr, the central regurgitant jet produced by chordal rupture was eliminated, RF decreased to 21%, and LAP returned to near-baseline levels. However, the double-orifice valve configuration generated dual high-velocity inflow jets and reduced diastolic filling.
conclusionThe repair effectively reduces regurgitation and atrial pressure while also influencing diastolic filling and ventricular output. These findings highlight the importance of evaluating both regurgitation reduction and inflow characteristics when assessing TMVr outcomes and demonstrate the potential of patient-specific computational modeling for procedural planning and optimization.
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