ArticleEuropean heart journal. Imaging methods and practice2026
Toward precision in simulation of paediatric mitral valve repair using patient-specific fluid-structure interaction modelling.
Article in European heart journal. Imaging methods and practice, 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
Aims: Assessment of mitral valve (MV) function and haemodynamics is essential for optimizing surgical repair in children with mitral regurgitation. Patient-specific fluid-structure interaction (FSI) modelling can capture the complex interplay between valvular mechanics and blood flow. In this study, we apply a patient-specific FSI framework to evaluate MV function and haemodynamics in paediatric patients before and after surgery. Methods and results: Seven paediatric patients with mitral regurgitation were analysed (age range: 2-17 years; median: 6 years; 57% female). Patient-specific MV apparatus geometries were segmented from pre- and postoperative 3D echocardiograms. Flow boundary conditions were derived from left ventricular volume measurements. Valve dynamics and haemodynamics were simulated using the FSI framework. Model performance was evaluated against echocardiographic data, pre- and postoperatively. The FSI model reproduced the angle of the regurgitant jet. Preoperatively, the regurgitation grade matched echocardiographic assessment in six of seven patients, and postoperatively in four of seven. The site of regurgitation was correctly identified in six of seven patients, pre- and postoperatively. The model reproduced the observed intraventricular flow patterns in most patients, and the simulated transvalvular pressure gradients agreed with Doppler measurements (mean difference: 0.38 ± 1.57 mmHg preoperatively, -0.42 ± 3.26 mmHg postoperatively). Conclusion: The proposed FSI framework captured MV function, haemodynamics, and disease-specific features in paediatric patients pre- and postoperatively, based on evaluation in one of the largest cohorts for the field. This computational framework has the potential to enable predictive simulations that could support surgical planning in the future and improve repair outcomes in children.
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