A Computational Pipeline for Patient-Specific Prediction of the Postoperative Mitral Valve Functional State.

Autor:	Liu H; James T. Willerson Center for Cardiovascular Modeling and Simulation, The Oden Institute for Computational Engineering and Sciences, Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712-1229., Simonian NT; James T. Willerson Center for Cardiovascular Modeling and Simulation, The Oden Institute for Computational Engineering and Sciences, Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712-1229., Pouch AM; Departments of Radiology and Bioengineering, University of Pennsylvania, Philadelphia, PA 19104., Iaizzo PA; Visible Heart Laboratories, Department of Surgery, University of Minnesota, Minneapolis, MN 55455., Gorman JH; Gorman Cardiovascular Research Group, Department of Surgery, Smilow Center for Translational Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104., Gorman RC; Gorman Cardiovascular Research Group, Department of Surgery, Smilow Center for Translational Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104., Sacks MS; James T. Willerson Center for Cardiovascular Modeling and Simulation, The Oden Institute for Computational Engineering and Sciences, Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712-1229.
Jazyk:	angličtina
Zdroj:	Journal of biomechanical engineering [J Biomech Eng] 2023 Nov 01; Vol. 145 (11).
DOI:	10.1115/1.4062849
Abstrakt:	While mitral valve (MV) repair remains the preferred clinical option for mitral regurgitation (MR) treatment, long-term outcomes remain suboptimal and difficult to predict. Furthermore, pre-operative optimization is complicated by the heterogeneity of MR presentations and the multiplicity of potential repair configurations. In the present work, we established a patient-specific MV computational pipeline based strictly on standard-of-care pre-operative imaging data to quantitatively predict the post-repair MV functional state. First, we established human mitral valve chordae tendinae (MVCT) geometric characteristics obtained from five CT-imaged excised human hearts. From these data, we developed a finite-element model of the full patient-specific MV apparatus that included MVCT papillary muscle origins obtained from both the in vitro study and the pre-operative three-dimensional echocardiography images. To functionally tune the patient-specific MV mechanical behavior, we simulated pre-operative MV closure and iteratively updated the leaflet and MVCT prestrains to minimize the mismatch between the simulated and target end-systolic geometries. Using the resultant fully calibrated MV model, we simulated undersized ring annuloplasty (URA) by defining the annular geometry directly from the ring geometry. In three human cases, the postoperative geometries were predicted to 1 mm of the target, and the MV leaflet strain fields demonstrated close agreement with noninvasive strain estimation technique targets. Interestingly, our model predicted increased posterior leaflet tethering after URA in two recurrent patients, which is the likely driver of long-term MV repair failure. In summary, the present pipeline was able to predict postoperative outcomes from pre-operative clinical data alone. This approach can thus lay the foundation for optimal tailored surgical planning for more durable repair, as well as development of mitral valve digital twins. (Copyright © 2023 by ASME.)
Databáze:	MEDLINE
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