Cai, L., Zhao, T., Wang, Y., Luo, X. and Gao, H. (2023) Fluid–structure interaction simulation of pathological mitral valve dynamics in a coupled mitral valve-left ventricle model. Intelligent Medicine, 3(2), pp. 104-114. (doi: 10.1016/j.imed.2022.06.005)
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Abstract
Background: Understanding the interaction between the mitral valve (MV) and the left ventricle (LV) is very important in assessing cardiac pump function, especially when the MV is dysfunctional. Such dysfunction is a major medical problem owing to the essential role of the MV in cardiac pump function. Computational modelling can provide new approaches to gain insight into the functions of the MV and LV. Methods: In this study, a previously developed LV–MV model was used to study cardiac dynamics of MV leaflets under normal and pathological conditions, including hypertrophic cardiomyopathy (HOCM) and calcification of the valve. The coupled LV–MV model was implemented using a hybrid immersed boundary/finite element method to enable assessment of MV haemodynamic performance. Constitutive parameters of the HOCM and calcified valves were inversely determined from published experimental data. The LV compensation mechanism was further studied in the case of the calcified MV. Results: Our results showed that MV dynamics and LV pump function could be greatly affected by MV pathology. For example, the HOCM case showed bulged MV leaflets at the systole owing to low stiffness, and the calcified MV was associated with impaired diastolic filling and much-reduced stroke volume. We further demonstrated that either increasing the LV filling pressure or increasing myocardial contractility could enable a calcified valve to achieve near-normal pump function. Conclusion: The modelling approach developed in this study may deepen our understanding of the interactions between the MV and the LV and help in risk stratification of heart valve disease and in silico treatment planning by exploring intrinsic compensation mechanisms.
Item Type: | Articles |
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Additional Information: | Funding: This work was supported by the National Natural Science Foundation of China (Grant No.11871399) and the UK EPSRC (Grant Nos. EP/S030875, EP/S014284/1, EP/S020950/1, EP/R511705/1, and EP/T017899/1). |
Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Luo, Professor Xiaoyu and Gao, Dr Hao |
Creator Roles: | |
Authors: | Cai, L., Zhao, T., Wang, Y., Luo, X., and Gao, H. |
College/School: | College of Science and Engineering > School of Mathematics and Statistics > Mathematics |
Journal Name: | Intelligent Medicine |
Publisher: | Elsevier |
ISSN: | 2667-1026 |
Published Online: | 02 August 2022 |
Copyright Holders: | Copyright © 2023 The Authors |
First Published: | First published in Intelligent Medicine 3(2):104-114 |
Publisher Policy: | Reproduced under a Creative Commons license |
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