Significant mitral regurgitation (MR) is a common ailment among patients with severe aortic stenosis (AS), and the management of these patients is challenging because the aortic and mitral valves are interdependent. Transcatheter aortic valve replacement (TAVR) can significantly reduce AS symptoms in high-risk patients; however, this procedure displaces the aortic-mitral curtain delineating the two valves, and may consequently have unexpected and adverse effects on mitral valve function. Studies have shown that MR severity is improved in approximately 50% of patients following TAVR, but unchanged or even worsened in others. The objective of this study is to develop a coupled mitral-aortic-left ventricle (LV) computational modeling framework to investigate the biomechanics involved in TAVR in patients with concomitant MR, in order to improve our understanding of mitral-aortic coupling and predict the effects of TAVR on MR severity. To achieve this goal, mitral-aortic-LV coupling computational models will be developed to investigate pre-TAVR hemodynamics, and validated against clinical 3D echocardiogram (3DE) and Cardiac Magnectic Resonance (CMR) image data (Aim 1). The models will be used to simulate TAVR device deployment to investigate the impacts of TAVR on the mitral valve and MR severity, and validated against post-TAVR 3DE and CMR data (Aim 2). Through parametric study, the optimal TAVR device and deployment strategies to improve patient outcomes will be investigated through the validated computational methods of Aims 1 and 2 (Aim 3). The results from this study may provide scientific rationale to improve the current clinical decision-making process, including patient and TAVR device selection, TAVR device positioning and deployment strategy, and the selection of intervention methods for MR improvement.

Public Health Relevance

This research will develop a computational framework to investigate the biomechanics of mitral-aortic coupling and predict the effects of transcatheter aortic valve replacement on mitral regurgitation severity.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21HL127570-01A1
Application #
9112314
Study Section
Bioengineering, Technology and Surgical Sciences Study Section (BTSS)
Program Officer
Evans, Frank
Project Start
2016-06-01
Project End
2018-05-31
Budget Start
2016-06-01
Budget End
2017-05-31
Support Year
1
Fiscal Year
2016
Total Cost
$210,763
Indirect Cost
$68,776
Name
Georgia Institute of Technology
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
097394084
City
Atlanta
State
GA
Country
United States
Zip Code
30318
Liu, Minliang; Liang, Liang; Sun, Wei (2018) Estimation of in vivo mechanical properties of the aortic wall: A multi-resolution direct search approach. J Mech Behav Biomed Mater 77:649-659
Mao, Wenbin; Wang, Qian; Kodali, Susheel et al. (2018) Numerical Parametric Study of Paravalvular Leak Following a Transcatheter Aortic Valve Deployment Into a Patient-Specific Aortic Root. J Biomech Eng 140:
Madukauwa-David, Immanuel David; Pierce, Eric L; Sulejmani, Fatiesa et al. (2018) Suture dehiscence and collagen content in the human mitral and tricuspid annuli. Biomech Model Mechanobiol :
Liu, Minliang; Liang, Liang; Liu, Haofei et al. (2018) On the computation of in vivo transmural mean stress of patient-specific aortic wall. Biomech Model Mechanobiol :
Kong, Fanwei; Pham, Thuy; Martin, Caitlin et al. (2018) Finite Element Analysis of Tricuspid Valve Deformation from Multi-slice Computed Tomography Images. Ann Biomed Eng 46:1112-1127
Caballero, Andrés; Mao, Wenbin; McKay, Raymond et al. (2018) New insights into mitral heart valve prolapse after chordae rupture through fluid-structure interaction computational modeling. Sci Rep 8:17306
Liang, Liang; Liu, Minliang; Martin, Caitlin et al. (2018) A machine learning approach as a surrogate of finite element analysis-based inverse method to estimate the zero-pressure geometry of human thoracic aorta. Int J Numer Method Biomed Eng :e3103
Kong, Fanwei; Pham, Thuy; Martin, Caitlin et al. (2018) Finite element analysis of annuloplasty and papillary muscle relocation on a patient-specific mitral regurgitation model. PLoS One 13:e0198331
Pokutta-Paskaleva, Anastassia; Sulejmani, Fatiesa; DelRocini, Marissa et al. (2018) Comparative mechanical, morphological, and microstructural characterization of porcine mitral and tricuspid leaflets and chordae tendineae. Acta Biomater :
Liang, Liang; Liu, Minliang; Martin, Caitlin et al. (2017) A machine learning approach to investigate the relationship between shape features and numerically predicted risk of ascending aortic aneurysm. Biomech Model Mechanobiol 16:1519-1533

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