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Research Papers

Numerical Parametric Study of Paravalvular Leak Following a Transcatheter Aortic Valve Deployment Into a Patient-Specific Aortic Root

[+] Author and Article Information
Wenbin Mao, Qian Wang

Tissue Mechanics Laboratory,
The Wallace H. Coulter Department
of Biomedical Engineering,
Georgia Institute of Technology
and Emory University,
Atlanta, GA 30313-2412

Susheel Kodali

Division of Cardiology,
Columbia University Medical Center,
New York 10032

Wei Sun

Tissue Mechanics Laboratory,
The Wallace H. Coulter Department
of Biomedical Engineering,
Georgia Institute of Technology
and Emory University,
206 Technology Enterprise Park,
Georgia Institute of Technology,
387 Technology Circle,
Atlanta, GA 30313-2412
e-mail: wei.sun@bme.gatech.edu

1Corresponding author.

Manuscript received November 20, 2017; final manuscript received May 28, 2018; published online June 21, 2018. Assoc. Editor: Alison Marsden.

J Biomech Eng 140(10), 101007 (Jun 21, 2018) (11 pages) Paper No: BIO-17-1533; doi: 10.1115/1.4040457 History: Received November 20, 2017; Revised May 28, 2018

Paravalvular leak (PVL) is a relatively frequent complication after transcatheter aortic valve replacement (TAVR) with increased mortality. Currently, there is no effective method to pre-operatively predict and prevent PVL. In this study, we developed a computational model to predict the severity of PVL after TAVR. Nonlinear finite element (FE) method was used to simulate a self-expandable CoreValve deployment into a patient-specific aortic root, specified with human material properties of aortic tissues. Subsequently, computational fluid dynamics (CFD) simulations were performed using the post-TAVR geometries from the FE simulation, and a parametric investigation of the impact of the transcatheter aortic valve (TAV) skirt shape, TAV orientation, and deployment height on PVL was conducted. The predicted PVL was in good agreement with the echocardiography data. Due to the scallop shape of CoreValve skirt, the difference of PVL due to TAV orientation can be as large as 40%. Although the stent thickness is small compared to the aortic annulus size, we found that inappropriate modeling of it can lead to an underestimation of PVL up to 10 ml/beat. Moreover, the deployment height could significantly alter the extent and the distribution of regurgitant jets, which results in a change of leaking volume up to 70%. Further investigation in a large cohort of patients is warranted to verify the accuracy of our model. This study demonstrated that a rigorously developed patient-specific computational model can provide useful insights into underlying mechanisms causing PVL and potentially assist in pre-operative planning for TAVR to minimize PVL.

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Figures

Grahic Jump Location
Fig. 1

(a) Pre-TAVR aortic root geometry from CT scans used for FE simulations of TAV deployment and (b) post-TAVR geometry obtained from FE simulation results. TAV skirt and leaflets were added to accommodate CFD simulations.

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Fig. 2

Computational fluid dynamics mesh and boundary conditions. Physiological pressure waveforms were used at the LVOT and ascending aorta as the pressure outlet and pressure inlet boundary conditions, respectively. Lumped parameter model was used at each coronary outlet.

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Fig. 3

TAV models with (a) two different orientations: r1 and r2, (b) three different deployment heights: h1, h2, and h3, (c) three skirt shapes: s1, s2, and s3, and (d) brick stent and shell stent

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Fig. 4

(a) The representative coronary artery flow rate and pressure waveforms from the simulation. (b) PVL flow rate curve calculated from the simulation of brick-s1-r1-h2 model. The dotted line represents the pressure drop between the ascending aorta and LVOT.

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Fig. 5

(a) Paravalvular leak flow rate curves from three TAV models with different skirt shapes as shown in Fig. 3(c) and (b) velocity vector profiles in a vertical cross section illustrate the leaking flow through the gaps between the aortic root and TAV stent

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Fig. 6

Regurgitant velocity vectors of a vertical cross section from (a) the brick stent (brick-s1-r2-h3) model and (b) shell stent (shell-s1-r2-h3) model

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Fig. 7

Volume rendering of velocity fields from the models of (a) aligned orientation r1 (brick-s1-r1-h3 model), and (b) misaligned orientation r2 (brick-s1-r2-h3 model)

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Fig. 8

Volume rendering of velocity fields from the models with different deployment heights (a) higher than the optimum, h1 (brick-s1-r2-h1 model), (b) around the optimum, h2 (brick-s1-r2-h2 model), (c) lower than the optimum, h3 (brick-s1-r2-h3 model), and (d) corresponding PVL flow rate curves from five TAV models in Figs. 68

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Fig. 9

(a) Pressure distribution on the skirt with a regurgitant flow velocity profile to show the distribution of regurgitant jets from the brick-s1-r1-h3 model. (b) A corresponding FE simulation to show the skirt bending under a pressure load of 10 mmHg.

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