Research Papers

In Silico Prediction of the Mechanobiological Response of Arterial Tissue: Application to Angioplasty and Stenting

[+] Author and Article Information
Colin J. Boyle, Alexander B. Lennon

Trinity Centre for Bioengineering, School of Engineering,  University of Dublin, Trinity College, Dublin, Ireland

Patrick J. Prendergast1

Trinity Centre for Bioengineering, School of Engineering,  University of Dublin, Trinity College, Dublin, Irelandpprender@tcd.ie


Corresponding author.

J Biomech Eng 133(8), 081001 (Aug 26, 2011) (10 pages) doi:10.1115/1.4004492 History: Received February 08, 2011; Revised June 03, 2011; Posted June 30, 2011; Published August 26, 2011; Online August 26, 2011

One way to restore physiological blood flow to occluded arteries involves the deformation of plaque using an intravascular balloon and preventing elastic recoil using a stent. Angioplasty and stent implantation cause unphysiological loading of the arterial tissue, which may lead to tissue in-growth and reblockage; termed “restenosis.” In this paper, a computational methodology for predicting the time-course of restenosis is presented. Stress-induced damage, computed using a remaining life approach, stimulates inflammation (production of matrix degrading factors and growth stimuli). This, in turn, induces a change in smooth muscle cell phenotype from contractile (as exists in the quiescent tissue) to synthetic (as exists in the growing tissue). In this paper, smooth muscle cell activity (migration, proliferation, and differentiation) is simulated in a lattice using a stochastic approach to model individual cell activity. The inflammation equations are examined under simplified loading cases. The mechanobiological parameters of the model were estimated by calibrating the model response to the results of a balloon angioplasty study in humans. The simulation method was then used to simulate restenosis in a two dimensional model of a stented artery. Cell activity predictions were similar to those observed during neointimal hyperplasia, culminating in the growth of restenosis. Similar to experiment, the amount of neointima produced increased with the degree of expansion of the stent, and this relationship was found to be highly dependant on the prescribed inflammatory response. It was found that the duration of inflammation affected the amount of restenosis produced, and that this effect was most pronounced with large stent expansions. In conclusion, the paper shows that the arterial tissue response to mechanical stimulation can be predicted using a stochastic cell modeling approach, and that the simulation captures features of restenosis development observed with real stents. The modeling approach is proposed for application in three dimensional models of cardiovascular stenting procedures.

Copyright © 2011 by American Society of Mechanical Engineers
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Figure 1

Schematic representation of restenosis development. Initially, quiescent SMCs occupy the vessel wall and an intact endothelium exists (a). Upon stenting, the plaque is plastically deformed, endothelium is denuded, vascular tissue is stretched and inflammation is initiated (b). In response, SMCs become synthetic, migratory, and proliferative, producing neointima (c), which may achieve homeostasis, provided the inflammation stimulus recedes and a layer of endothelial cells is re-established (d). Neointimal area is measured as the difference between the lumen area immediately poststenting and the lumen area upon follow-up.

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Figure 2

Flow chart outlining the computational method for the simulation of restenosis. The stress may be updated, depending on whether or not the new geometry significantly affects the structural behavior of the artery, and whether the injury stimulus is assumed to occur once in the initial expansion procedure, or is chronic.

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Figure 3

The neighboring points around a cell in an orthogonal lattice. In the left-most diagram, VN indicates the von Neumann neighborhood (shown in 3D in the central diagram), and M indicates the Moore neighborhood (shown in 3D in the right-most diagram).

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Figure 4

Stress versus remaining life (number of cycles to failure, Nf ) for porcine coronary artery. Crosses indicate the experimental data from McLoughlin [37]. The solid lines show the fitted bilinear model; the horizontal line indicates the fatigue strength, σ0  = 90 kPa, the y-intercept is the failure strength, σƒ  = 1417 kPa, and the slope, α = −187.

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Figure 5

The geometry of the idealised artery and stent (left). A cross-section of a symmetrically stenosed artery is shown with six stent struts. A 1/6 segment is considered due to symmetry and this model is meshed as shown on the right. A displacement is applied to the strut, and the upper and lower surfaces of the mesh are restrained circumferentially.

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Figure 6

Case (i): An initial stress is applied and removed. Damage is initiated abruptly, and decays exponentially to zero, while extracellular matrix decays exponentially to e = e|t =0 – D|t =0 . Case (ii): A constant stress is prescribed, leading to complete ECM removal at a rate dependant on the stress applied. Case (iii): A decaying stress is prescribed. Case (iv): A constant stretch is prescribed, which leads to stress-relaxation.

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Figure 7

The best-fit approximation to the data of Schwartz [32], found by systematically testing variable values. The solid line indicates the average cell number over ten simulations, with the gray region indicating the variation between runs (±1 standard deviation). The experimental data (shown as crosses) indicate cell numbers calculated from human PTCA patients.

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Figure 8

Diagrams of lattice state over time after balloon angioplasty (i.e., no stent). The background color indicates concentration of extracellular matrix (e). The spheres indicate the positions of cells, while the color indicates phenotype (green contractile to red synthetic). A band of injury forms at the lumen surface due to balloon expansion, from which cells proliferate and migrate into the lumen over time.

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Figure 9

The amount of restenosis (neointimal area/initial lumen area*100%) as a function of initial lumen area (i.e., the area within the expanded stent). Five simulations were conducted at each point with variation occurring due to the stochastic cell activity model. In the high inflammation case, parameters controlling inflammation were calibrated to produce long-term inflammation, while in the low inflammation case, these were selected to produce a removal of damage within 15 days.

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Figure 10

Diagrams of lattice state over time after stent expansion, showing SMC activation and proliferation leading to lesion formation and restenosis over time. The background color indicates concentration of extracellular matrix. The spheres indicate the positions of cells, while the color indicates phenotype (green contractile phenotype to red synthetic phenotype).




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