Application of a mechanobiological simulation technique to stents used clinically

Boyle, Colin; Lennon, Alex; Prendergast, Patrick J.

doi:10.1016/j.jbiomech.2012.12.014

Cited by 26 publications

(25 citation statements)

References 19 publications

(29 reference statements)

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“…Also, there exist several analytical and computational models of this process (Prendergast et al, 2003; Boyle et al, 2011, 2013; Keller et al, 2014; Nolan et al, 2014; Zahedmanesh et al, 2014), including those developed earlier by our group (Evans et al, 2008; Tahir et al, 2011, 2015; Amatruda et al, 2014). For example, Zahedmanesh et al (2014) use a two-dimensional (2D) finite element method (FEM) model of stent deployment coupled with an (also 2D) agent-based model of SMC proliferation and extracellular matrix (ECM) generation.…”

Section: Introductionmentioning

confidence: 98%

A Comparison of Fully-Coupled 3D In-Stent Restenosis Simulations to In-vivo Data

et al. 2017

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We describe our fully-coupled 3D multiscale model of in-stent restenosis, with blood flow simulations coupled to smooth muscle cell proliferation, and report results of numerical simulations performed with this model. This novel model is based on several previously reported 2D models. We study the effects of various parameters on the process of restenosis and compare with in vivo porcine data where we observe good qualitative agreement. We study the effects of stent deployment depth (and related injury score), reendothelization speed, and simulate the effect of stent width. Also we demonstrate that we are now capable to simulate restenosis in real-sized (18 mm long, 2.8 mm wide) vessel geometries.

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Section: Introductionmentioning

confidence: 98%

A Comparison of Fully-Coupled 3D In-Stent Restenosis Simulations to In-vivo Data

et al. 2017

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“…9, and also by using computational models. 3,5,13,18,34 The computational models of ISR usually represent cells by agents, either freely moving 5,18,34 or placed on a lattice. 3,13 These agents take cues from the blood flow, concentration of drugs eluted from the stent, and from the vessel damage and mechanics, which affect the growth and proliferation of the cells.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Quantification of a Multiscale Model for In-Stent Restenosis

Nikishova

Veen

Zun

et al. 2018

Cardiovasc Eng Tech

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Purpose-Coronary artery stenosis, or abnormal narrowing, is a widespread and potentially fatal cardiac disease. After treatment by balloon angioplasty and stenting, restenosis may occur inside the stent due to excessive neointima formation. Simulations of in-stent restenosis can provide new insight into this process. However, uncertainties due to variability in patient-specific parameters must be taken into account. Methods-We performed an uncertainty quantification (UQ) study on a complex two-dimensional in-stent restenosis model. We used a quasi-Monte Carlo method for UQ of the neointimal area, and the Sobol sensitivity analysis (SA) to estimate the proportions of aleatory and epistemic uncertainties and to determine the most important input parameters. Results-We observe approximately 30% uncertainty in the mean neointimal area as simulated by the model. Depending on whether a fast initial endothelium recovery occurs, the proportion of the model variance due to natural variability ranges from 15 to 35%. The endothelium regeneration time is identified as the most influential model parameter. Conclusion-The model output contains a moderate quantity of uncertainty, and the model precision can be increased by obtaining a more certain value on the endothelium regeneration time. We conclude that the quasi-Monte Carlo UQ and the Sobol SA are reliable methods for estimating uncertainties in the response of complicated multiscale cardiovascular models.

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“…In the model by Sáez et al (2014), vascular growth is limited to the media and occurs by expansion of each element upon reaching a critical level of stretch. Herein, we improved and/or added to the previous coupled ABM-FEA models used to study vascular adaptation (including, (Zahedmanesh and Lally 2012; Boyle et al 2013; Zahedmanesh et al 2014; Garbey et al 2015)) by (1) incorporating three arterial cell types, (2) including cell-specific production of growth factors and proteases and (3) accounting for the structural role of collagen, elastin and gelatin by implementing a content-based strain energy density function in the FEA. Thus, we present a coupled ABM-FEA computational framework that simulates spatiotemporal changes in geometry, composition and state of stress in response to parameter manipulation.…”

Section: Discussionmentioning

confidence: 99%

“…However, these models were incapable of capturing non-axisymmetric spatial variations in mechanical properties, a shortcoming that can be addressed by coupling ABM and FEA models. Coupled ABM and FEA models have been used to study intimal hyperplasia in a tissue engineered vessel (Zahedmanesh and Lally 2012) and stent restenosis (Boyle et al 2013; Zahedmanesh et al 2014). Zahedmanesh et al (2014) modeled response of smooth muscle cells (SMCs) to placement of a stent, defined production of matrix metalloproteinase-2 (MMP-2) by SMCs as a function of mechanical damage and used a constant doubling time for proliferation of synthetic SMCs.…”

Section: Introductionmentioning

confidence: 99%

“…Zahedmanesh et al (2014) modeled response of smooth muscle cells (SMCs) to placement of a stent, defined production of matrix metalloproteinase-2 (MMP-2) by SMCs as a function of mechanical damage and used a constant doubling time for proliferation of synthetic SMCs. Boyle et al (2013) used a combination of an injury model, inflammation model and SMC model to simulate stent restenosis.…”

Section: Introductionmentioning

confidence: 99%

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Mechanobiological model of arterial growth and remodeling

Keshavarzian

Meyer

Hayenga

2017

Biomech Model Mechanobiol

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A coupled agent-based model (ABM) and finite element analysis (FEA) computational framework is developed to study the interplay of bio-chemo-mechanical factors in blood vessels and their role in maintaining homeostasis. The agent-based model implements the power of REPAST Simphony libraries and adapts its environment for biological simulations. Coupling a continuum-level model (FEA) to a cellular-level model (ABM) has enabled this computational framework to capture the response of blood vessels to increased or decreased levels of growth factors, proteases and other signaling molecules (on the micro scale) as well as altered blood pressure. Performance of the model is assessed by simulating porcine left anterior descending artery under normotensive conditions and transient increases in blood pressure and by analyzing sensitivity of the model to variations in the rule parameters of the ABM. These simulations proved that the model is stable under normotensive conditions and can recover from transient increases in blood pressure. Sensitivity studies revealed that the model is most sensitive to variations in the concentration of growth factors that affect cellular proliferation and regulate extracellular matrix composition (mainly collagen).

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Application of a mechanobiological simulation technique to stents used clinically

Cited by 26 publications

References 19 publications

A Comparison of Fully-Coupled 3D In-Stent Restenosis Simulations to In-vivo Data

A Comparison of Fully-Coupled 3D In-Stent Restenosis Simulations to In-vivo Data

Uncertainty Quantification of a Multiscale Model for In-Stent Restenosis

Mechanobiological model of arterial growth and remodeling

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