A Complex Automata approach for in-stent restenosis: Two-dimensional multiscale modelling and simulations

Caiazzo, Alfonso; Evans, D.; Falcone, Jean-Luc; Hegewald, Jan; Lorenz, Eric; Stahl, Bernd; Wang, Dinan; Bernsdorf, J.; Chopard, Bastien; Gunn, Julian; Hose, D.R.; Krafczyk, Manfred; Lawford, Patricia; Smallwood, R. H.; Walker, Dawn; Hoekstra, Alfons G.

doi:10.1016/j.jocs.2010.09.002

Cited by 77 publications

(96 citation statements)

References 28 publications

(47 reference statements)

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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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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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“…phenotypic changes, production of ECM and proliferation, with changes in cyclic strain [22,23]. Given the lack of consensus in the literature on the specific mechanical stimuli driving vascular injury, and ultimately the in-stent restenosis process, mechanobiological models of in-stent restenosis have been defined which are governed by many different mechanical stimuli, namely shear stress or oscillatory shear stress [24], stretch [25], von Mises stress [14] and cyclic damage accumulation [19] to name but a few.…”

Section: Figurementioning

confidence: 99%

“…Stretch [25] , cyclic strain [15,23] and shear stress imposed by blood flow [24,37] are just a few of the other mechanical stimuli that have been postulated to play a role in driving the in-stent restenosis process and this model could be adapted to include such stimuli. It can also be used to explore cell migration along with cell proliferation, or even cell differentiation.…”

Section: Figure 10mentioning

confidence: 99%

An investigation of damage mechanisms in mechanobiological models of in-stent restenosis

Nolan

Lally

2018

Journal of Computational Science

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The mechanisms behind in-stent restenosis, the re-narrowing of a stented artery, are poorly understood. However it is known that mechanical damage due to stent implantation plays a major role. This paper investigates the mechanism behind damage-induced cell proliferation using a coupled finite element and agent based model, assuming it is based on a) instantaneous loading, or b) cyclic loading. Furthermore the role of remnant endothelial cells in attenuating in-stent restenosis is examined. Results show that a cyclic damage model predicts a nonphysiological, overly proliferative response, whilst the instantaneous model demonstrates that lumen loss may be regulated by re-endothelialisation.

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“…Figure 7 describes the case of in-stent restenosis [1,4,21]. After the stenting of a coronary artery, the SMCs are likely to proliferate into the lumen, causing again a stenosis.…”

Section: Model 1dmentioning

confidence: 99%

“…It reflects the fact that, during the time iterations of the submodels, a reduced set of generic operations have to be Figure 7. SSM of the in-stent restenosis application described in [1,21]. performed over and over again.…”

Section: (B) Submodel Execution Loop and Coupling Templatesmentioning

confidence: 99%

A framework for multi-scale modelling

Chopard

Borgdorff

Hoekstra

2014

Phil. Trans. R. Soc. A.

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One contribution of 13 to a Theme Issue 'Multi-scale systems in fluids and soft matter: approaches, numerics and applications' . We review a methodology to design, implement and execute multi-scale and multi-science numerical simulations. We identify important ingredients of multi-scale modelling and give a precise definition of them. Our framework assumes that a multi-scale model can be formulated in terms of a collection of coupled single-scale submodels. With concepts such as the scale separation map, the generic submodel execution loop (SEL) and the coupling templates, one can define a multi-scale modelling language which is a bridge between the application design and the computer implementation. Our approach has been successfully applied to an increasing number of applications from different fields of science and technology.

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A Complex Automata approach for in-stent restenosis: Two-dimensional multiscale modelling and simulations

Cited by 77 publications

References 28 publications

Uncertainty Quantification of a Multiscale Model for In-Stent Restenosis

Uncertainty Quantification of a Multiscale Model for In-Stent Restenosis

An investigation of damage mechanisms in mechanobiological models of in-stent restenosis

A framework for multi-scale modelling

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