The unexplained success of stentplasty vasospasm treatment View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03-26

AUTHORS

P. Bhogal, G. Pederzani, A. Grytsan, Y. Loh, P. A. Brouwer, T. Andersson, Namrata Gundiah, Anne M. Robertson, Paul N. Watton, Michael Söderman

ABSTRACT

BACKGROUND: Cerebral vasospasm (CVS) following subarachnoid hemorrhage occurs in up to 70% of patients. Recently, stents have been used to successfully treat CVS. This implies that the force required to expand spastic vessels and resolve vasospasm is lower than previously thought. OBJECTIVE: We develop a mechanistic model of the spastic arterial wall to provide insight into CVS and predict the forces required to treat it. MATERIAL AND METHODS: The arterial wall is modelled as a cylindrical membrane using a constrained mixture theory that accounts for the mechanical roles of elastin, collagen and vascular smooth muscle cells (VSMC). We model the pressure diameter curve prior to CVS and predict how it changes following CVS. We propose a stretch-based damage criterion for VSMC and evaluate if several commercially available stents are able to resolve vasospasm. RESULTS: The model predicts that dilatation of VSMCs beyond a threshold of mechanical failure is sufficient to resolve CVS without damage to the underlying extracellular matrix. Consistent with recent clinical observations, our model predicts that existing stents have the potential to provide sufficient outward force to successfully treat CVS and that success will be dependent on an appropriate match between stent and vessel. CONCLUSION: Mathematical models of CVS can provide insights into biological mechanisms and explore treatment approaches. Improved understanding of the underlying mechanistic processes governing CVS and its mechanical treatment may assist in the development of dedicated stents. More... »

PAGES

1-12

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00062-019-00776-2

DOI

http://dx.doi.org/10.1007/s00062-019-00776-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1113005854

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30915482


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Turtle is a human-readable linked data format.

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This table displays all metadata directly associated to this object as RDF triples.

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