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Med Biol Eng Comput. 2009 Apr;47(4):385-93. doi: 10.1007/s11517-009-0432-5. Epub 2009 Feb 3.

Determination of the influence of stent strut thickness using the finite element method: implications for vascular injury and in-stent restenosis.

Author information

1
School of Mechanical and Manufacturing Engineering, Dublin City University, Glasnevin, Dublin 9, Ireland.

Abstract

Many clinical studies, including the ISAR-STEREO trial, have identified stent strut thickness as an independent predictor of in-stent restenosis where thinner struts result in lower restenosis than thicker struts. The aim of this study was to more conclusively identify the mechanical stimulus for in-stent restenosis using results from such clinical trials as the ISAR-STEREO trial. The mechanical environment in arteries stented with thin and thicker strut stents was investigated using numerical modelling techniques. Finite element models of the stents used in the ISAR-STEREO clinical trial were developed and the stents were deployed in idealized stenosed vessel geometries in order to compare the mechanical environment of the vessel for each stent. The stresses induced within the stented vessels by these stents were compared to determine the level of vascular injury caused to the artery by the stents with different strut thickness. The study found that when both stents were expanded to achieve the same initial maximum stent diameter that the thinner strut stent recoiled to a greater extent resulting in lower luminal gain but also lower stresses in the vessel wall, which is hypothesised to be responsible for the lower restenosis outcome. This study supports the hypothesis that arteries develop restenosis in response to injury, where high vessel stresses are a good measure of that injury. This study points to a critical stress level in arteries, above which an aggressive healing response leads to in-stent restenosis in stented vessels. Stents can be designed to reduce stresses in this range in arteries using preclinical tools such as numerical modelling.

PMID:
19189146
DOI:
10.1007/s11517-009-0432-5
[Indexed for MEDLINE]

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