Vascular stents have become a standard for treating atherosclerosis due to
distinct advantages in trauma and cost with other surgical techniques. Unfortunately, the
therapy is hindered by the risk of a new blockage (termed restenosis) developing in the
treated artery. Clinical studies have indicated that stent design is a major risk factor for
restenosis, with failure rates varying from 20 to 40% for bare metal stents. Subsequently,
there has been a significant effort devoted to reducing failure rates by covering stents in
polymer coatings in which anti-proliferative drugs are embedded, however
complications have arisen (e.g. incomplete endothelization, lack of success in peripheral
arteries, lack of long-term follow-up studies) that have limited the success of this
technology. It has been thought that restenosis is directly related to the mechanical
conditions that vascular stents create. Moreover, it has been hypothesized that stents that
induce higher non-physiologic stresses result in a more aggressive pathobiological
response that can lead to restenosis development.
In this study, a combination of computational modeling and in vivo analysis were
conducted to investigate the artery stent-induced wall stresses, and subsequent biological inflammatory response. In particular, variations in stent design were investigated as a
means of examining specific stent design criteria that minimize the mechanical impact of
stenting. Collectively, these data indicate that stent designs that subject the artery wall to
higher stress values result in significantly more neointimal tissue proliferation, therefore,
confirming the aforementioned hypothesis. Moreover, this work provides valuable
insight into the role that biomechanics can play in improving the success rate of this
percutaneous therapy and overall patient care.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2010-05-7682 |
| Date | 2010 May 1900 |
| Creators | Timmins, Lucas Howard |
| Contributors | Moore, Jr., James E. |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | thesis, text |
| Format | application/pdf |
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