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Cerebral collateral circulation as an independent predictor for in-stent restenosis after carotid artery stenting

BACKGROUND: In-stent restenosis is a crucial problem after carotid artery stenting, but the exact predictors of in-stent restenosis remain unclear. We aimed to evaluate the effect of cerebral collateral circulation on in-stent restenosis after carotid artery stenting and to establish a clinical pred...

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Detalles Bibliográficos
Autores principales: Liu, Luji, Su, Xudong, Zhang, Lihong, Li, Zhongzhong, Bu, Kailin, Yuan, Si, Wang, Qisong, Wang, Ye, Aime, Ndoumou Justin, Liu, Zengpin, Zhou, Cunhe, Yu, Jianghua, Tan, Guojun, Guo, Li, Liu, Xiaoyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10167455/
https://www.ncbi.nlm.nih.gov/pubmed/37179948
http://dx.doi.org/10.21037/qims-22-975
Descripción
Sumario:BACKGROUND: In-stent restenosis is a crucial problem after carotid artery stenting, but the exact predictors of in-stent restenosis remain unclear. We aimed to evaluate the effect of cerebral collateral circulation on in-stent restenosis after carotid artery stenting and to establish a clinical prediction model for in-stent restenosis. METHODS: This retrospective case-control study enrolled 296 patients with severe carotid artery stenosis of C1 segment (≥70%) who underwent stent therapy from June 2015 to December 2018. Based on follow-up data, the patients were divided into the in-stent restenosis and no in-stent restenosis groups. The collateral circulation of the brain was graded according to the criteria of the American Society for Interventional and Therapy Neuroradiology/Society for Interventional Radiology (ASITN/SIR). Clinical data were collected, such as age, sex, traditional vascular risk factors, blood cell count, high-sensitivity C-reactive protein, uric acid, stenosis degree before stenting and residual stenosis rate, and medication after stenting. Binary logistic regression analysis was performed to identify potential predictors of in-stent restenosis, and a clinical prediction model for in-stent restenosis after carotid artery stenting was established. RESULTS: Binary logistic regression analysis showed that poor collateral circulation was an independent predictor of in-stent restenosis (P=0.003). We also found that a 1% increase in residual stenosis rate was associated with a 9% increase in the risk of in-stent restenosis (P=0.02). Ischemic stroke history (P=0.03), family history of ischemic stroke (P<0.001), in-stent restenosis history (P<0.001), and nonstandard medication after stenting (P=0.04) were predictors of in-stent restenosis. The risk of in-stent restenosis was lowest when the residual stenosis rate was 12.5% after carotid artery stenting. Furthermore, we used some significant parameters to construct a binary logistic regression prediction model for in-stent restenosis after carotid artery stenting in the form of a nomogram. CONCLUSIONS: Collateral circulation is an independent predictor of in-stent restenosis after successful carotid artery stenting, and the residual stenosis rate tends to be below 12.5% to reduce restenosis risk. The standard medication should be strictly carried out for patients after stenting to prevent in-stent restenosis.