Prognostic Factors for In-stent Restenosis After Cerebrovascular Stent Implantation among Ischemic Stroke Patients

Shiliang Xia1, JIaxing You2, Delong Wang1

  • 1Department of Neurology, Minhang Hospital, Fudan University, Shanghai, China.

PubMed

Insights

Hypertension and high neutral-lymphocyte ratio (NLR) are key risk factors for in-stent restenosis after cerebrovascular stenting. Patients with vertebral artery stenosis face a higher risk of restenosis compared to those with internal carotid artery stenosis.

Area of Science:

  • Neurology
  • Cardiovascular Medicine
  • Medical Technology

Background:

  • Cerebrovascular stent implantation is a common revascularization treatment.
  • In-stent restenosis significantly impacts the effectiveness of cerebrovascular stenting.
  • Identifying factors influencing restenosis is crucial for improving patient outcomes.

Purpose of the Study:

  • To prospectively investigate clinical factors associated with in-stent restenosis after cerebrovascular stent implantation.
  • To identify predictors of in-stent restenosis in ischemic stroke patients.

Main Methods:

  • A prospective study of 251 ischemic stroke patients undergoing cerebrovascular stenting.
  • Outcome measured: in-stent restenosis within 12 months post-procedure.
  • Statistical analyses included univariate logistic regression, stepwise regression, and ROC curves.

Main Results:

  • Hypertension (OR 2.15), neutral-lymphocyte ratio (NLR) (OR 1.10), and vertebral artery stenosis (OR 3.30) were associated with increased restenosis risk.
  • Stepwise analysis identified hypertension, NLR, and stenosis location as independent predictors.
  • The predictive model showed an AUC of 0.736 for general stenting, 0.621 for vertebral, and 0.775 for internal carotid artery stenting.

Conclusions:

  • Hypertension and NLR are potential predictive factors for in-stent restenosis.
  • Vertebral artery stenosis poses a higher restenosis risk than internal carotid artery stenosis.
  • Further research is needed to validate these findings and discover additional predictive factors.
Abstract

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