Predictors of Perioperative Stroke/Death after Carotid Artery Stenting: A Review Article

Ali F AbuRahma1

  • 1Department of Surgery, West Virginia University, Charleston, West Virginia, USA.

Insights

Carotid artery stenting (CAS) outcomes depend on patient factors like age and comorbidities, and lesion characteristics such as plaque type and stenosis degree. Technical aspects, including the use of embolic protection devices, also influence perioperative stroke and death risks.

Area of Science:

  • Vascular Surgery
  • Interventional Cardiology
  • Neurology

Background:

  • Carotid artery stenting (CAS) is an alternative to carotid endarterectomy for significant carotid stenosis.
  • Few studies comprehensively analyze factors influencing CAS perioperative outcomes.
  • Identifying predictors of stroke and death after CAS is crucial for patient safety.

Purpose of the Study:

  • To review and synthesize data on clinical, anatomical, and technical variables affecting perioperative outcomes of CAS.
  • To identify key predictors of stroke and death following CAS procedures.

Main Methods:

  • Comprehensive Medline search to identify relevant studies.
  • Analysis of clinical, angiographic, and technical factors associated with perioperative stroke and death rates.
  • Review of data presented at the 45th Annual Meeting of the Japanese Society for Vascular Surgery.

Main Results:

  • Clinical factors like advanced age (>80), renal failure, diabetes, and recent transient ischemic attack symptoms increase perioperative risk.
  • Angiographic findings such as ulcerated/calcified plaques, severe stenosis (>90%), and specific arch/artery angulations are linked to higher stroke rates.
  • Technical factors like percutaneous transluminal angioplasty (PTA) without embolic protection and multiple stent use are associated with increased perioperative stroke risk.

Conclusions:

  • Patient age, comorbidities, lesion characteristics, and specific procedural techniques significantly impact perioperative stroke and death rates after CAS.
  • Understanding these predictors can help optimize patient selection and procedural strategies to improve CAS safety and outcomes.

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