Haemodynamic impact of stent-vessel (mal)apposition following carotid artery stenting: mind the gaps!

G De Santis1, M Conti, B Trachet

  • 1BiTech-bioMMeda, Ghent University, Ghent, Belgium. gianluca.desantis@ugent.be

Insights

Computational analysis of carotid artery stenting (CAS) reveals how stent placement impacts blood flow. Poor stent apposition can cause abnormal shear forces, potentially leading to adverse events like thrombosis.

Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Computational Fluid Dynamics

Background:

  • Carotid artery stenting (CAS) is a less invasive option than endarterectomy but faces limitations due to post-stenting complications.
  • Hemodynamic factors in stented vessels may influence endothelial responses, contributing to restenosis and thrombosis.
  • In vivo measurement of flow-related shear forces on endothelium is limited by current non-invasive imaging resolution.

Purpose of the Study:

  • To develop a computational framework for precise determination of wall shear stress (WSS) after carotid artery stenting.
  • To investigate the hemodynamic consequences of stent implantation in a mildly stenosed carotid artery model.

Main Methods:

  • Utilized angiographic CT and micro-CT scans to create a 3D model of the stented carotid artery.
  • Performed in silico stent deployment using finite element analysis.
  • Simulated blood flow with defined inlet pressure and outlet impedance boundary conditions.

Main Results:

  • Identified atheroprone regions characterized by low time-average WSS and high relative residence time.
  • Localized these regions to areas of stent malapposition and strut interconnections.
  • Observed that unapposed stent struts in the external carotid artery ostium disrupt flow and generate abnormal shear forces.

Conclusions:

  • Computational analysis provides crucial insights into hemodynamic alterations post-CAS.
  • Stent malapposition is linked to adverse hemodynamic conditions that may promote thromboembolic events.
  • This in silico approach aids in understanding and potentially mitigating CAS complications.

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