Multi-objective optimisation of stent dilation strategy in a patient-specific coronary artery via computational and

Georgios E Ragkousis1, Nick Curzen2, Neil W Bressloff1

  • 1Computational Engineering & Design Group, Faculty of Engineering & the Environment, University of Southampton, Boldrewood Campus, Southampton SO16 7QF, UK.

Journal of Biomechanics
|January 1, 2016
PubMed

Insights

Optimizing stent dilation protocols using computer simulations can minimize stent malapposition and vessel trauma in complex coronary artery cases. This approach helps interventional cardiologists achieve better stent expansion and drug delivery for improved patient outcomes.

Area of Science:

  • Biomedical Engineering
  • Cardiovascular Research
  • Computational Fluid Dynamics

Background:

  • Contemporary stents improve clinical outcomes, but optimal dilation protocols remain unclear for complex anatomies (long, calcified, tortuous).
  • Suboptimal stent deployment can lead to stent thrombosis (ST) and neointimal thickening due to stent malapposition (SM) and vessel trauma.
  • Balloon dilation during stent deployment is a significant contributor to vessel trauma.

Purpose of the Study:

  • To investigate the impact of balloon pressure and unpressurized diameter on stent malapposition, drug distribution, and wall stresses using computer simulations.
  • To identify optimal stent dilation protocols that minimize stent malapposition and tissue wall stresses while maximizing drug diffusion.

Main Methods:

  • Implemented a Kriging-based response surface modeling approach for optimization.
  • Performed patient-specific computer simulations of coronary artery stenting.
  • Utilized multi-objective optimization to analyze trade-offs between stent malapposition, tissue stresses, and drug delivery.

Main Results:

  • Stent malapposition was found to be inversely proportional to tissue stresses and drug deliverability.
  • A set of "non-dominated" dilation scenarios was proposed for protocol selection.
  • Optimal stent expansion can be predicted for patient-specific models.

Conclusions:

  • The developed framework can predict optimal stent expansion in patient-specific cases.
  • This approach offers a potential tool for interventional cardiologists to minimize stent malapposition and tissue stresses.
  • Maximizing drug deliverability alongside minimizing adverse effects is achievable with optimized protocols.