A Computational Model of Restenosis under Deployment of Rapamycin-eluting Stents

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Abstract

Stents are meshed tubular devices placed inside blood vessels to keep them open after angioplasty. Since bare-metal stents show an increased probability of re-narrowing (restenosis) within a few months after treatment, so-called drug-eluting stents (DESs) were proposed. These devices gradually emit an antiproliferative drug (e.g., Rapamycin), thereby reducing the tissue growth rate, lowering the restenosis risk. Nevertheless, due to the decreased cell growth, the pace of wound closure is decreased simultaneously, increasing the risk of thrombosis. In this work, we present a computational model of the restenosis process considering the effects of nutrient availability, growth factors and the antiproliferative drug Rapamycin. We use a combination of experimental data and physical first-principle reasoning to obtain a system of ordinary differential equations describing the evolution of the populations of key components like smooth vascular muscle cells and endothelial cells. We simulate the system for a hypothetical patient, showing the influence of the Rapamycin dose on both restenosis and wound closure and discuss implications for future DES design.Clinical relevance-In this work, we present a computational model of the restenosis process after drug-eluting stent placement. We link clinically relevant parameters, like wound geometry, drug dose and blood nutrient availability, to growth and development of the vascular tissue. The proposed model provides a tool for understanding and improving drug-elution patterns, in order to optimize patient outcomes. Hence, this work can be considered an important step towards a digital twin which will have direct application in clinical settings.

Original languageEnglish
Title of host publication2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Pages1-7
Number of pages7
Edition1
DOIs
Publication statusPublished - Jul 2025

Fields of science

  • 202 Electrical Engineering, Electronics, Information Engineering
  • 202038 Telecommunications
  • 202037 Signal processing
  • 202030 Communication engineering

JKU Focus areas

  • Digital Transformation

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