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Updated: Oct 2, 2025
Imaging In-Stent Restenosis: An Inexpensive, Reliable, and Rapid Preclinical Model
Published on: September 14, 2009
Uncertainty quantification of a three-dimensional in-stent restenosis model with surrogate modelling
Dongwei Ye1, Pavel Zun1,2, Valeria Krzhizhanovskaya1
1Computational Science Lab, Institute for Informatics, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands.
Insights
In-stent restenosis, a narrowing of coronary arteries, involves uncertainties in key parameters. Blood flow and endothelium regeneration significantly impact restenosis progression, highlighting areas for future research and treatment strategies.
Area of Science:
- Cardiovascular Research
- Biomedical Engineering
- Computational Biology
Background:
- In-stent restenosis (ISR) is the re-narrowing of coronary arteries after stenting, often leading to angina or acute coronary syndromes.
- Vascular injury from balloon angioplasty and stent deployment triggers ISR, involving complex biological and mechanical processes.
Purpose of the Study:
- To quantify uncertainties in a computational model of in-stent restenosis.
- To identify key parameters influencing ISR progression and outcomes.
Main Methods:
- Developed a surrogate model using Gaussian process regression and proper orthogonal decomposition to handle high computational costs.
- Performed uncertainty quantification on a model with four uncertain parameters: endothelium regeneration time, smooth muscle cell bond breaking threshold, blood flow velocity, and internal elastic lamina fenestration percentage.
- Analyzed uncertainty propagation for two quantities of interest: average cross-sectional area and maximum relative area loss.
Main Results:
- Observed approximately 11% uncertainty in average cross-sectional area and 16% in maximum relative area loss.
- Higher internal elastic lamina fenestration was found to be a primary driver of uncertainty in neointimal growth during the early stages.
- Blood flow velocity and endothelium regeneration time emerged as major contributors to uncertainty in the later, clinically significant stages of restenosis.
Conclusions:
- Uncertainty quantification reveals critical parameters influencing in-stent restenosis.
- Early-stage ISR uncertainty is linked to vascular structure (fenestration), while later stages are influenced by dynamic factors (blood flow, regeneration).
- Findings guide future research and therapeutic interventions targeting specific stages of in-stent restenosis.
Abstract:
In-stent restenosis is a recurrence of coronary artery narrowing due to vascular injury caused by balloon dilation and stent placement. It may lead to the relapse of angina symptoms or to an acute coronary syndrome. An uncertainty quantification of a model for in-stent restenosis with four uncertain parameters (endothelium regeneration time, the threshold strain for smooth muscle cell bond breaking, blood flow velocity and the percentage of fenestration in the internal elastic lamina) is presented. Two quantities of interest were studied, namely the average cross-sectional area and the maximum relative area loss in a vessel. Owing to the high computational cost required for uncertainty quantification, a surrogate model, based on Gaussian process regression with proper orthogonal decomposition, was developed and subsequently used for model response evaluation in the uncertainty quantification. A detailed analysis of the uncertainty propagation is presented. Around 11% and 16% uncertainty is observed on the two quantities of interest, respectively, and the uncertainty estimates show that a higher fenestration mainly determines the uncertainty in the neointimal growth at the initial stage of the process. The uncertainties in blood flow velocity and endothelium regeneration time mainly determine the uncertainty in the quantities of interest at the later, clinically relevant stages of the restenosis process.
