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.
Highlights
Coronary heart disease is mainly due to the accumulation and development of atherosclerotic plaques, which narrow the vessel lumen and reduce the flow of blood
To train the surrogate models, 512 samples were generated by the quasi-Monte Carlo (qMC) method and evaluated by the ISR3D model
Before the surrogate model was deployed to the Uncertainty quantification (UQ) experiment, the surrogates were validated with a fourfold cross-validation
Summary
Coronary heart disease is mainly due to the accumulation and development of atherosclerotic plaques, which narrow the vessel lumen and reduce the flow of blood. The agent-based SMC model simulates the biological and mechanical states of each cell of the vessel, while the BF model provides the haemodynamics information as a function of the current vessel lumen shape This multiscale model has been applied to investigate the effect of functional endothelium regeneration and the impact of stent deployment and design on restenosis [6,7,9,10]. With this computationally efficient surrogate model, uncertainty estimations and sensitivity analysis of the restenosis process are conducted and analysed.
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