Towards a Complex Automata Multiscale Model of In-Stent Restenosis

Easing Multiscale Model Design and Coupling with MUSCLE 3

Multiscale modelling and simulation typically entails coupling multiple simulation codes into a single program. Doing this in an ad-hoc fashion tends to result in a tightly coupled, difficult-to-change computer program. This makes it difficult to experiment with different submodels, or to implement advanced techniques such as surrogate modelling. Furthermore, building the coupling itself is time-consuming. The MUltiScale Coupling Library and Environment version 3 (MUSCLE 3) aims to alleviate these problems. It allows the coupling to be specified in a simple configuration file, which specifies the components of the simulation and how they should be connected together. At runtime a simulation manager takes care of coordination of submodels, while data is exchanged over the network in a peer-to-peer fashion via the MUSCLE library. Submodels need to be linked to this library, but this is minimally invasive and restructuring simulation codes is usually not needed. Once operational, the model may be rewired or augmented by changing the configuration, without further changes to the submodels. MUSCLE 3 is developed openly on GitHub, and is available as Open Source software under the Apache 2.0 license.

Uncertainty Quantification of a Multiscale Model for In-Stent Restenosis

Purpose

Coronary artery stenosis, or abnormal narrowing, is a widespread and potentially fatal cardiac disease. After treatment by balloon angioplasty and stenting, restenosis may occur inside the stent due to excessive neointima formation. Simulations of in-stent restenosis can provide new insight into this process. However, uncertainties due to variability in patient-specific parameters must be taken into account.

Methods

We performed an uncertainty quantification (UQ) study on a complex two-dimensional in-stent restenosis model. We used a quasi-Monte Carlo method for UQ of the neointimal area, and the Sobol sensitivity analysis (SA) to estimate the proportions of aleatory and epistemic uncertainties and to determine the most important input parameters.

Results

We observe approximately 30% uncertainty in the mean neointimal area as simulated by the model. Depending on whether a fast initial endothelium recovery occurs, the proportion of the model variance due to natural variability ranges from 15 to 35%. The endothelium regeneration time is identified as the most influential model parameter.

Conclusion

The model output contains a moderate quantity of uncertainty, and the model precision can be increased by obtaining a more certain value on the endothelium regeneration time. We conclude that the quasi-Monte Carlo UQ and the Sobol SA are reliable methods for estimating uncertainties in the response of complicated multiscale cardiovascular models.

Multi-scale simulations of the dynamics of in-stent restenosis: impact of stent deployment and design

Neointimal hyperplasia, a process of smooth muscle cell re-growth, is the result of a natural wound healing response of the injured artery after stent deployment. Excessive neointimal hyperplasia following coronary artery stenting results in in-stent restenosis (ISR). Regardless of recent developments in the field of coronary stent design, ISR remains a significant complication of this interventional therapy. The influence of stent design parameters such as strut thickness, shape and the depth of strut deployment within the vessel wall on the severity of restenosis has already been highlighted but the detail of this influence is unclear. These factors impact on local haemodynamics and vessel structure and affect the rate of neointima formation. This paper presents the first results of a multi-scale model of ISR. The development of the simulated restenosis as a function of stent deployment depth is compared with an in vivo porcine dataset. Moreover, the influence of strut size and shape is investigated, and the effect of a drug released at the site of injury, by means of a drug-eluting stent, is also examined. A strong correlation between strut thickness and the rate of smooth muscle cell proliferation has been observed. Simulation results also suggest that the growth of the restenotic lesion is strongly dependent on the stent strut cross-sectional profile.