Uncertainty Quantification of a Multiscale Model for In-Stent Restenosis

• Published in: Cardiovascular engineering and technology 2018-12, Vol.9 (4), p.761-774
• Main Authors: Nikishova, Anna, Veen, Lourens, Zun, Pavel, Hoekstra, Alfons G.
• Format: Article
• Language: English
• Subjects: Animals; Biomedical Engineering and Bioengineering; Biomedicine; Cardiology; Computer Simulation; Coronary Restenosis - etiology; Coronary Restenosis - pathology; Coronary Restenosis - physiopathology; Coronary Stenosis - pathology; Coronary Stenosis - physiopathology; Coronary Stenosis - surgery; Coronary Vessels - pathology; Coronary Vessels - physiopathology; Coronary Vessels - surgery; Endothelium; Engineering; Mathematical models; Models, Cardiovascular; Monte Carlo Method; Monte Carlo simulation; Neointima; Numerical Analysis, Computer-Assisted; Parameter identification; Parameter uncertainty; Percutaneous Coronary Intervention - adverse effects; Percutaneous Coronary Intervention - instrumentation; Regeneration; Reproducibility of Results; Sensitivity analysis; Stents; Surgical implants; Sus scrofa; Two dimensional models; Uncertainty; Vascular Remodeling
• ISSN: 1869-408X; 1869-4098
• DOI: 10.1007/s13239-018-00372-4

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.