A fully coupled computational fluid dynamics – agent-based model of atherosclerotic plaque development: Multiscale modeling framework and parameter sensitivity analysis. (March 2020)

• Record Type: Journal Article
• Authors: Corti, Anna; Chiastra, Claudio; Colombo, Monika; Garbey, Marc; Migliavacca, Francesco; Casarin, Stefano
• Is Part Of: Computers in biology and medicine. Volume 118(2020)
• Publication Date: 2020-03
• Subjects: Atherosclerosis -- Computer modeling -- Multiscale model -- Agent-based model -- Lipid plaque -- SMC -- ECM -- Remodeling -- Wall shear stress -- Hemodynamics; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2020.103623
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: Peripheral Artery Disease (PAD) is an atherosclerotic disorder that leads to impaired lumen patency through intimal hyperplasia and the build-up of plaques, mainly localized in areas of disturbed flow. Computational models can provide valuable insights in the pathogenesis of atherosclerosis and act as a predictive tool to optimize current interventional techniques. Our hypothesis is that a reliable predictive model must include the atherosclerosis development history. Accordingly, we developed a multiscale modeling framework of atherosclerosis that replicates the hemodynamic-driven arterial wall remodeling and plaque formation. Methods: The framework was based on the coupling of Computational Fluid Dynamics (CFD) simulations with an Agent-Based Model (ABM). The CFD simulation computed the hemodynamics in a 3D artery model, while 2D ABMs simulated cell, Extracellular Matrix (ECM) and lipid dynamics in multiple vessel cross-sections. A sensitivity analysis was also performed to evaluate the oscillation of the ABM output to variations in the inputs and to identify the most influencing ABM parameters. Results: Our multiscale model qualitatively replicated both the physiologic and pathologic arterial configuration, capturing histological-like features. The ABM outputs were mostly driven by cell and ECM dynamics, largely affecting the lumen area. A subset of parameters was found to affect the final lipid core size, without influencing cell/ECM or lumen area trends. Conclusion: The fully coupled CFD-ABM framework described atherosclerotic morphological and compositional changes triggered by a disturbed hemodynamics. Highlights: Two-way coupling of computational fluid dynamics and agent based models. CFD-ABM framework able to capture hemodynamics-driven arterial wall remodeling. ABM replicating morphological and compositional changes in atherosclerosis. Simulated, asymmetric plaques formed in areas affected by disturbed hemodynamics. The sensitivity analysis identified one parameter mostly driving the ABM output.