Sensitivity analysis and uncertainty quantification of 1‐D models of pulmonary hemodynamics in mice under control and hypertensive conditions. (10th September 2019)

• Record Type: Journal Article
• Authors: Colebank, Mitchel J.; Umar Qureshi, M.; Olufsen, Mette S.
• Is Part Of: International journal for numerical methods in biomedical engineering. Volume 37:Number 11(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-09-10
• Subjects: computational fluid dynamics -- network models -- pulmonary hypertension -- sensitivity analysis -- uncertainty quantification; Biomedical engineering -- Periodicals; Imaging systems in medicine -- Periodicals; Numerical analysis -- Periodicals; Engineering mathematics -- Periodicals; 610.28
• DOI: 10.1002/cnm.3242
• Languages: English
• ISSNs: 2040-7939

Abstract

Abstract: Pulmonary hypertension (PH), defined as an elevated mean blood pressure in the main pulmonary artery (MPA) at rest, is associated with vascular remodeling of both large and small arteries. PH has several sub‐types that are all linked to high mortality rates. In this study, we use a one‐dimensional (1‐D) fluid dynamics model driven by in vivo measurements of MPA flow to understand how model parameters and network size influence MPA pressure predictions in the presence of PH. We compare model predictions with in vivo MPA pressure measurements from a control and a hypertensive mouse and analyze results in three networks of increasing complexity, extracted from micro‐computed tomography (micro‐CT) images. We introduce global scaling factors for boundary condition parameters and perform local and global sensitivity analysis to calculate parameter influence on model predictions of MPA pressure and correlation analysis to determine a subset of identifiable parameters. These are inferred using frequentist optimization and Bayesian inference via the Delayed Rejection Adaptive Metropolis (DRAM) algorithm. Frequentist and Bayesian uncertainty is computed for model parameters and MPA pressure predictions. Results show that MPA pressure predictions are most sensitive to distal vascular resistance and that parameter influence changes with increasing network complexity. Our outcomes suggest that PH leads to increased vascular stiffness and decreased peripheral compliance, congruent with clinical observations. Abstract : Pulmonary hypertension (PH) induces network‐wide changes in hemodynamics. This study uses 1D fluids models coupled with Windkessel outflow conditions to study PH. We use scaling factors to reduce the model complexity, yielding four parameters assessed by sensitivity analyses, parameter estimation, and uncertainty quantification. Results in three networks of increasing complexity constructed from mice microCT images reveal that model sensitivity changes with increasing network size and that mice with PH have increased resistance and decreased compliance in comparison to controls.