Sensitivity analysis and uncertainty quantification of 1D models of the pulmonary circulation
Mitchel J. Colebank, M. Umar Qureshi, Mette S. Olufsen

TL;DR
This paper integrates 1D pulmonary circulation models with imaging and hemodynamic data to analyze parameter sensitivity, estimate uncertainties, and understand disease-related changes in vascular properties in mice.
Contribution
It introduces combined sensitivity and uncertainty quantification methods applied to 1D pulmonary models, revealing disease-related parameter changes and model robustness.
Findings
Peripheral vascular resistance is most sensitive.
Network size affects parameter behavior.
Hypertensive mice have stiffer, larger vessels with decreased compliance.
Abstract
This study combines a one-dimensional (1D) model with micro-CT imaging and hemodynamic data to quantify uncertainty of flow and pressure predictions in the pulmonary arteries in a control and hypoxia induced hypertensive mouse. We use local and global sensitivity and correlation analysis to determine parameters that can be inferred from the model and available data. Least squares optimization is used to estimate mouse specific parameters, and Bayesian as well as asymptotic uncertainty quantification techniques are employed to determine confidence, credible, and prediction intervals for the model parameters and response. These techniques are used to examine the effects of network size and to understand how parameters change with disease (hypertension). Results showed that the peripheral vascular resistance is the most sensitive, and as the network size increases the parameter behavior…
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