38 research outputs found
Efficient Uncertainty Quantification in a Multiscale Model of Pulmonary Arterial and Venous Hemodynamics
Computational hemodynamics models are becoming increasingly useful in the
management and prognosis of complex, multiscale pathologies, including those
attributed to the development of pulmonary vascular disease. However, diseases
like pulmonary hypertension are heterogeneous, and affect both the proximal
arteries and veins as well as the microcirculation. Simulation tools and the
data used for model calibration are also inherently uncertain, requiring a full
analysis of the sensitivity and uncertainty attributed to model inputs and
outputs. Thus, this study quantifies model sensitivity and output uncertainty
in a multiscale, pulse-wave propagation model of pulmonary hemodynamics. Our
pulmonary circuit model consists of fifteen proximal arteries and twelve
proximal veins, connected by a two-sided, structured tree model of the distal
vasculature. We use polynomial chaos expansions to expedite the sensitivity and
uncertainty quantification analyses and provide results for both the proximal
and distal vasculature. Our analyses provide uncertainty in blood pressure,
flow, and wave propagation phenomenon, as well as wall shear stress and cyclic
stretch, both of which are important stimuli for endothelial cell
mechanotransduction. We conclude that, while nearly all the parameters in our
system have some influence on model predictions, the parameters describing the
density of the microvascular beds have the largest effects on all simulated
quantities in both the proximal and distal circulation.Comment: 10 Figures, 2 table
Inference in Cardiovascular Modelling Subject to Medical Interventions
Pulmonary hypertension (PH), i.e., high blood pressure in the lungs, is a serious medical condition that can damage the right ventricle of the heart and ultimately lead to heart failure. Standard diagnostic procedures are based on right-heart catheterization, which is an invasive technique that can potentially have serious side effects. Recent methodological advancements in fluid dynamics modelling of the pulmonary blood circulation system promise to mathematically predict the blood pressure based on non-invasive measurements of the blood flow. Thus, subsequent to PH diagnostication, further investigations would no longer require catheterization. However, in order for these alternative techniques to be applicable in the clinic, accurate model calibration and parameter estimation are paramount. Medical interventions taken to combat high blood pressure (as predicted from the mathematical model) alter the underlying cardiovascular physiology, thus interfering with the parameter estimation procedure. In the present study, we have carried out a series of cardiovascular simulations to assess the reliability of cardiovascular physiological parameter estimation in the presence of medical interventions. Our principal result is that if the closed-loop effect of medical interventions is accounted for, the model calibration provides accurate parameter estimates. This finding has important implications for the applicability of cardio-physiological modelling in the clinical practice
Influence of image segmentation on one-dimensional fluid dynamics predictions in the mouse pulmonary arteries
Computational fluid dynamics (CFD) models are emerging as tools for assisting
in diagnostic assessment of cardiovascular disease. Recent advances in image
segmentation has made subject-specific modelling of the cardiovascular system a
feasible task, which is particularly important in the case of pulmonary
hypertension (PH), which requires a combination of invasive and non-invasive
procedures for diagnosis. Uncertainty in image segmentation can easily
propagate to CFD model predictions, making uncertainty quantification crucial
for subject-specific models. This study quantifies the variability of
one-dimensional (1D) CFD predictions by propagating the uncertainty of network
geometry and connectivity to blood pressure and flow predictions. We analyse
multiple segmentations of an image of an excised mouse lung using different
pre-segmentation parameters. A custom algorithm extracts vessel length, vessel
radii, and network connectivity for each segmented pulmonary network. We
quantify uncertainty in geometric features by constructing probability
densities for vessel radius and length, and then sample from these
distributions and propagate uncertainties of haemodynamic predictions using a
1D CFD model. Results show that variation in network connectivity is a larger
contributor to haemodynamic uncertainty than vessel radius and length
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Assessing model mismatch and model selection in a Bayesian uncertainty quantification analysis of a fluid-dynamics model of pulmonary blood circulation
This study uses Bayesian inference to quantify the uncertainty of model parameters and haemodynamic predictions in a one-dimensional pulmonary circulation model based on an integration of mouse haemodynamic and micro-computed tomography imaging data. We emphasize an often neglected, though important source of uncertainty: in the mathematical model form due to the discrepancy between the model and the reality, and in the measurements due to the wrong noise model (jointly called ‘model mismatch’). We demonstrate that minimizing the mean squared error between the measured and the predicted data (the conventional method) in the presence of model mismatch leads to biased and overly confident parameter estimates and haemodynamic predictions. We show that our proposed method allowing for model mismatch, which we represent with Gaussian processes, corrects the bias. Additionally, we compare a linear and a nonlinear wall model, as well as models with different vessel stiffness relations. We use formal model selection analysis based on the Watanabe Akaike information criterion to select the model that best predicts the pulmonary haemodynamics. Results show that the nonlinear pressure–area relationship with stiffness dependent on the unstressed radius predicts best the data measured in a control mouse
Aerosol Transport Modeling: The Key Link Between Lung Infections of Individuals and Populations
The recent COVID-19 pandemic has propelled the field of aerosol science to the forefront, particularly the central role of virus-laden respiratory droplets and aerosols. The pandemic has also highlighted the critical need, and value for, an information bridge between epidemiological models (that inform policymakers to develop public health responses) and within-host models (that inform the public and health care providers how individuals develop respiratory infections). Here, we review existing data and models of generation of respiratory droplets and aerosols, their exhalation and inhalation, and the fate of infectious droplet transport and deposition throughout the respiratory tract. We then articulate how aerosol transport modeling can serve as a bridge between and guide calibration of within-host and epidemiological models, forming a comprehensive tool to formulate and test hypotheses about respiratory tract exposure and infection within and between individuals
Aerosol Transport Modeling: The Key Link Between Lung Infections of Individuals and Populations
The recent COVID-19 pandemic has propelled the field of aerosol science to the forefront, particularly the central role of virus-laden respiratory droplets and aerosols. The pandemic has also highlighted the critical need, and value for, an information bridge between epidemiological models (that inform policymakers to develop public health responses) and within-host models (that inform the public and health care providers how individuals develop respiratory infections). Here, we review existing data and models of generation of respiratory droplets and aerosols, their exhalation and inhalation, and the fate of infectious droplet transport and deposition throughout the respiratory tract. We then articulate how aerosol transport modeling can serve as a bridge between and guide calibration of within-host and epidemiological models, forming a comprehensive tool to formulate and test hypotheses about respiratory tract exposure and infection within and between individuals