arXiv subjects

Laura Ellwein Fix

Publications and source records attributed to Laura Ellwein Fix.

4 recordsLinked to original sources

arXiv2025

Practical parameter identifiability of respiratory mechanics in the extremely preterm infant

The complexity of mathematical models describing respiratory mechanics has grown in recent years, however, parameter identifiability of such models has only been studied in the last decade in the context of observable data. This study investigates parameter identifiability of a nonlinear respiratory mechanics model tuned to the physiology of an extremely preterm infant, using global Morris screening, local deterministic sensitivity analysis, and singular value decomposition-based subset selection. The model predicts airflow and dynamic pulmonary volumes and pressures under varying levels of continuous positive airway pressure, and a range of parameters characterizing both surfactant-treated and surfactant-deficient lung. Sensitivity analyses indicated eleven parameters influence model outputs over the range of continuous positive airway pressure and lung health scenarios. The model was adapted to data from a spontaneously breathing 1 kg infant using gradient-based optimization to estimate the parameter subset characterizing the patient's state of health.

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arXiv2020

Parameter identifiability of a respiratory mechanics model in an idealized preterm infant

The complexity of mathematical models describing respiratory mechanics has grown in recent years to integrate with cardiovascular models and incorporate nonlinear dynamics. However, additional model complexity has rarely been studied in the context of patient-specific observable data. This study investigates parameter identification of a previously developed nonlinear respiratory mechanics model (Ellwein Fix, PLoS ONE 2018) tuned to the physiology of 1 kg preterm infant, using local deterministic sensitivity analysis, subset selection, and gradient-based optimization. The model consists of 4 differential state equations with 31 parameters to predict airflow and dynamic pulmonary volumes and pressures generated under six simulation conditions. The relative sensitivity solutions of the model state equations with respect to each of the parameters were calculated with finite differences and a sensitivity ranking was created for each parameter and simulation. Subset selection identified a set of independent parameters that could be estimated for all six simulations. The combination of these analyses produced a subset of 6 independent sensitive parameters that could be estimated given idealized clinical data. All optimizations performed using pseudo-data with perturbed nominal parameters converged within 40 iterations and estimated parameters within ~8% of nominal values on average. This analysis indicates the feasibility of performing parameter estimation on real patient-specific data set described by a nonlinear respiratory mechanics model for studying dynamics in preterm infants.

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arXiv2019

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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arXiv2018

Theoretical open-loop model of respiratory mechanics in the extremely preterm infant

Non-invasive ventilation is increasingly used for respiratory support in preterm infants, and is associated with a lower risk of chronic lung disease. However, this mode is often not successful in the extremely preterm infant in part due to their markedly increased chest wall compliance that does not provide enough structure against which the forces of inhalation can generate sufficient pressure. To address the continued challenge of studying treatments in this fragile population, we developed a nonlinear lumped-parameter model of respiratory system mechanics of the extremely preterm infant that incorporates nonlinear lung and chest wall compliances and lung volume parameters tuned to this population. In particular we developed a novel empirical representation of progressive volume loss based on compensatory alveolar pressure increase resulting from collapsed alveoli. The model demonstrates increased rate of volume loss related to high chest wall compliance, and simulates laryngeal braking for elevation of end-expiratory lung volume and constant positive airway pressure (CPAP). The model predicts that low chest wall compliance (chest stiffening) in addition to laryngeal braking and CPAP enhance breathing and delay lung volume loss. These results motivate future data collection strategies and investigation into treatments for chest wall stiffening.

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