Document Type
Article
Original Publication Date
2018
Journal/Book/Conference Title
PLoS ONE
Volume
13
Issue
6:e019842
First Page
1
Last Page
21
DOI of Original Publication
10.1371/ journal.pone.0198425
Date of Submission
October 2019
Abstract
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 respiratory system mechanics model 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.
Rights
© 2018 Ellwein Fix et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Is Part Of
VCU Mathematics and Applied Mathematics Publications
Comments
Originally published at https://doi.org/10.1371/ journal.pone.0198425
Funded in part by the VCU Libraries Open Access Publishing Fund.