TY - JOUR
T1 - Modelling structural determinants of ventilation heterogeneity: a perturbative approach.
AU - Whitfield, Carl
AU - Horsley, Alexander
AU - Jensen, Oliver
N1 - Funding Information:
CW was funded by the UK Medical Research Council (MRC) grant number MR/ R024944/1. CW and OE acknowledge the UK Engineering and Physical Sciences Research Council (EPSRC) for funding through grant reference EP/K037145/1. AH was funded by a UK National Institute for Health Research (NIHR) Clinician Scientist (CS012-13). This report presents independent research funded by the NIHR. This research was also supported by the NIHR Manchester Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the UK National Health Service, the NIHR or the UK Department of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
© 2018 Whitfield 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.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2018/11/29
Y1 - 2018/11/29
N2 - We have developed a computational model of gas mixing and ventilation in the human lung represented as a bifurcating network. We have simulated multiple-breath washout (MBW), a clinical test for measuring ventilation heterogeneity (VH) in patients with obstructive lung conditions. By applying airway constrictions inter-regionally, we have predicted the response of MBW indices to obstructions and found that they detect a narrow range of severe constrictions that reduce airway radius to 10%–30% of healthy values. These results help to explain the success of the MBW test to distinguish obstructive lung conditions from healthy controls. Further, we have used a perturbative approach to account for intra-regional airway heterogeneity that avoids modelling each airway individually. We have found, for random airway heterogeneity, that the variance in MBW indices is greater when indices are already elevated due to constrictions. By quantifying this effect, we have shown that variability in lung structure and mechanical properties alone can lead to clinically significant variability in MBW indices (specifically the Lung Clearance Index—LCI, and the gradient of phase-III slopes—Scond), but only in cases simulating obstructive lung conditions. This method is a computationally efficient way to probe the lung’s sensitivity to structural changes, and to quantify uncertainty in predictions due to random variations in lung mechanical and structural properties.
AB - We have developed a computational model of gas mixing and ventilation in the human lung represented as a bifurcating network. We have simulated multiple-breath washout (MBW), a clinical test for measuring ventilation heterogeneity (VH) in patients with obstructive lung conditions. By applying airway constrictions inter-regionally, we have predicted the response of MBW indices to obstructions and found that they detect a narrow range of severe constrictions that reduce airway radius to 10%–30% of healthy values. These results help to explain the success of the MBW test to distinguish obstructive lung conditions from healthy controls. Further, we have used a perturbative approach to account for intra-regional airway heterogeneity that avoids modelling each airway individually. We have found, for random airway heterogeneity, that the variance in MBW indices is greater when indices are already elevated due to constrictions. By quantifying this effect, we have shown that variability in lung structure and mechanical properties alone can lead to clinically significant variability in MBW indices (specifically the Lung Clearance Index—LCI, and the gradient of phase-III slopes—Scond), but only in cases simulating obstructive lung conditions. This method is a computationally efficient way to probe the lung’s sensitivity to structural changes, and to quantify uncertainty in predictions due to random variations in lung mechanical and structural properties.
KW - Computer Simulation
KW - Forced Expiratory Volume
KW - Forecasting/methods
KW - Humans
KW - Lung Diseases, Obstructive/diagnosis
KW - Pulmonary Ventilation/physiology
KW - Respiration
KW - Respiratory Function Tests/methods
KW - Tidal Volume
KW - Ventilation/methods
KW - Vital Capacity/physiology
UR - https://www.scopus.com/pages/publications/85057569006
U2 - 10.1371/journal.pone.0208049
DO - 10.1371/journal.pone.0208049
M3 - Article
C2 - 30496317
SN - 1932-6203
VL - 13
SP - e0208049
JO - P L o S One
JF - P L o S One
IS - 11
M1 - e0208049
ER -