In:
PLOS ONE, Public Library of Science (PLoS), Vol. 18, No. 6 ( 2023-6-16), p. e0287383-
Abstract:
Predicted lung volumes based on the Global Lung Function Initiative (GLI) model are used in pulmonary disease detection and monitoring. It is unknown how well the predicted lung volume corresponds with computed tomography (CT) derived total lung volume (TLV). The aim of this study was to compare the GLI-2021 model predictions of total lung capacity (TLC) with CT-derived TLV. 151 female and 139 male healthy participants (age 45–65 years) were consecutively selected from a Dutch general population cohort, the Imaging in Lifelines (ImaLife) cohort. In ImaLife, all participants underwent low-dose, inspiratory chest CT. TLV was measured by an automated analysis, and compared to predicted TLC based on the GLI-2021 model. Bland-Altman analysis was performed for analysis of systematic bias and range between limits of agreement. To further mimic the GLI-cohort all analyses were repeated in a subset of never-smokers (51% of the cohort). Mean±SD of TLV was 4.7±0.9 L in women and 6.2±1.2 L in men. TLC overestimated TLV, with systematic bias of 1.0 L in women and 1.6 L in men. Range between limits of agreement was 3.2 L for women and 4.2 L for men, indicating high variability. Performing the analysis with never-smokers yielded similar results. In conclusion, in a healthy cohort, predicted TLC substantially overestimates CT-derived TLV, with low precision and accuracy. In a clinical context where an accurate or precise lung volume is required, measurement of lung volume should be considered.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0287383
DOI:
10.1371/journal.pone.0287383.g001
DOI:
10.1371/journal.pone.0287383.g002
DOI:
10.1371/journal.pone.0287383.g003
DOI:
10.1371/journal.pone.0287383.g004
DOI:
10.1371/journal.pone.0287383.t001
DOI:
10.1371/journal.pone.0287383.s001
DOI:
10.1371/journal.pone.0287383.s002
DOI:
10.1371/journal.pone.0287383.s003
DOI:
10.1371/journal.pone.0287383.s004
DOI:
10.1371/journal.pone.0287383.s005
DOI:
10.1371/journal.pone.0287383.r001
DOI:
10.1371/journal.pone.0287383.r002
DOI:
10.1371/journal.pone.0287383.r003
DOI:
10.1371/journal.pone.0287383.r004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2023
detail.hit.zdb_id:
2267670-3
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