UMMS Affiliation

Department of Radiology

Publication Date

2022-01-25

Document Type

Article

Disciplines

Infectious Disease | Pulmonology | Radiology | Respiratory Tract Diseases | Virus Diseases

Abstract

BACKGROUND: Coronavirus disease 2019 (COVID-19) may severely impair pulmonary function and cause hypoxia. However, the association of COVID-19 pneumonia on CT with impaired ventilation remains unexplained. This pilot study aims to demonstrate the relationship between the radiological findings on COVID-19 CT images and ventilation abnormalities simulated in a computational model linked to the patients' symptoms.

METHODS: Twenty-five patients with COVID-19 and four test-negative healthy controls who underwent a baseline non-enhanced CT scan: 7 dyspneic patients, 9 symptomatic patients without dyspnea, and 9 asymptomatic patients were included. A 2D U-Net-based CT segmentation software was used to quantify radiological futures of COVID-19 pneumonia. The CT image-based full-scale airway network (FAN) flow model was employed to assess regional lung ventilation. Functional and radiological features were compared across groups and correlated with the clinical symptoms. Heterogeneity in ventilation distribution and ventilation defects associated with the pneumonia and the patients' symptoms were assessed.

RESULTS: Median percentage ventilation defects were 0.2% for healthy controls, 0.7% for asymptomatic patients, 1.2% for symptomatic patients without dyspnea, and 11.3% for dyspneic patients. The median of percentage pneumonia was 13.2% for dyspneic patients and 0% for the other groups. Ventilation defects preferentially affected the posterior lung and worsened with increasing pneumonia linearly (y = 0.91x + 0.99, R2 = 0.73) except for one of the nine dyspneic patients who had disproportionally large ventilation defects (7.8% of the entire lung) despite mild pneumonia (1.2%). The symptomatic and dyspneic patients showed significantly right-skewed ventilation distributions (symptomatic without dyspnea: 0.86 +/- 0.61, dyspnea 0.91 +/- 0.79) compared to the patients without symptom (0.45 +/- 0.35). The ventilation defect analysis with the FAN model provided a comparable diagnostic accuracy to the percentage pneumonia in identifying dyspneic patients (area under the receiver operating characteristic curve, 0.94 versus 0.96).

CONCLUSIONS: COVID-19 pneumonia segmentations from CT scans are accompanied by impaired pulmonary ventilation preferentially in dyspneic patients. Ventilation analysis with CT image-based computational modelling shows it is able to assess functional impairment in COVID-19 and potentially identify one of the aetiologies of hypoxia in patients with COVID-19 pneumonia.

Keywords

COVID 19, Pneumonia, Computed axial tomography, Dyspnea, Pulmonary imaging, Opacity, Reverse transcriptase-polymerase chain reaction, Medical hypoxia

Rights and Permissions

Copyright: © 2022 Inui 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.

DOI of Published Version

10.1371/journal.pone.0263158

Source

Inui S, Yoon SH, Doganay O, Gleeson FV, Kim M. Impaired pulmonary ventilation beyond pneumonia in COVID-19: A preliminary observation. PLoS One. 2022 Jan 25;17(1):e0263158. doi: 10.1371/journal.pone.0263158. PMID: 35077496; PMCID: PMC8789183. Link to article on publisher's site

Journal/Book/Conference Title

PloS one

Related Resources

Link to Article in PubMed

PubMed ID

35077496

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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