An analysis of the radiological factors associated with respiratory failure in COVID-19 pneumonia and the CT features among different age categories
Open accessAbstract
Purpose To investigate CT patterns of COVID-19 pneumonia associated with respiratory failure (RF) focused on the distribution of lesions.Materials and methods Eighty-five patients with COVID-19 pneumonia were reviewed. CT findings were classified as follows: Type A; patchy ground glass attenuation (GGA) with/without air-space consolidation, Type B; non-segmental GGA with/without air-space consolidation in both the central and peripheral lung portions especially with subpleural spare, and Type C; non-segmental GGA with/without air-space consolidation predominantly distributed in the peripheral lung portion without subpleural spare. We analyzed CT patterns and clinical factors associated with RF, including age categories.Results The number of patients with Type A, B and C was 31 (37%), 24 (28%) and 30 (35%), respectively. Type C and hypertension were independently associated with RF. On comparing between Types B and C, the frequency of traction bronchiectasis was higher in Type C than in Type B (P < 0.001). The ratio of Type C in patients ≥ 65 years old (66%) was higher than in patients < 40 years old (P < 0.001) and 40-49 years old (P = 0.001).Conclusion The Type C, increasing with age, was associated with RF. Traction bronchiectasis in the lesion was more frequent in Type C than in Type B. Secondary abstract A lesion adjacent to the pleura and hypertension is associated with respiratory failure in patients with COVID-19. The frequency of a lesion adjacent to the pleura increased with age. The distribution of lesions is a useful parameter to predict respiratory failure.
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The COVID-19 pandemic increased the number of COVID-19-related studies from Japan, with a significant increase in such research focusing on AI.
“Furthermore, owing to the coronavirus disease 2019 (COVID-19) pandemic, the number of COVID-19-related studies from Japan has increased [54–61], with a significant increase in such research focusing on AI [62–64].”
From Fairness of artificial intelligence in healthcare: review and recommendations (2024) · cites this paper as [57]
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- Authors
- Fukuda, A., Yanagawa, N., Sekiya, N., Ohyama, K., Yomota, M., Inui, T., Fujiwara, S., Kawai, S., Fukushima, K., Tanaka, M., Kobayashi, T., Yajima, K., & Imamura, A.
- Affiliations
- Tokyo Metropolitan Komagome Hospital (JP)
- Corresponding author
- Sekiya, Noritaka
- ORCID
- Venue
- Japanese Journal of Radiology
- Published
- 2021-04-12
- Volume
- 39
- Issue
- 8
- Pages
- 783-790
- ISSN
- 1867-1071 (ISSN-L)
- 1867-108X
- PMC
- PMC8040361
- OpenAlex
- W4238063834
- Cited by
- 8
- References
- 15
- Citations by year
- 2023: 4 · 2022: 3 · 2021: 1
- FWCI
- 0.63
- Type
- journal article
- Language
- en
- Update notices
- correction (2021-08-13)
- Open access
- bronze
- doi.org
- Locations
- Japanese Journal of Radiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
- Keywords
- COVID-19 pneumonia, respiratory failure, CT patterns, ground-glass attenuation, air-space consolidation, traction bronchiectasis, lesion distribution, age, hypertension, pleural involvement, risk factors
- MeSH terms
- COVID-19, SARS-CoV-2, Adult, Aged, Female, Humans, Lung, Male, Middle Aged, Pneumonia, Respiratory Insufficiency, Retrospective Studies, Tomography, X-Ray Computed
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