Journal
JAPANESE JOURNAL OF RADIOLOGY
Volume 39, Issue 8, Pages 783-790Publisher
SPRINGER
DOI: 10.1007/s11604-021-01118-4
Keywords
COVID-19; SARS-CoV-2; Pneumonia; Respiratory failure; Computed tomography
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The study found that in patients with COVID-19 pneumonia, CT patterns associated with respiratory failure are mainly classified as Type A, Type B, and Type C, with Type C and hypertension being related to respiratory failure. The proportion of Type C lesions increases with age.
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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