CT Lilun yu yingyong yanjiu (May 2023)

Comparative Analysis of Clinical and Computed Tomography Imaging Features of COVID-19 with Different Disease Courses

  • Mingxia ZHANG,
  • Ling LI,
  • Ying SUN,
  • Jia GUO,
  • Changyue DU,
  • Xingpeng LI,
  • Yan ZHANG,
  • Qi HAO,
  • Shuhong DUAN,
  • Xiaoyan LIU,
  • Lei SUN,
  • Meng HUO,
  • Chunyan ZHANG,
  • Rengui WANG

DOI
https://doi.org/10.15953/j.ctta.2023.021
Journal volume & issue
Vol. 32, no. 3
pp. 380 – 386

Abstract

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Objective: To compare and analyze the clinical and chest computed tomography (CT) imaging features of COVID-19 patients with different disease courses. Methods: A retrospective analysis was performed for 161 cases with confirmed COVID-19 and positive chest CT lung infections from December 2022 to January 2023 at the fever clinic of Beijing Shijitan Hospital affiliated with Capital Medical University. The patients were divided into two groups based on the time of CT examination: <10 days and ≥10 days. We statistically analyzed the clinical manifestations and chest CT imaging characteristics of the two groups. Results: Of the 161 cases, 92 cases (57.1%) were in the <10-day group, and 69 cases (42.9%) were in the ≥10-day group. The clinical symptoms of the two groups showed that there was a statistical difference in the proportion of sore throat and myalgia between the two groups. Laboratory indicators showed that the C-reactive protein and lymphocyte count were significantly higher in the <10-day group. In terms of CT imaging features, the proportion of patients with perivascular, mixed distribution, large area, and air bronchogram was higher in the patients from the <10-day group, while the patients in the ≥10-day group had a significantly higher proportion of irregular boundaries, intralesional cord, reversed halo sign, pleural tail sign, subpleural line, and subpleural palisade. Conclusion: The clinical symptoms, laboratory indexes, and CT imaging features of COVID-19 pulmonary infection differed depending on the disease course, and exploring these differences can help clinicians diagnose and treat COVID-19 lung infections more effectively.

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