Initial CT features of COVID-19 predicting clinical category.
Chin J Acad Radiol
; 4(4): 241-247, 2021.
Article
in English
| MEDLINE | ID: covidwho-1107922
ABSTRACT
PURPOSE:
To analyze the initial CT features of different clinical categories of COVID-19. MATERIAL ANDMETHODS:
A total of 86 patients with COVID-19 were analyzed, including the clinical, laboratory and imaging features. The following imaging features were analyzed, the lesion amount, location, density, lung nodule, halo sign, reversed-halo sign, distribution pattern, inner structures and changes of adjacent structures. Chi-square test, Fisher's exact test, or Mann-Whitney U test was used for the enumeration data. Binary logistic regression analysis was performed to draw a regression equation to estimate the likelihood of severe and critical category. The forward conditional method was employed for variable selection.RESULTS:
Significant statistical differences were found in age (p = 0.001) and sex (p = 0.028) between mild and moderate and severe and critical category. No significant difference was found in clinical symptoms and WBC count between the two groups. The majority of cases (91.8%) showed multifocal lesions. The presence of GGO was higher in severe and critical category than in the mild and moderate category. (57.8% vs.31.7%, p = 0.015). Lymphocyte count was important indicator for the severe and critical category.CONCLUSION:
The initial CT features of the different clinical category overlapped. Combining with laboratory test, especially the lymphocyte count, could help to predict the severity of COVID-19. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s42058-021-00056-4.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Experimental Studies
/
Prognostic study
/
Randomized controlled trials
Language:
English
Journal:
Chin J Acad Radiol
Year:
2021
Document Type:
Article
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