Predictive Computed Tomography and Clinical Features for Diagnosis of COVID-19 Pneumonia: Compared With Common Viral Pneumonia.
J Comput Assist Tomogr
; 44(5): 627-632, 2020.
Article
en En
| MEDLINE
| ID: mdl-32889972
ABSTRACT
OBJECTIVE:
To determine the predictive computed tomography (CT) and clinical features for diagnosis of COVID-19 pneumonia.METHODS:
The CT and clinical data including were analyzed using univariate analysis and multinomial logistic regression, followed by receiver operating characteristic curve analysis.RESULTS:
The factors including size of ground grass opacity (GGO), GGO with reticular and/or interlobular septal thickening, vascular enlargement, "tree-in-bud" opacity, centrilobular nodules, and stuffy or runny nose were associated with the 2 groups of viral pneumonia, as determined by univariate analysis (P < 0.05). Only GGO with reticular and/or interlobular septal thickening, centrilobular nodules, and stuffy or runny nose remained independent risk factors in multinomial logistic regression analysis. Receiver operating characteristic curve analysis showed that the area under curve of the obtained logistic regression model was 0.893.CONCLUSION:
Computed tomography and clinical features including GGO with reticular and/or interlobular septal thickening, absence of centrilobular nodules, and absence of stuffy or runny nose are potential patients with COVID-19 pneumonia.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Neumonía Viral
/
Tomografía Computarizada por Rayos X
/
Infecciones por Coronavirus
/
Técnicas de Laboratorio Clínico
/
Betacoronavirus
/
Pulmón
Tipo de estudio:
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Adult
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Female
/
Humans
/
Male
/
Middle aged
Idioma:
En
Revista:
J Comput Assist Tomogr
Año:
2020
Tipo del documento:
Article
País de afiliación:
China