Article ; Online: CT differential diagnosis of COVID-19 and non-COVID-19 in symptomatic suspects: a practical scoring method.
2020 Volume 20, Issue 1, Page(s) 129
Abstract: ... system based on CT imaging features, we can make a hierarchical diagnosis of COVID-19 and non-COVID-19 ... from other non-COVID-19 pneumonia were used. The scoring analysis of CT features was compared between the two ... of COVID-19 and forty-three cases of other aetiology or clinically confirmed non-COVID-19 ...
| Abstract | Background: Although typical and atypical CT image findings of COVID-19 are reported in current studies, the CT image features of COVID-19 overlap with those of viral pneumonia and other respiratory diseases. Hence, it is difficult to make an exclusive diagnosis. Methods: Thirty confirmed cases of COVID-19 and forty-three cases of other aetiology or clinically confirmed non-COVID-19 in a general hospital were included. The clinical data including age, sex, exposure history, laboratory parameters and aetiological diagnosis of all patients were collected. Seven positive signs (posterior part/lower lobe predilection, bilateral involvement, rounded GGO, subpleural bandlike GGO, crazy-paving pattern, peripheral distribution, and GGO +/- consolidation) from significant COVID-19 CT image features and four negative signs (only one lobe involvement, only central distribution, tree-in-bud sign, and bronchial wall thickening) from other non-COVID-19 pneumonia were used. The scoring analysis of CT features was compared between the two groups (COVID-19 and non-COVID-19). Results: Older age, symptoms of diarrhoea, exposure history related to Wuhan, and a lower white blood cell and lymphocyte count were significantly suggestive of COVID-19 rather than non-COVID-19 (p < 0.05). The receiver operating characteristic (ROC) curve of the combined CT image features analysis revealed that the area under the curve (AUC) of the scoring system was 0.854. These cut-off values yielded a sensitivity of 56.67% and a specificity of 95.35% for a score > 4, a sensitivity of 100% and a specificity of 23.26% for a score > 0, and a sensitivity of 86.67% and a specificity of 67.44% for a score > 2. Conclusions: With a simple and practical scoring system based on CT imaging features, we can make a hierarchical diagnosis of COVID-19 and non-COVID-19 with different management suggestions. |
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| MeSH term(s) | Adult ; Betacoronavirus ; COVID-19 ; Coronavirus Infections/diagnostic imaging ; Diagnosis, Differential ; Female ; Humans ; Male ; Middle Aged ; Pandemics ; Pneumonia, Viral/diagnostic imaging ; SARS-CoV-2 ; Tomography, X-Ray Computed |
| Keywords | covid19 |
| Language | English |
| Publishing date | 2020-05-07 |
| Publishing country | England |
| Document type | Journal Article |
| ZDB-ID | 2059871-3 |
| ISSN | 1471-2466 ; 1471-2466 |
| ISSN (online) | 1471-2466 |
| ISSN | 1471-2466 |
| DOI | 10.1186/s12890-020-1170-6 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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