Clinical differences in chest CT characteristics between the progression and remission stages of patients with COVID‐19 pneumonia
| Autor: | Yu Chen, Gui-Qing He, Que-Lu Chen, Chong-Quan Huang, Jie-Lan Liao, Ji-Cheng Du |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
Coronavirus disease 2019 (COVID-19) Pneumonia Viral Chest ct chest CT 030204 cardiovascular system & hematology Logistic regression Lesion 03 medical and health sciences COVID-19 Testing remission 0302 clinical medicine Text mining COVID‐19 medicine Humans Cutoff 030212 general & internal medicine Lung Retrospective Studies Original Paper SARS-CoV-2 business.industry COVID-19 Retrospective cohort study General Medicine Original Papers ROC Curve progression Radiology Tomography medicine.symptom Tomography X-Ray Computed business consolidation |
| Zdroj: | International Journal of Clinical Practice |
| ISSN: | 1742-1241 1368-5031 |
| DOI: | 10.1111/ijcp.13760 |
| Popis: | Introduction Computed tomography (CT) can be effective for the early screening and diagnosis of COVID‐19. This study aimed to investigate the distinctive CT characteristics of two stages of the disease (progression and remission). Methods We included all COVID‐19 patients admitted to Wenzhou Central Hospital from January to February, 2020. Patients underwent multiple chest CT scans at intervals of 3–10 days. CT features were recorded, such as the lesion lobe, distribution characteristics (subpleural, scattered, or diffused), shape of the lesion, maximum size of the lesion, lesion morphology (ground‐glass opacity, GGO), and consolidation features. When consolidation was positive, the boundary was identified to determine its clarity. Results The ratios of some representative features differed between the remission stage and the progression phase, such as round‐shape lesion (8.0% vs. 34.4%), GGO (65.0% vs. 87.5%), consolidation (62.0% vs. 31.3%), large cable sign (59.0% vs. 9.4%), and crazy‐paving sign (20.0% vs. 50.0%). Using these features, we pooled all the CT data (n = 132) and established a logistic regression model to predict the current development stage. The variables consolidation, boundary feature, large cable sign, and crazy‐paving sign were the most significant factors, based on a variable named ‘prediction of progression or remission’ (PPR) that we constructed. The ROC curve showed that PPR had an AUC of 0.882 (cutoff value = 0.66, sensitivity = 0.75, specificity = 0.875). Conclusion CT characteristics, in particular, round shape, GGO, consolidation, large cable sign, and crazy‐paving sign, may increase the recognition of the intrapulmonary development of COVID‐19. |
| Databáze: | OpenAIRE |
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