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10.1111/ijcp.13760

http://scihub22266oqcxt.onion/10.1111/ijcp.13760
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suck abstract from ncbi


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pmid33068310      Int+J+Clin+Pract 2021 ; 75 (4): e13760
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  • Clinical differences in chest CT characteristics between the progression and remission stages of patients with COVID-19 pneumonia #MMPMID33068310
  • Liao JL; Chen Y; Huang CQ; He GQ; Du JC; Chen QL
  • Int J Clin Pract 2021[Apr]; 75 (4): e13760 PMID33068310show ga
  • 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.
  • |*COVID-19/diagnostic imaging[MESH]
  • |*Tomography, X-Ray Computed[MESH]
  • |COVID-19 Testing[MESH]
  • |Humans[MESH]
  • |Lung/diagnostic imaging[MESH]
  • |Pneumonia, Viral/diagnostic imaging[MESH]
  • |ROC Curve[MESH]
  • |Retrospective Studies[MESH]


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