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Identifying features and severity of 2019 coronavirus disease (COVID-19) on admission based on thoracic computer tomography
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Z. Mo   , Q. Zhang , X.W. Luo , Y. Zhang , T. Qin , Y.Z. Zhu |
| Department of Radiology, The 901st Hospital of the Joint Logistics Support Force of PLA, Heifei 230031, People Republic of China , zhangyu105fsk@163.com |
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Abstract: (1292 Views) |
Background: To investigate the computer tomography (CT) features of 2019 coronavirus disease (COVID-19)-related pneumonia and its value for identifying severity. Materials and Methods: Seventy-three patients with COVID-19 were divided into severe and nonsevere groups. CT signs were divided into two states: presence and absence; involvement range was divided into four grades; and affected lobes were divided into two states: ≥3 lobes and < 3 lobes; laboratory indices were divided into two states: normal and abnormal; co-occurrence of signs was divided into three states: ground-glass opacity (GGO) plus consolidation, only GGO, or only consolidation. The numbers of patients were respectively recorded. Statistical analysis was performed through the χ2 test, followed by multivariate logistic regression analysis. Results: Some indicators differed, including pure GGO (p<0.001), GGO with focal consolidation (p=0.009), patchy consolidation (p=0.004), sheeted consolidation (p<0.001), fibrotic appearance (p=0.020), involvement grade (p<0.001), affected lobes (p=0.027), pleural effusion (p=0.001), subpleural line (p=0.015), crazy paving signs (p<0.001), halo signs (p=0.020), thickened bronchial walls (p<0.001), air bronchi signs (p=0.003), lesions in mid/inner zone (p<0.001), liver function (p=0.044), interleukin-6 (p<0.001), c-reactive protein (p<0.001), lymphocyte count (p<0.001), and age (p=0.036). Pure GGO (OR:30.711, HR:1.292~729.882, p=0.034) and involvement grade (OR:0.017, HR:0.001~0.342, p=0.008) were independent risk factors. Conclusion: On admission, CT signs of COVID-19-related pneumonia were diverse but characteristic, and some CT findings may be potential warning factors for severity, while a lack of GGO and extensive pneumonia may be independent risk factors. |
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| Keywords: 2019 coronavirus disease, pneumonia, Tomography, X-ray compute. |
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Type of Study: Original Research |
Subject:
Radiation Biology
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Mo Z, Zhang Q, Luo X, Zhang Y, Qin T, Zhu Y. Identifying features and severity of 2019 coronavirus disease (COVID-19) on admission based on thoracic computer tomography. Int J Radiat Res 2022; 20 (3) :579-585 URL: http://ijrr.com/article-1-4343-en.html
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