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CT Quantitative Analysis and Its Relationship with Clinical Features for Assessing the Severity of Patients with COVID-19

  • Dong Sun (Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University) ;
  • Xiang Li (Department of Radiology, Chongqing Three Gorges Central Hospital) ;
  • Dajing Guo (Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University) ;
  • Lan Wu (Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University) ;
  • Ting Chen (Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University) ;
  • Zheng Fang (Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University) ;
  • Linli Chen (Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University) ;
  • Wenbing Zeng (Department of Radiology, Chongqing Three Gorges Central Hospital) ;
  • Ran Yang (Department of Radiology, Chongqing Three Gorges Central Hospital)
  • Received : 2020.03.15
  • Accepted : 2020.04.02
  • Published : 2020.07.01

Abstract

Objective: To investigate the value of initial CT quantitative analysis of ground-glass opacity (GGO), consolidation, and total lesion volume and its relationship with clinical features for assessing the severity of coronavirus disease 2019 (COVID-19). Materials and Methods: A total of 84 patients with COVID-19 were retrospectively reviewed from January 23, 2020 to February 19, 2020. Patients were divided into two groups: severe group (n = 23) and non-severe group (n = 61). Clinical symptoms, laboratory data, and CT findings on admission were analyzed. CT quantitative parameters, including GGO, consolidation, total lesion score, percentage GGO, and percentage consolidation (both relative to total lesion volume) were calculated. Relationships between the CT findings and laboratory data were estimated. Finally, a discrimination model was established to assess the severity of COVID-19. Results: Patients in the severe group had higher baseline neutrophil percentage, increased high-sensitivity C-reactive protein (hs-CRP) and procalcitonin levels, and lower baseline lymphocyte count and lymphocyte percentage (p < 0.001). The severe group also had higher GGO score (p < 0.001), consolidation score (p < 0.001), total lesion score (p < 0.001), and percentage consolidation (p = 0.002), but had a lower percentage GGO (p = 0.008). These CT quantitative parameters were significantly correlated with laboratory inflammatory marker levels, including neutrophil percentage, lymphocyte count, lymphocyte percentage, hs-CRP level, and procalcitonin level (p < 0.05). The total lesion score demonstrated the best performance when the data cut-off was 8.2%. Furthermore, the area under the curve, sensitivity, and specificity were 93.8% (confidence interval [CI]: 86.8-100%), 91.3% (CI: 69.6-100%), and 91.8% (CI: 23.0-98.4%), respectively. Conclusion: CT quantitative parameters showed strong correlations with laboratory inflammatory markers, suggesting that CT quantitative analysis might be an effective and important method for assessing the severity of COVID-19, and may provide additional guidance for planning clinical treatment strategies.

Keywords

Acknowledgement

We express our deepest condolences to all patients with COVID-19 and their families. We also appreciate all medical staffs who are fighting against the COVID-19. The authors are grateful to Jack-Chen and Pejman for language editing.

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