Longitudinal Chest CT Features in Severe/Critical COVID-19 Cases and the Predictive Value of the Initial CT for Mortality

Abstract

Hailan Li,1,* Shiyong Luo,2,* Youming Zhang,3 Xiaoyi Xiao,4 Huaping Liu4 1Department of Radiology, Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, 410000, Hunan Province, People’s Republic of China; 2Department of Radiology, Wuhan Third Hospital (Tongren Hospital of Wuhan University), Wuhan, 430060, Hubei Province, People’s Republic of China; 3Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan Province, People’s Republic of China; 4Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan Province, People’s Republic of China*These authors contributed equally to this workCorrespondence: Huaping LiuDepartment of Radiology, The Third Xiangya Hospital, Central South University, Tongzipo Road 138, Changsha, 410013, Hunan Province, People’s Republic of ChinaTel [phone]Fax [phone]Email [email protected]: To evaluate longitudinal computed tomography (CT) features and the predictive value of the initial CT and clinical characteristics for mortality in patients with severe/critical coronavirus disease 2019 (COVID-19) pneumonia.Methods: A retrospective analysis was performed on patients with COVID-19 pneumonia confirmed by laboratory. By excluding mild and common patients, 155 severe/critical patients with definite outcome were finally enrolled. A total of 516 CTs of 147 patients were divided into four stages according to the time after onset (stage 1, 1– 7 days; stage 2, 8– 14 days; stage 3, 15– 21 days, and stage 4, > 21 days). The evolving imaging features between the survival and non-survival groups were compared by using Chi-square, Fisher’s exact test, student’s t-test or Mann–Whitney U-test, as appropriate. The predictive value of clinical and CT features at admission for mortality was analysed through logistic regression analysis. To avoid overfitting caused by CT scores, CT scores were divided into two parts, which were combined with clinical variables, respectively, to construct the models.Results: Ground-glass opacities (GGO) patterns were predominant for stages 1 and 2 for both groups (both P> 0.05). The numbers of consolidation lesions increased in stage 3 in both groups (P=0.857), whereas the linear opacity increased in the survival group but decreased in the non-survival group (P=0.0049). In stage 4, the survival group predominantly presented linear opacity patterns, whereas the non-survival group mainly showed consolidation patterns (P=0.007). Clinical and imaging characteristics correlated with mortality; multivariate analyses revealed age > 71 years, neutrophil count > 6.38 × 109/L, aspartate aminotransferase (AST) > 58 IU/L, and CT score (total lesions score > 17 in model 1, GGO score > 14 and consolidation score > 2 in model 2) as independent risk factors (all P< 0.05). The areas under the curve of the six independent risk factors alone ranged from 0.65 to 0.75 and were 0.87 for model 2, 0.89 for model 1, and 0.92 for the six variables combined. Statistical differences were observed between Kaplan Meier curves of groups separated by cut-off values of these six variables (all P< 0.01).Conclusion: Longitudinal imaging features demonstrated differences between the two groups, which may help determine the patient’s prognosis. The initial CT score combined with age, AST, and neutrophil count is an excellent predictor for mortality in COVID-19 patients.Keywords: COVID-19, SARS-CoV-2, mortality, multivariate combined analysis, chest CT score, risk factors

Keywords

• covid-19
• sars-cov-2
• mortality
• multivariate combined analysis
• chest ct score
• risk factors