Zhao Shuang, Huang Zixing, Zeng Hanjiang, Chen Zhixia, Luo Fengming, Zhang Chongwei, Song Bin
Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Sci Rep. 2021 Mar 19;11(1):6422. doi: 10.1038/s41598-021-85779-1.
Coronavirus disease 2019 (COVID-19) has spread in more than 100 countries and regions around the world, raising grave global concerns. COVID-19 has a similar pattern of infection, clinical symptoms, and chest imaging findings to influenza pneumonia. In this retrospective study, we analysed clinical and chest CT data of 24 patients with COVID-19 and 79 patients with influenza pneumonia. Univariate analysis demonstrated that the temperature, systolic pressure, cough and sputum production could distinguish COVID-19 from influenza pneumonia. The diagnostic sensitivity and specificity for the clinical features are 0.783 and 0.747, and the AUC value is 0.819. Univariate analysis demonstrates that nine CT features, central-peripheral distribution, superior-inferior distribution, anterior-posterior distribution, patches of GGO, GGO nodule, vascular enlargement in GGO, air bronchogram, bronchiectasis within focus, interlobular septal thickening, could distinguish COVID-19 from influenza pneumonia. The diagnostic sensitivity and specificity for the CT features are 0.750 and 0.962, and the AUC value is 0.927. Finally, a multivariate logistic regression model combined the variables from the clinical variables and CT features models was made. The combined model contained six features: systolic blood pressure, sputum production, vascular enlargement in the GGO, GGO nodule, central-peripheral distribution and bronchiectasis within focus. The diagnostic sensitivity and specificity for the combined features are 0.87 and 0.96, and the AUC value is 0.961. In conclusion, some CT features or clinical variables can differentiate COVID-19 from influenza pneumonia. Moreover, CT features combined with clinical variables had higher diagnostic performance.
2019冠状病毒病（COVID-19）已在全球100多个国家和地区传播，引起了全球的严重关注。COVID-19在感染模式、临床症状和胸部影像学表现方面与流感肺炎相似。在这项回顾性研究中，我们分析了24例COVID-19患者和79例流感肺炎患者的临床及胸部CT数据。单因素分析表明，体温、收缩压、咳嗽和咳痰情况可将COVID-19与流感肺炎区分开来。临床特征的诊断敏感性和特异性分别为0.783和0.747，AUC值为0.819。单因素分析表明，9个CT特征，即中央-外周分布、上下分布、前后分布、磨玻璃影（GGO）斑片、GGO结节、GGO内血管增粗、空气支气管征、病灶内支气管扩张、小叶间隔增厚，可将COVID-19与流感肺炎区分开来。CT特征的诊断敏感性和特异性分别为0.750和0.962，AUC值为0.927。最后，构建了一个多因素逻辑回归模型，该模型结合了临床变量模型和CT特征模型中的变量。联合模型包含6个特征：收缩压、咳痰、GGO内血管增粗、GGO结节、中央-外周分布和病灶内支气管扩张。联合特征的诊断敏感性和特异性分别为0.87和0.96，AUC值为0.961。总之，一些CT特征或临床变量可将COVID-19与流感肺炎区分开来。此外，CT特征与临床变量相结合具有更高的诊断效能。
