Combining initial chest CT with clinical variables in differentiating coronavirus disease 2019 (COVID-19) pneumonia from influenza pneumonia.

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.

Imaging features and mechanisms of novel coronavirus pneumonia (COVID-19): Study Protocol Clinical Trial (SPIRIT Compliant).

INTRODUCTION: A novel coronavirus, tentatively designated as 2019 Novel Coronavirus (2019-nCoV), now called severe acute respiratory syndrome coronavirus 2, emerged in Wuhan, China, at the end of 2019 and which continues to expand. On February 11, 2020, the World Health Organization (WHO) named the disease coronavirus disease 2019 (COVID-19). On February 28, WHO increased our assessment of the risk of spread and the risk of impact of COVID-19 to very high at a global level. The COVID-19 poses significant threats to international health.Computed tomography (CT) has been an important imaging modality in assisting in the diagnosis and management of patients withCOVID-19. Some retrospective imaging studies have reported chest CT findings of COVID-19 in the past 2 months, suggesting that several CT findings may be characteristic. To our knowledge, there has been no prospective multicentre imaging study of COVID-19 to date.We proposed a hypothesis: There are some specific CT features on Chest CT of COVID-19 patients. And the mechanism of these CT features is explicable based on pathological findings. OBJECTIVE: To investigate the specific CT features of COVID-19 and the formation mechanism of these CT features. METHOD: This study is a prospective multicenter observational study. We will recruit 100 patients with COVID-19 at 55 hospitals. All patients undergo chest CT examination with the same scan protocol. The distribution and morphology of lesions on chest CT, clinical data will be recorded. A number of patients will be pathologically examined after permission is granted. The data of these three aspects will be analyzed synthetically. DISCUSSION: This study will help us to identify the chest CT features of COVID-19 and its mechanism. ETHICS AND DISSEMINATION: This retrospective study was approved by the Biomedical Research Ethics Committee of West China Hospital of Sichuan University (No. 2020-140). Written informed consent will be obtained from all study participants prior to enrollment in the study. To protect privacy of participants, all private information were kept anonymous. The results will be published in a peer-reviewed journal and will be disseminated electronically and in print regardless of results.