A Linear Relationship Between De Ritis Ratio and Mortality in Hospitalized Patients With COVID-19: A Secondary Analysis Based on a Large Retrospective Cohort Study

Background and aims Although some studies have identified a possible link between the De Ritis ratio and the mortality of patients with COVID-19, the predictive value and the optimal cut-value remain unclear. This study aimed to explore the correlation between the De Ritis ratio and mortality in hospitalized COVID-19 patients. Methods The data for this cohort study came from a retrospective cohort study that was carried out in a medical system in New York City. The primary outcome was the in-hospital mortality of included patients. The researchers ran multivariate Cox regression analyses, curve fitting, and subgroup analysis to support our findings. Overall survival in different De Ritis ratio groups was plotted as Kaplan–Meier survival curves. Results The study enrolled 4371 participants with COVID-19 from 1 March 2020 to 16 April 2020. The overall mortality was 24.8% (1082/4371). The curve fitting analyses indicated that the De Ritis ratio has a positive linear connection with mortality in COVID-19 patients. After adjusting for all covariates, participants with a De Ritis ratio ≥ 2 exhibited 1.29 times the risk of in-hospital mortality compared with those with a De Ritis ratio < 1 (HR 1.29, 95% CI 1.02–1.62, p=0.031). The P for trend was<0.05 for all models. Patients in the group with a De Ritis ratio ≥ 2 experienced the shortest survival time in the Kaplan–Meier survival analysis. Conclusion A higher baseline De Ritis ratio is correlated with a corresponding higher mortality among hospitalized people with COVID-19.

Auxiliary Diagnosis for COVID-19 with Deep Transfer Learning.

To assist physicians identify COVID-19 and its manifestations through the automatic COVID-19 recognition and classification in chest CT images with deep transfer learning. In this retrospective study, the used chest CT image dataset covered 422 subjects, including 72 confirmed COVID-19 subjects (260 studies, 30,171 images), 252 other pneumonia subjects (252 studies, 26,534 images) that contained 158 viral pneumonia subjects and 94 pulmonary tuberculosis subjects, and 98 normal subjects (98 studies, 29,838 images). In the experiment, subjects were split into training (70%), validation (15%) and testing (15%) sets. We utilized the convolutional blocks of ResNets pretrained on the public social image collections and modified the top fully connected layer to suit our task (the COVID-19 recognition). In addition, we tested the proposed method on a finegrained classification task; that is, the images of COVID-19 were further split into 3 main manifestations (ground-glass opacity with 12,924 images, consolidation with 7418 images and fibrotic streaks with 7338 images). Similarly, the data partitioning strategy of 70%-15%-15% was adopted. The best performance obtained by the pretrained ResNet50 model is 94.87% sensitivity, 88.46% specificity, 91.21% accuracy for COVID-19 versus all other groups, and an overall accuracy of 89.01% for the three-category classification in the testing set. Consistent performance was observed from the COVID-19 manifestation classification task on images basis, where the best overall accuracy of 94.08% and AUC of 0.993 were obtained by the pretrained ResNet18 (P < 0.05). All the proposed models have achieved much satisfying performance and were thus very promising in both the practical application and statistics. Transfer learning is worth for exploring to be applied in recognition and classification of COVID-19 on CT images with limited training data. It not only achieved higher sensitivity (COVID-19 vs the rest) but also took far less time than radiologists, which is expected to give the auxiliary diagnosis and reduce the workload for the radiologists.

COVID-19 in China: From epidemiology to treatment (Review).

Coronavirus disease 2019 (COVID-19) is a newly emerging infectious disease caused by the novel coronavirus SARS-CoV-2. It first became prevalent in Wuhan, Hubei, China in December 2019. COVID-19 was initially characterized by pneumonia of unknown etiology, accompanied by fever, dry cough and fatigue. Due to its highly infectious nature it rapidly led to widespread human infection, causing 80,924 confirmed cases and 3,140 mortalities in mainland China as of March 9, 2020. The present review highlights the prevalence of COVID-19 in China, the etiology, pathology, clinical presentation, laboratory and chest imaging tests, and treatment of this disease.