Clinical features and viral RNA shedding of imported and local cases with COVID-19 in Wenzhou, China.

ABSTRACT: Wenzhou had the highest number of confirmed novel coronavirus 2019 (COVID-19) cases outside the Hubei province. The aim of this study was to identify the difference in clinical features and viral RNA shedding between the imported and local COVID-19 cases in Wenzhou.All patients with confirmed COVID-19 admitted to Wenzhou Sixth People's Hospital, Wenzhou Central Hospital Medical Group, from January 17 to February 11, 2020, were enrolled in this study. Data was analyzed and compared for the imported and local cases with regard to epidemiological, demographic, clinical, radiological features, and laboratory findings. Outcomes for the enrolled participants were followed up until May 7, 2020.Of the 136 cases, 50 were imported from Wuhan. The median age was 45 years and 73 (53.7%) were men. The most common symptoms at onset were fever (104 [76.5%]) and cough (85[62.5%]). Pleural effusion was more common among imported cases compared to local cases. The white blood cell count, neutrophil count, lymphocyte count and platelet count of the imported cases were significantly lower than those of the local cases, while the prothrombin time was significantly longer than that of the local cases. Severe and critically ill patients accounted for 15.4% and 2.9%, respectively. The median duration of SARS-CoV-2 RNA shedding from symptom onset was 26 days (IQR 17-32.3 days) and there were no significant differences in duration of viral RNA shedding between the two groups.The study findings suggest that imported cases from Wuhan were more likely to be severe compared to the local cases in Wenzhou. However, there was no difference between imported and local cases on the viral shedding among the COVID patients.

Do post-COVID-19 symptoms exist? A longitudinal study of COVID-19 sequelae in Wenzhou, China.

OBJECTIVE: We conducted a cohort of tracing discharge patients of COVID-19. MATERIALS AND METHODS: We used the Mann-Whitney U test, χ2 test, or Fisher's exact test to compare differences between age groups and gender groups where appropriate. RESULTS: Our study provides insights into the nature and severity of medical conditions specific to survivors of COVID-19. CONCLUSIONS: It also highlights the potential mental health issues resulting from infectious disease outbreaks within communities.

OBJECTIFS: Nous avons suivi une cohorte de patients à la sortie du COVID-19. MATÉRIAUX ET MÉTHODES: Nous avons utilisé les test de Mann­Whitney U, de Fisher ou du Chi2 pour comparer les différences entre les groupes d'âge et de genre, le cas échéant. RÉSULTATS: Notre étude fournit un aperçu de la nature et de la gravité des troubles médicaux propres aux survivants du COVID-19. CONCLUSIONS: Elle met également en lumière les problèmes de santé mentale potentiels découlant des éclosions de maladies infectieuses dans les collectivités.

Epidemiological Features of 105 Patients Infected with the COVID-19.

OBJECTIVE: To investigate and evaluate the clinical features of patients infected with the 2019 novel coronavirus (COVID-19) outside of Wuhan. METHODS: 105 patients admitted to our hospital with clinical- and laboratory-confirmed COVID-19 infection were studied. Data were collected from January 17, 2020 to March 5, 2020. RESULTS: 105 patients (57 male and 48 female) were confirmed to have COVID-19 infection. Among the 105 patients, 55 (52%) had made short trips to Wuhan during the two weeks before the onset of illness, and these were the first-generation confirmed cases. An exact date of close contact with someone in Wenzhou with confirmed or suspected COVID-19 infection from Wuhan (the second-generation confirmed cases) could be provided by 38 (36%) patients. Of the remaining patients, six (6%; the third-generation confirmed cases) were familial clusters of the second-generation confirmed cases, three (3%) had no definite epidemiological features, and 16 (15%) were from the same location as for the case report. CONCLUSION: Due to the infectiousness of COVID-19, patients with infections should be diagnosed and treated as early as possible after developing fever symptoms or showing other clinical characteristics or imaging features. With respect to high-risk cases, we must focus on any complications that arise and take effective measures to treat them immediately. This will significantly improve the prognosis of patients with severe infections.

Clinical differences in chest CT characteristics between the progression and remission stages of patients with COVID-19 pneumonia.

INTRODUCTION: Computed tomography (CT) can be effective for the early screening and diagnosis of COVID-19. This study aimed to investigate the distinctive CT characteristics of two stages of the disease (progression and remission). METHODS: We included all COVID-19 patients admitted to Wenzhou Central Hospital from January to February, 2020. Patients underwent multiple chest CT scans at intervals of 3-10 days. CT features were recorded, such as the lesion lobe, distribution characteristics (subpleural, scattered or diffused), shape of the lesion, maximum size of the lesion, lesion morphology (ground-glass opacity, GGO) and consolidation features. When consolidation was positive, the boundary was identified to determine its clarity. RESULTS: The ratios of some representative features differed between the remission stage and the progression phase, such as round-shape lesion (8.0% vs 34.4%), GGO (65.0% vs 87.5%), consolidation (62.0% vs 31.3%), large cable sign (59.0% vs 9.4%) and crazy-paving sign (20.0% vs 50.0%). Using these features, we pooled all the CT data (n = 132) and established a logistic regression model to predict the current development stage. The variables consolidation, boundary feature, large cable sign and crazy-paving sign were the most significant factors, based on a variable named "prediction of progression or remission" (PPR) that we constructed. The ROC curve showed that PPR had an AUC of 0.882 (cutoff value = 0.66, sensitivity = 0.75, specificity = 0.875). CONCLUSION: CT characteristics, in particular, round shape, GGO, consolidation, large cable sign, and crazy-paving sign, may increase the recognition of the intrapulmonary development of COVID-19.

Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity

The virus SARS-CoV2, which causes coronavirus disease (COVID-19) has become a pandemic and has spread to every inhabited continent. Given the increasing caseload, there is an urgent need to augment clinical skills in order to identify from among the many mild cases the few that will progress to critical illness. We present a first step towards building an artificial intelligence (AI) framework, with predictive analytics (PA) capabilities applied to real patient data, to provide rapid clinical decision-making support. COVID-19 has presented a pressing need as a) clinicians are still developing clinical acumen to this novel disease and b) resource limitations in a surging pandemic require difficult resource allocation decisions. The objectives of this research are: (1) to algorithmically identify the combinations of clinical characteristics of COVID-19 that predict outcomes, and (2) to develop a tool with AI capabilities that will predict patients at risk for more severe illness on initial presentation. The predictive models learn from historical data to help predict who will develop acute respiratory distress syndrome (ARDS), a severe outcome in COVID-19. Our results, based on data from two hospitals in Wenzhou, Zhejiang, China, identified features on initial presentation with COVID-19 that were most predictive of later development of ARDS. A mildly elevated alanine aminotransferase (ALT) (a liver enzyme), the presence of myalgias (body aches), and an elevated hemoglobin (red blood cells), in this order, are the clinical features, on presentation, that are the most predictive. The predictive models that learned from historical data of patients from these two hospitals achieved 70% to 80% accuracy in predicting severe cases.

Declined serum high density lipoprotein cholesterol is associated with the severity of COVID-19 infection.

BACKGROUND: COVID-19 infection is epidemic worldwide. We describe the serum lipid profile of the patients with COVID-19 infection. METHODS: In this retrospective study, we collected the first clinical laboratory data of 114 patients on admission, and 80 healthy controls. Meanwhile, we monitored the serum lipid profile, COVID-19 nucleic acid and chest CT scan of a severe patient from the early stage of infection to the recovery period for a total of 80 days. RESULTS: Compared with the healthy controls, the patients had sharply decreased concentrations of total cholesterol, HDL-cholesterol and LDL-cholesterol (P < 0.001). Among the patients, HDL-cholesterol concentration in severe groups was significantly lower than the common groups [1.01 (0.88-1.20) vs 1.21 (1.02-1.48) mmol/l, P < 0.001]. The lipid profile of a severe patient showed that serum cholesterol concentration significantly decreased in the early stage and returned to be normal in the recovery period. Moreover, the change of HDL-cholesterol in this patient was consistent with the results of nucleic acid tests and chest CT scans. In correlation analysis, HDL-cholesterol concentration was negatively correlated with C-reactive protein (CRP, r = -0.396, P < 0.001) and positively correlated with lymphocytes (r = 0.336, P < 0.001). The area under curve (AUC) in receiver operating characteristic (ROC) of HDL-cholesterol was 0.732 (P < 0.001), and, the adjusted odd ratio (OR) of HDL-cholesterol was 0.023 (95% CI 0.002-0.227). CONCLUSIONS: Decreased serum HDL-cholesterol is associated with the severity of COVID-19 infection.

Abnormalities of peripheral blood system in patients with COVID-19 in Wenzhou, China.

BACKGROUND: In December 2019, coronavirus disease 2019 (COVID-19) was first found in Wuhan, China and soon was reported all around the world. METHODS: All confirmed cases with COVID-19 in Wenzhou from January 19 to February 20, 2020, were collected and analyzed. Of the 116 patients with COVID-19, 27 were diagnosed as severe cases. Among severe cases, 9 were treated in ICU. The data of blood routine examination were analyzed and compared among common patients (as common group), severe patients admitted to intensive care unit (as severe ICU group) and severe patients not admitted to ICU (as severe non-ICU group). The blood routine examination results were dynamically observed in the above groups after admission. RESULTS: Patients with COVID-19 have lower counts of leucocytes, lymphocytes, eosinophils, platelets, and hemoglobin, but have higher neutrophil-lymphocyte ratio (NLR) and monocyte-lymphocyte ratio (MLR), which were compared with controls (P < 0.001). In severe ICU group, patients have the lowest count of lymphocytes, but the highest neutrophil count and NLR among the above three groups (all P values < 0.05); NLR and MLR indicators were combined for diagnostic efficacy analysis of severe COVID-19, and its area under the curve reached 0.925. The odds ratio of the delay in days to the start of the increase of eosinophil count for predicting the outcome of patients with severe COVID-19 was 2.291 after age adjusted. CONCLUSIONS: Patients with COVID-19 have abnormal peripheral blood routine examination results. Dynamic surveillance of peripheral blood system especially eosinophils is helpful in the prediction of severe COVID-19 cases.

Indirect Virus Transmission in Cluster of COVID-19 Cases, Wenzhou, China, 2020.

To determine possible modes of virus transmission, we investigated a cluster of coronavirus disease cases associated with a shopping mall in Wenzhou, China. Data indicated that indirect transmission of the causative virus occurred, perhaps resulting from virus contamination of common objects, virus aerosolization in a confined space, or spread from asymptomatic infected persons.