BACKGROUND: Since December 2019, when coronavirus disease 2019 (Covid-19) emerged in Wuhan city and rapidly spread throughout China, data have been needed on the clinical characteristics of the affected patients. METHODS: We extracted data regarding 1099 patients with laboratory-confirmed Covid-19 from 552 hospitals in 30 provinces, autonomous regions, and municipalities in mainland China through January 29, 2020. The primary composite end point was admission to an intensive care unit (ICU), the use of mechanical ventilation, or death. RESULTS: The median age of the patients was 47 years; 41.9% of the patients were female. The primary composite end point occurred in 67 patients (6.1%), including 5.0% who were admitted to the ICU, 2.3% who underwent invasive mechanical ventilation, and 1.4% who died. Only 1.9% of the patients had a history of direct contact with wildlife. Among nonresidents of Wuhan, 72.3% had contact with residents of Wuhan, including 31.3% who had visited the city. The most common symptoms were fever (43.8% on admission and 88.7% during hospitalization) and cough (67.8%). Diarrhea was uncommon (3.8%). The median incubation period was 4 days (interquartile range, 2 to 7). On admission, ground-glass opacity was the most common radiologic finding on chest computed tomography (CT) (56.4%). No radiographic or CT abnormality was found in 157 of 877 patients (17.9%) with nonsevere disease and in 5 of 173 patients (2.9%) with severe disease. Lymphocytopenia was present in 83.2% of the patients on admission. CONCLUSIONS: During the first 2 months of the current outbreak, Covid-19 spread rapidly throughout China and caused varying degrees of illness. Patients often presented without fever, and many did not have abnormal radiologic findings. (Funded by the National Health Commission of China and others.).

BACKGROUND: In December 2019, coronavirus 2019 (COVID-19) emerged in Wuhan and rapidly spread throughout China. METHODS: Demographic and clinical data of all confirmed cases with COVID-19 on admission at Tongji Hospital from 10 January to 12 February 2020 were collected and analyzed. The data on laboratory examinations, including peripheral lymphocyte subsets, were analyzed and compared between patients with severe and nonsevere infection. RESULTS: Of the 452 patients with COVID-19 recruited, 286 were diagnosed as having severe infection. The median age was 58 years and 235 were male. The most common symptoms were fever, shortness of breath, expectoration, fatigue, dry cough, and myalgia. Severe cases tend to have lower lymphocyte counts, higher leukocyte counts and neutrophil-lymphocyte ratio (NLR), as well as lower percentages of monocytes, eosinophils, and basophils. Most severe cases demonstrated elevated levels of infection-related biomarkers and inflammatory cytokines. The number of T cells significantly decreased, and were more impaired in severe cases. Both helper T (Th) cells and suppressor T cells in patients with COVID-19 were below normal levels, with lower levels of Th cells in the severe group. The percentage of naive Th cells increased and memory Th cells decreased in severe cases. Patients with COVID-19 also have lower levels of regulatory T cells, which are more obviously decreased in severe cases. CONCLUSIONS: The novel coronavirus might mainly act on lymphocytes, especially T lymphocytes. Surveillance of NLR and lymphocyte subsets is helpful in the early screening of critical illness, diagnosis, and treatment of COVID-19.

AIM: To accumulate evidence that indicated the key role played by virus-triggered inflammation in the 2019-novel coronavirus disease (COVID-19) which emerged in Wuhan City and rapidly spread throughout China. METHODS: Age, neutrophil(NEU)-to-lymphocyte (LYM) ratio (NLR), lymphocyte-to-monocyte (MON) ratio, platelet-to-lymphocyte ratio (PLR), and C-reactive protein (CRP) of 93 patients with laboratory confirmed COVID-19 were investigated and compared. The receiver operating characteristic curve was applied to determine the thresholds for five bio-markers, and their prognostic values were assessed via the Kaplan-Meier curve and multivariate COX regression models. RESULTS: The median age was 46.4 years old, and 37cases were females. A total of 27.8% of patients had been to Wuhan, and 73.1% had contacted with people from Wuhan. Fever (83.8%) and cough (70.9%) were the two most common symptoms. Elevated NLR and age were significantly associated with illness severity. The binary logistic analysis identified elevated NLR (hazard risk [HR] 2.46, 95% confidence interval [CI] 1.98-4.57) and age (HR 2.52, 95% CI 1.65-4.83) as independent factors for poor clinical outcome of COVID-19. NLR exhibited the largest area under the curve at 0.841, with the highest specificity (63.6%) and sensitivity (88%). CONCLUSIONS: Elevated age and NLR can be considered independent biomarkers for indicating poor clinical outcomes.

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.

To study the relationship between clinical indexes and the severity of coronavirus disease 2019 (COVID-19), and to explore its role in predicting the severity of COVID-19. Clinical data of 443 patients with COVID-19 admitted to our hospital were retrospectively analyzed, which were divided into nonsevere group (n = 304) and severe group (n = 139) according to their condition. Clinical indicators were compared between different groups. The differences in sex, age, the proportion of patients with combined heart disease, leukocyte, neutrophil-to-lymphocyte ratio (NLR), neutrophil, lymphocyte, platelet, D-dimer, C-reactive protein (CRP), procalcitonin, lactate dehydrogenase, and albumin on admission between the two groups were statistically significant (P < .05). Multivariate logistic regression analysis showed NLR and CRP were independent risk factors for severe COVID-19. Platelets were independent protective factors for severe COVID-19. The receiver operating characteristic (ROC) curve analysis demonstrated area under the curve of NLR, platelet, CRP, and combination was 0.737, 0.634, 0.734, and 0.774, respectively. NLR, CRP, and platelets can effectively assess the severity of COVID-19, among which NLR is the best predictor of severe COVID-19, and the combination of three clinical indicators can further predict severe COVID-19.

OBJECTIVE: To identify the biomarkers as early warning signals for severe COVID-19. METHODS: We retrospectively analyzed the clinical data of 63 patients with COVID- 19 from Hubei Provincial Hospital of Integrated Chinese and Western Medicine, including 32 moderate cases and 31 severe cases. The demographic data, underlying diseases, clinical manifestations and laboratory test results were compared between the two groups. Logistic regression analysis was performed to identify the factors that predicted the severity of COVID-19. The receiver- operating characteristic curve (ROC) of neutrophil/lymphocyte ratio (NLR) was calculated, and the area under the curve (AUC) was determined to estimate the optimal threshold of NLR for predicting severe cases of COVID-19. RESULTS: The patients with moderate and server COVID-19 showed significant differences in the rate of diabetes, NLR, serum amyloid A (SSA), C-reactive protein (CRP) and serum albumin (ALB) levels (P < 0.05). The co- morbidity of diabetes, NLR, SSA and CRP were found to positively correlate and ALB to inversely correlate with the severity of COVID-19 (P < 0.05). Multivariate logistic regression analysis showed that NLR was an independent risk factor for severe COVID-19 (OR=1.264, 95% CI: 1.046-1.526, P=0.015) with an AUC of 0.831 (95% CI: 0.730-0.932), an optimal diagnostic threshold of 4.795, a sensitivity of 0.839, and a specificity of 0.750. CONCLUSIONS: An increased NLR can serve as an early warning signal of severe COVID-19.

BACKGROUND: Several studies have described the clinical characteristics of patients with novel coronavirus (SARS-CoV-2) infected pneumonia (COVID-19), indicating severe patients tended to have higher neutrophil to lymphocyte ratio (NLR). Whether baseline NLR could be an independent predictor of in-hospital death in Chinese COVID-19 patients remains to be investigated. METHODS: A cohort of patients with COVID-19 admitted to the Zhongnan Hospital of Wuhan University from January 1 to February 29 was retrospectively analyzed. The baseline data of laboratory examinations, including NLR, were collected. Univariate and multivariate logistic regression models were developed to assess the independent relationship between the baseline NLR and in-hospital all-cause death. A sensitivity analysis was performed by converting NLR from a continuous variable to a categorical variable according to tertile. Interaction and stratified analyses were conducted as well. RESULTS: 245 COVID-19 patients were included in the final analyses, and the in-hospital mortality was 13.47%. Multivariate analysis demonstrated that there was 8% higher risk of in-hospital mortality for each unit increase in NLR (Odds ratio [OR]=1.08; 95% confidence interval [95% CI], 1.01 to 1.14; P=0.0147). Compared with patients in the lowest tertile, the NLR of patients in the highest tertile had a 15.04-fold higher risk of death (OR=16.04; 95% CI, 1.14 to 224.95; P=0.0395) after adjustment for potential confounders. Notably, the fully adjusted OR for mortality was 1.10 in males for each unit increase of NLR (OR=1.10; 95% CI, 1.02 to 1.19; P=0.016). CONCLUSIONS: NLR is an independent risk factor of the in-hospital mortality for COVID-19 patients especially for male. Assessment of NLR may help identify high risk individuals with COVID-19.

BACKGROUND: The dynamic changes of lymphocyte subsets and cytokines profiles of patients with novel coronavirus disease (COVID-19) and their correlation with the disease severity remain unclear. METHODS: Peripheral blood samples were longitudinally collected from 40 confirmed COVID-19 patients and examined for lymphocyte subsets by flow cytometry and cytokine profiles by specific immunoassays. FINDINGS: Of the 40 COVID-19 patients enrolled, 13 severe cases showed significant and sustained decreases in lymphocyte counts [0.6 (0.6-0.8)] but increases in neutrophil counts [4.7 (3.6-5.8)] than 27 mild cases [1.1 (0.8-1.4); 2.0 (1.5-2.9)]. Further analysis demonstrated significant decreases in the counts of T cells, especially CD8(+) T cells, as well as increases in IL-6, IL-10, IL-2 and IFN-gamma levels in the peripheral blood in the severe cases compared to those in the mild cases. T cell counts and cytokine levels in severe COVID-19 patients who survived the disease gradually recovered at later time points to levels that were comparable to those of the mild cases. Moreover, the neutrophil-to-lymphocyte ratio (NLR) (AUC=0.93) and neutrophil-to-CD8(+) T cell ratio (N8R) (AUC =0.94) were identified as powerful prognostic factors affecting the prognosis for severe COVID-19. INTERPRETATION: The degree of lymphopenia and a proinflammatory cytokine storm is higher in severe COVID-19 patients than in mild cases, and is associated with the disease severity. N8R and NLR may serve as a useful prognostic factor for early identification of severe COVID-19 cases. FUNDING: The National Natural Science Foundation of China, the National Science and Technology Major Project, the Health Commission of Hubei Province, Huazhong University of Science and Technology, and the Medical Faculty of the University of Duisburg-Essen and Stiftung Universitaetsmedizin, Hospital Essen, Germany.

AIMS: To investigate the clinical characteristics, laboratory findings and high- resolution CT (HRCT) features and to explore the risk factors for in-hospital death and complications of coronavirus disease 2019 (COVID-19) patients with diabetes. METHODS: From Dec 31, 2019, to Apr 5, 2020, a total of 132 laboratory-confirmed COVID-19 patients with diabetes from two hospitals were retrospectively included in our study. Clinical, laboratory and chest CT data were analyzed and compared between the two groups with an admission glucose level of 11 mmol/L (group 2). Logistic regression analyses were used to identify the risk factors associated with in-hospital death and complications. RESULTS: Of 132 patients, 15 died in hospital and 113 were discharged. Patients in group 2 were more likely to require intensive care unit care (21.4% vs. 9.2%), to develop acute respiratory distress syndrome (ARDS) (23.2% vs. 9.2%) and acute cardiac injury (12.5% vs. 1.3%), and had a higher death rate (19.6% vs. 5.3%) than group 1. In the multivariable analysis, patients with admission glucose of >11 mmol/l had an increased risk of death (OR: 7.629, 95%CI: 1.391-37.984) and in-hospital complications (OR: 3.232, 95%CI: 1.393-7.498). Admission d-dimer of >/=1.5 mug/mL (OR: 6.645, 95%CI: 1.212-36.444) and HRCT score of >/=10 (OR: 7.792, 95%CI: 2.195-28.958) were associated with increased odds of in-hospital death and complications, respectively. CONCLUSIONS: In COVID-19 patients with diabetes, poorly-controlled blood glucose (>11 mmol/L) may be associated with poor outcomes. Admission hyperglycemia, elevated d-dimer and high HRCT score are potential risk factors for adverse outcomes and death.