Temporal Profiles of Antibody Responses, Cytokines, and Survival of COVID-19 Patients: A Retrospective Cohort.

The longitudinal immunologic status of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected patients and its association with the clinical outcome are barely known. Thus, we sought to analyze the temporal profiles of specific antibodies, as well as the associations between the antibodies, proinflammatory cytokines, and survival of patients with coronavirus disease 2019 (COVID-19). A total of 1830 laboratory-confirmed COVID-19 cases were recruited. The temporal profiles of the virus, antibodies, and cytokines of the patients until 12 weeks since illness onset were fitted by the locally weighted scatter plot smoothing method. The mediation effect of cytokines on the associations between antibody responses and survival were explored by mediation analysis. Of the 1830 patients, 1435 were detectable for SARS-CoV-2, while 395 were positive in specific antibodies only. Of the 1435 patients, 2.4% presented seroconversion for neither immunoglobulin G (IgG) nor immunoglobulin M (IgM) during hospitalization. The seropositive rates of IgG and IgM were 29.6% and 48.1%, respectively, in the first week, and plateaued within five weeks. For the patients discharged from the hospital, the IgM decreased slowly, while high levels of IgG were maintained at around 188 AU·mL-1 for the 12 weeks since illness onset. In contrast, in the patients who subsequently died, IgM declined rapidly and IgG dropped to 87 AU·mL-1 at the twelfth week. Elevated interleukin-6, interleukin-8, interleukin-10, interleukin-1ß, interleukin-2R, and tumor necrosis factor-α levels were observed in the deceased patients in comparison with the discharged patients, and 12.5% of the association between IgG level and mortality risk was mediated by these cytokines. Our study deciphers the temporal profiles of SARS-CoV-2-specific antibodies within the 12 weeks since illness onset and indicates the protective effect of antibody response on survival, which may help to guide prognosis estimation.

Risk factors for mortality in patients over 70 years old with COVID-19 in Wuhan at the early break: retrospective case series.

BACKGROUND: Elderly patients with COVID-19 were shown to have a high case-fatality rate. We aimed to explore the risk factors associated with death in patients over 70 years old (yr). METHODS: In this retrospective study, we enrolled consecutively hospitalized patients over 70 yr with COVID-19 between January 20 and February 15, 2020 in Renmin Hospital of Wuhan University. Epidemiological, demographic, and clinical data were collected. Clinical subtypes, including mild, moderate, severe, and critical types, were used to evaluate the severity of disease. Patients were classified into two groups: survivor and non-survivor groups. Clinical data were compared between the two groups. Univariable and multivariable Cox regression methods were used to explore the risk factors. RESULTS: A total of 147 patients were enrolled. The case-fatality rate was 28.6%. Multivariable Cox proportional hazard regression showed that clinical subtypes, including the severe type (HR = 2.983, 95% CI: 1.231-7.226, P = 0.016) and the critical type (HR = 3.267, 95%CI: 1.009-10.576, P = 0.048), were associated with increasing risk of death when compared with the general type. Blood urea nitrogen greater than 9.5 mmol/L (HR = 2.805, 95% CI: 1.141-6.892, P = 0.025) on admission was an independent risk factor for death among laboratory findings. CONCLUSION: The patients over 70 yr with COVID-19 had a high case-fatality rate. The risk factors, including clinical subtypes and blood urea nitrogen greater than 9.5 mmol/L, could help physicians to identify elderly patients with poor clinical outcomes at an early stage.

Risk Factors of Coronavirus Disease 2019-Related Mortality and Optimal Treatment Regimens: A Retrospective Study.

BACKGROUND Coronavirus disease 2019 (COVID-19) is spreading rapidly worldwide, and scientists are trying to find a way to overcome the disease. We explored the risk factors that influence patient outcomes, including treatment regimens, which can provide a reference for further treatment. MATERIAL AND METHODS A retrospective cohort study analysis was performed using data from 97 patients with COVID-19 who visited Wuhan Union Hospital from February 2020 to March 2020. We collected data on demographics, comorbidities, clinical manifestations, laboratory tests, treatment methods, outcomes, and complications. Patients were divided into a recovered group and a deceased group. We compared the differences between the 2 groups and analyzed risk factors influencing the treatment effect. RESULTS Seventy-six patients recovered and 21 died. The average age and body mass index (BMI) of the deceased group were significantly higher than those of the recovered group (69.81±6.80 years vs 60.79±11.28 years, P<0.001 and 24.95±3.14 kg/m² vs 23.09±2.97 kg/m², P=0.014, respectively). The combination of antiviral drugs and supportive therapy appears to be associated with the lowest mortality (P<0.05). Multivariate Cox regression analysis revealed that age, BMI, H-CRP, shock, and acute respiratory distress syndrome (ARDS) were independent risk factors for patients with COVID-19 (P<0.05). CONCLUSIONS Elderly patients and those with a high BMI, as well as patients who experience shock and ARDS, may have a higher risk of death from COVID-19. The combination of antiviral drugs and supportive therapy appears to be associated with lower mortality, although further research is needed.

A New Geo-Propagation Model of Event Evolution Chain Based on Public Opinion and Epidemic Coupling.

The online public opinion is the sum of public views, attitudes and emotions spread on major public health emergencies through the Internet, which maps out the scope of influence and the disaster situation of public health events in real space. Based on the multi-source data of COVID-19 in the context of a global pandemic, this paper analyzes the propagation rules of disasters in the coupling of the spatial dimension of geographic reality and the dimension of network public opinion, and constructs a new gravity model-complex network-based geographic propagation model of the evolution chain of typical public health events. The strength of the model is that it quantifies the extent of the impact of the epidemic area on the surrounding area and the spread of the epidemic, constructing an interaction between the geographical reality dimension and online public opinion dimension. The results show that: The heterogeneity in the direction of social media discussions before and after the "closure" of Wuhan is evident, with the center of gravity clearly shifting across the Yangtze River and the cyclical changing in public sentiment; the network model based on the evolutionary chain has a significant community structure in geographic space, divided into seven regions with a modularity of 0.793; there are multiple key infection trigger nodes in the network, with a spatially polycentric infection distribution.