Public screening for COVID-19 in Wuhan, China and beware of the antibody positive in women and tumor patients.

The novel coronavirus disease 2019 (COVID-19) has become a global health emergency. Early detection and intervention are key factors for improving outcomes in patients with COVID-19. Real-time reverse transcriptase polymerase chain reaction-based molecular assays and antibody for detecting SARS-CoV-2 in respiratory specimens are the current reference standard for COVID-19 diagnosis. Clinical implications of different specimen types for nucleic acid and antibody testing of COVID-19 in Zhongnan hospital of Wuhan University were analyzed. Compared with health groups, tumor patients had higher rate of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (+/-) immunoglobulin M (IgM) (+) immunoglobulin G (IgG) (+). The rate of SARS-CoV-2 (-) IgM (+) IgG (-) or SARS-CoV-2 (-) IgM (-) IgG (+) in female was significantly higher than that in male. These results can help governments to take screening measures to prevent the COVID-19 pandemic again.

Impact of Treatment Regimens on Antibody Response to the SARS-CoV-2 Coronavirus.

The coronavirus disease 2019 (COVID-19) is widely spread and remains a global pandemic. Limited evidence on the systematic evaluation of the impact of treatment regimens on antibody responses exists. Our study aimed to analyze the role of antibody response on prognosis and determine factors influencing the IgG antibodies' seroconversion. A total of 1,111 patients with mild to moderate COVID-19 symptoms admitted to Leishenshan Hospital in Wuhan were retrospectively analyzed. A serologic SARS-CoV-2 IgM/IgG antibody test was performed on all the patients 21 days after the onset of symptoms. Patient clinical characteristics were compared. In the study, 42 patients progressed to critical illness, with 6 mortalities reported while 1,069 patients reported mild to moderate disease. Advanced age (P = 0.028), gasping (P < 0.001), dyspnea (P = 0.024), and IgG negativity (P = 0.006) were associated with progression to critical illness. The mortality rate in critically ill patients with IgG antibody was 6.45% (95% CI 1.12-22.84%) and 36.36% (95% CI 12.36-68.38%) in patients with no IgG antibody (P = 0.003). Symptomatic patients were more likely to develop IgG antibody responses than asymptomatic patients. Using univariable analysis, fever (P < 0.001), gasping (P = 0.048), cancer (P < 0.001), cephalosporin (P = 0.015), and chloroquine/hydroxychloroquine (P = 0.021) were associated with IgG response. In the multivariable analysis, fever, cancer, cephalosporins, and chloroquine/hydroxychloroquine correlated independently with IgG response. We determined that the absence of SARS-CoV-2 antibody IgG in the convalescent stage had a specific predictive role in critical illness progression. Importantly, risk factors affecting seropositivity were identified, and the effect of antimalarial drugs on antibody response was determined.

Novel biomarkers for the prediction of COVID-19 progression a retrospective, multi-center cohort study.

A pandemic designated as Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading worldwide. Up to date, there is no efficient biomarker for the timely prediction of the disease progression in patients. To analyze the inflammatory profiles of COVID-19 patients and demonstrate their implications for the illness progression of COVID-19. Retrospective analysis of 3,265 confirmed COVID-19 cases hospitalized between 10 January 2020, and 26 March 2020 in three medical centers in Wuhan, China. Patients were diagnosed as COVID-19 and hospitalized in Leishenshan Hospital, Zhongnan Hospital of Wuhan University and The Seventh Hospital of Wuhan, China. Univariable and multivariable logistic regression models were used to determine the possible risk factors for disease progression. Moreover, cutoff values, the sensitivity and specificity of inflammatory parameters for disease progression were determined by MedCalc Version 19.2.0. Age (95%CI, 1.017 to 1.048; P < 0.001), serum amyloid A protein (SAA) (95%CI, 1.216 to 1.396; P < 0.001) and erythrocyte sedimentation rate (ESR) (95%CI, 1.006 to 1.045; P < 0.001) were likely the risk factors for the disease progression. The Area under the curve (AUC) of SAA for the progression of COVID-19 was 0.923, with the best predictive cutoff value of SAA of 12.4 mg/L, with a sensitivity of 83.9% and a specificity of 97.67%. SAA-containing parameters are novel promising ones for predicting disease progression in COVID-19.

Scoring systems for predicting mortality for severe patients with COVID-19.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has been widely spread and caused tens of thousands of deaths, especially in patients with severe COVID-19. This analysis aimed to explore risk factors for mortality of severe COVID-19, and establish a scoring system to predict in-hospital deaths. METHODS: Patients with COVID-19 were retrospectively analyzed and clinical characteristics were compared. LASSO regression as well as multivariable analysis were used to screen variables and establish prediction model. FINDINGS: A total of 2529 patients with COVID-19 was retrospectively analyzed, and 452 eligible severe COVID-19 were used for finally analysis. In training cohort, the median age was 66•0 years while it was 73•0 years in non-survivors. Patients aged 60-75 years accounted for the largest proportion of infected populations and mortality toll. Anti-SARS-CoV-2 antibodies were monitored up to 54 days, and IgG levels reached the highest during 20-30 days. No differences were observed of antibody levels between severe and non-severe patients. About 60.2% of severe patients had complications. Among acute myocardial injury (AMI), acute kidney injury (AKI) and acute liver injury (ALI), the heart was the earliest injured organ, whereas the time from AKI to death was the shortest. Age, diabetes, coronary heart disease (CHD), percentage of lymphocytes (LYM%), procalcitonin (PCT), serum urea, C reactive protein and D-dimer (DD), were identified associated with mortality by LASSO binary logistic regression. Then multivariable analysis was performed to conclude that old age, CHD, LYM%, PCT and DD remained independent risk factors for mortality. Based on the above variables, a scoring system of COVID-19 (CSS) was established to divide patients into low-risk and high-risk groups. This model displayed good discrimination (AUC=0·919) and calibration (P=0·264). Complications in low-risk and high-risk groups were significantly different (P<0·05). Use of corticosteroids in low-risk groups increased hospital stays by 4·5 days (P=0·036) and durations of disease by 7·5 days (P=0·012) compared with no corticosteroids. INTERPRETATION: Old age, CHD, LYM%, PCT and DD were independently related to mortality. CSS was useful for predicting in-hospital mortality and complications, and it could help clinicians to identify high-risk patients with poor prognosis. FUNDING: This work was supported by the Key Project for Anti-2019 novel Coronavirus Pneumonia from the Ministry of Science and Technology, China (grant number 2020YFC0845500).