Journal of medical virology
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We aimed to evaluate the rates of false-positive test results of three rapid diagnostic tests (RDTs) for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific immunoglobulin G (IgG) and IgM detection. Two serum panels from patients hospitalized in Paris, France, and from patients living in Bangui, Central African Republic, acquired before the 2019 COVID-19 outbreak, were tested by 3 CE IVD-labeled RDTs for SARS-CoV-2 serology (BIOSYNEX® COVID-19 BSS [IgG/IgM]; SIENNA™ COVID-19 IgG/IgM Rapid Test Cassette; NG-Test® IgG-IgM COVID-19). Detectable IgG or IgM reactivities could be observed in 31 (3.43%) of the 902 IgG and IgM bands of the 3 RDTs used with all pre-epidemic sera. The frequencies of IgG/IgM reactivities were similar for European (3.20%) and African (3.55%) sera. ⋯ The test NG-Test® IgG-IgM COVID-19 showed the highest rates of IgG or IgM reactivities (6.12% [18/294]), while the test BIOSYNEX® COVID-19 BSS (IgG/IgM) showed the lowest rate (1.36% [4/294]). Some combinations of 2 RDTs in series allowed decreasing significantly the risk of false-positive test results. Our observations point to the risk of false-positive reactivities when using currently available RDT for SARS-CoV-2 serological screening, especially for the IgM band, even if the test is CE IVD-labeled and approved by national health authorities, and provide the rational basis for confirmatory testing by another RDT in case of positive initial screening.
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Hyperglycemia commonly occurs in severe cases with COVID-19. In this study, we explored the associations between admission fasting plasma glucose (FPG) and 28-day mortality in COVID-19 patients. In this single centre retrospective study, 263 adult patients with COVID-19 were included. ⋯ Multivariable Cox regression analyses showed that age (per 10-year increase) (hazard ratio [HR], 1.41; 95% confidence interval [CI], 1.13-1.74), admission FPG between 7.0 and 11.0 and ≥11.1 mmol/L (HR, 1.90; 95% CI, 1.11-3.25 and HR, 2.09; 95% CI, 1.21-3.64, respectively), chronic obstructive pulmonary disease (HR, 2.89; 95% CI, 1.31-6.39), and cardiac injury (HR, 2.14; 95% CI, 1.33-3.47) were independent predictors of 28-day mortality in COVID-19 patients. Hyperglycemia on admission predicted worse outcome in hospitalized patients with COVID-19. Intensive monitoring and optimal glycemic control may improve the prognosis of COVID-19 patients.
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This study aims to screen useful predictors of critical cases among coronavirus disease 2019 (COVID-19) patients and to develop a simple-to-use nomogram for clinical utility. A retrospective study was conducted that consisted of a primary cohort with 315 COVID-19 patients and two validation cohorts with 69 and 123 patients, respectively. Logistic regression analyses were used to identify the independent risks of progression to critical. ⋯ Good discrimination (C-index, 0.882 and 0.906) and calibration were also noted on applying the nomogram in two validation cohorts. The clinical relevance of the nomogram was justified by the decision curve and clinical impact curve analysis. This study presents an individualized prediction nomogram incorporating six clinical characteristics, which can be conveniently applied to assess an individual's risk of progressing to critical COVID-19.
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Observational Study
Tocilizumab in hospitalized patients with COVID-19: Clinical outcomes, inflammatory marker kinetics, and safety.
Coronavirus disease 2019 (COVID-19) due to infection with severe acute respiratory syndrome coronavirus 2 causes substantial morbidity. Tocilizumab, an interleukin-6 receptor antagonist, might improve outcomes by mitigating inflammation. We conducted a retrospective study of patients admitted to the University of Washington Hospital system with COVID-19 and requiring supplemental oxygen. ⋯ A numerically higher proportion of tocilizumab-treated patients had subsequent infections, transaminitis, and cytopenias. Tocilizumab did not improve outcomes in hospitalized patients with COVID-19. However, this study was not powered to detect small differences, and there remains the possibility for a survival benefit.
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To develop and validate a nomogram using on admission data to predict in-hospital survival probabilities of coronavirus disease 2019 (COVID-19) patients. We analyzed 855 COVID-19 patients with 52 variables. The least absolute shrinkage and selection operator regression and multivariate Cox analyses were used to screen significant factors associated with in-hospital mortality. ⋯ Decision curve analysis showed relatively wide ranges of threshold probability, suggesting a high clinical value of the nomogram. Neutrophil, C-reactive protein, IL-6, d-dimer, prothrombin time, and myoglobin levels were significantly correlated with in-hospital mortality of COVID-19 patients. Demonstrating satisfactory discrimination and calibration, this model could predict patient outcomes as early as on admission and might serve as a useful triage tool for clinical decision making.