Journal of medical virology
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Detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is key to the clinical and epidemiological assessment of CoVID-19. We cross-validated manual and automated high-throughput testing for SARS-CoV-2-RNA, evaluated SARS-CoV-2 loads in nasopharyngeal-oropharyngeal swabs (NOPS), lower respiratory fluids, and plasma, and analyzed detection rates after lockdown and relaxation measures. ⋯ Manual and automated assays significantly correlated qualitatively and quantitatively. Following a successful lockdown, declining positive predictive values require independent dual-target confirmation for reliable assessment. Confirmatory and quantitative follow-up testing should be obtained within <5 days and consider lower respiratory fluids in symptomatic patients with SARS-CoV-2-negative NOPS.
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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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There is a debate in Argentina about the effectiveness of mandatory lockdown policies containing severe acute respiratory syndrome coronavirus type 2 disease. This policy has already 6 months long making it one of the longest in the world. The population effort to comply with the lockdown has been decreasing over time given the economic and social costs that it entails. ⋯ I use pool, fixed, and random effects panel data modeling and results show that lockdown in Argentina has been effective in reducing mobility but not in a way that reduces the rate of contagion. Strict lockdown seems to be effective in short periods of time and but extend it without complementary mitigation measures it losses effectiveness. The contagion rate seems to be discretely displaced in time and resurges amidst slowly increasing in mobility.
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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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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.