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      Serum Prealbumin Concentrations, COVID-19 Severity, and Mortality: A Systematic Review and Meta-Analysis

      systematic-review
      1 , * , 2
      Frontiers in Medicine
      Frontiers Media S.A.
      prealbumin, COVID-19, disease severity, mortality, biomarker

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          Abstract

          Excessive inflammation and malnutrition are associated with coronavirus disease 2019 (COVID-19) severity and mortality. Combined biomarkers of malnutrition and inflammation, such as serum prealbumin, might be particularly attractive for early risk stratification. We conducted a systematic review and meta-analysis of studies reporting serum prealbumin in patients with COVID-19. We searched PubMed, Web of Science and Scopus, between January and November 2020, for studies reporting data on serum prealbumin, COVID-19 severity, defined as severe illness, prolonged viral load, receiving mechanical ventilation or admitted to intensive care unit (ICU), and mortality. Nineteen studies in 4,616 COVID-19 patients were included in the meta-analysis. Pooled results showed that serum prealbumin concentrations were significantly lower in patients with severe disease and non-survivors (standard mean difference, SMD, −0.92, 95% CI, −1.10 to −0.74, P < 0.001). Extreme heterogeneity was observed ( I 2 = 77.9%; P < 0.001). In sensitivity analysis, the effect size was not significantly affected when each study was in turn removed (range between −0.86 and −0.95). The Begg's ( P = 0.06) and Egger's t-tests ( P = 0.26) did not show publication bias. Pooled SMD values were significantly and negatively associated with age ( t = −2.18, P = 0.045) and C-reactive protein ( t = −3.85, P = 0.002). In our meta-analysis, lower serum prealbumin concentrations were significantly associated with COVID-19 severity and mortality. This combined marker of malnutrition and inflammation might assist with early risk stratification and management in this group.

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          Most cited references52

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          Quantifying heterogeneity in a meta-analysis.

          The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright 2002 John Wiley & Sons, Ltd.
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            Dexamethasone in Hospitalized Patients with Covid-19 — Preliminary Report

            Abstract Background Coronavirus disease 2019 (Covid-19) is associated with diffuse lung damage. Glucocorticoids may modulate inflammation-mediated lung injury and thereby reduce progression to respiratory failure and death. Methods In this controlled, open-label trial comparing a range of possible treatments in patients who were hospitalized with Covid-19, we randomly assigned patients to receive oral or intravenous dexamethasone (at a dose of 6 mg once daily) for up to 10 days or to receive usual care alone. The primary outcome was 28-day mortality. Here, we report the preliminary results of this comparison. Results A total of 2104 patients were assigned to receive dexamethasone and 4321 to receive usual care. Overall, 482 patients (22.9%) in the dexamethasone group and 1110 patients (25.7%) in the usual care group died within 28 days after randomization (age-adjusted rate ratio, 0.83; 95% confidence interval [CI], 0.75 to 0.93; P<0.001). The proportional and absolute between-group differences in mortality varied considerably according to the level of respiratory support that the patients were receiving at the time of randomization. In the dexamethasone group, the incidence of death was lower than that in the usual care group among patients receiving invasive mechanical ventilation (29.3% vs. 41.4%; rate ratio, 0.64; 95% CI, 0.51 to 0.81) and among those receiving oxygen without invasive mechanical ventilation (23.3% vs. 26.2%; rate ratio, 0.82; 95% CI, 0.72 to 0.94) but not among those who were receiving no respiratory support at randomization (17.8% vs. 14.0%; rate ratio, 1.19; 95% CI, 0.91 to 1.55). Conclusions In patients hospitalized with Covid-19, the use of dexamethasone resulted in lower 28-day mortality among those who were receiving either invasive mechanical ventilation or oxygen alone at randomization but not among those receiving no respiratory support. (Funded by the Medical Research Council and National Institute for Health Research and others; RECOVERY ClinicalTrials.gov number, NCT04381936; ISRCTN number, 50189673.)
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              Risk Factors Associated With Acute Respiratory Distress Syndrome and Death in Patients With Coronavirus Disease 2019 Pneumonia in Wuhan, China

              Coronavirus disease 2019 (COVID-19) is an emerging infectious disease that was first reported in Wuhan, China, and has subsequently spread worldwide. Risk factors for the clinical outcomes of COVID-19 pneumonia have not yet been well delineated.
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                Author and article information

                Contributors
                Journal
                Front Med (Lausanne)
                Front Med (Lausanne)
                Front. Med.
                Frontiers in Medicine
                Frontiers Media S.A.
                2296-858X
                26 January 2021
                2021
                26 January 2021
                : 8
                : 638529
                Affiliations
                [1] 1Department of Biomedical Sciences, University of Sassari , Sassari, Italy
                [2] 2Discipline of Clinical Pharmacology, College of Medicine and Public Health, Flinders University and Flinders Medical Centre , Adelaide, SA, Australia
                Author notes

                Edited by: Fabrizio Ricci, University of Studies G. d'Annunzio Chieti and Pescara, Italy

                Reviewed by: Imad Omar Al Kassaa, Lebanese University, Lebanon; Liliane Okdah, King Abdullah International Medical Research Center (KAIMRC), Saudi Arabia

                *Correspondence: Angelo Zinellu azinellu@ 123456uniss.it

                This article was submitted to Infectious Diseases-Surveillance, Prevention and Treatment, a section of the journal Frontiers in Medicine

                Article
                10.3389/fmed.2021.638529
                7870685
                33575267
                bbf4c2c7-7001-414f-81a8-cce4d55ecc1c
                Copyright © 2021 Zinellu and Mangoni.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 07 December 2020
                : 04 January 2021
                Page count
                Figures: 7, Tables: 1, Equations: 0, References: 53, Pages: 9, Words: 4979
                Categories
                Medicine
                Systematic Review

                prealbumin,covid-19,disease severity,mortality,biomarker
                prealbumin, covid-19, disease severity, mortality, biomarker

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