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      Comparative efficacy and safety of pharmacological interventions for the treatment of COVID-19: A systematic review and network meta-analysis

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          Abstract

          Background

          Numerous clinical trials and observational studies have investigated various pharmacological agents as potential treatment for Coronavirus Disease 2019 (COVID-19), but the results are heterogeneous and sometimes even contradictory to one another, making it difficult for clinicians to determine which treatments are truly effective.

          Methods and findings

          We carried out a systematic review and network meta-analysis (NMA) to systematically evaluate the comparative efficacy and safety of pharmacological interventions and the level of evidence behind each treatment regimen in different clinical settings. Both published and unpublished randomized controlled trials (RCTs) and confounding-adjusted observational studies which met our predefined eligibility criteria were collected. We included studies investigating the effect of pharmacological management of patients hospitalized for COVID-19 management. Mild patients who do not require hospitalization or have self-limiting disease courses were not eligible for our NMA. A total of 110 studies (40 RCTs and 70 observational studies) were included. PubMed, Google Scholar, MEDLINE, the Cochrane Library, medRxiv, SSRN, WHO International Clinical Trials Registry Platform, and ClinicalTrials.gov were searched from the beginning of 2020 to August 24, 2020. Studies from Asia (41 countries, 37.2%), Europe (28 countries, 25.4%), North America (24 countries, 21.8%), South America (5 countries, 4.5%), and Middle East (6 countries, 5.4%), and additional 6 multinational studies (5.4%) were included in our analyses. The outcomes of interest were mortality, progression to severe disease (severe pneumonia, admission to intensive care unit (ICU), and/or mechanical ventilation), viral clearance rate, QT prolongation, fatal cardiac complications, and noncardiac serious adverse events. Based on RCTs, the risk of progression to severe course and mortality was significantly reduced with corticosteroids (odds ratio (OR) 0.23, 95% confidence interval (CI) 0.06 to 0.86, p = 0.032, and OR 0.78, 95% CI 0.66 to 0.91, p = 0.002, respectively) and remdesivir (OR 0.29, 95% CI 0.17 to 0.50, p < 0.001, and OR 0.62, 95% CI 0.39 to 0.98, p = 0.041, respectively) compared to standard care for moderate to severe COVID-19 patients in non-ICU; corticosteroids were also shown to reduce mortality rate (OR 0.54, 95% CI 0.40 to 0.73, p < 0.001) for critically ill patients in ICU. In analyses including observational studies, interferon-alpha (OR 0.05, 95% CI 0.01 to 0.39, p = 0.004), itolizumab (OR 0.10, 95% CI 0.01 to 0.92, p = 0.042), sofosbuvir plus daclatasvir (OR 0.26, 95% CI 0.07 to 0.88, p = 0.030), anakinra (OR 0.30, 95% CI 0.11 to 0.82, p = 0.019), tocilizumab (OR 0.43, 95% CI 0.30 to 0.60, p < 0.001), and convalescent plasma (OR 0.48, 95% CI 0.24 to 0.96, p = 0.038) were associated with reduced mortality rate in non-ICU setting, while high-dose intravenous immunoglobulin (IVIG) (OR 0.13, 95% CI 0.03 to 0.49, p = 0.003), ivermectin (OR 0.15, 95% CI 0.04 to 0.57, p = 0.005), and tocilizumab (OR 0.62, 95% CI 0.42 to 0.90, p = 0.012) were associated with reduced mortality rate in critically ill patients. Convalescent plasma was the only treatment option that was associated with improved viral clearance rate at 2 weeks compared to standard care (OR 11.39, 95% CI 3.91 to 33.18, p < 0.001). The combination of hydroxychloroquine and azithromycin was shown to be associated with increased QT prolongation incidence (OR 2.01, 95% CI 1.26 to 3.20, p = 0.003) and fatal cardiac complications in cardiac-impaired populations (OR 2.23, 95% CI 1.24 to 4.00, p = 0.007). No drug was significantly associated with increased noncardiac serious adverse events compared to standard care. The quality of evidence of collective outcomes were estimated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) framework. The major limitation of the present study is the overall low level of evidence that reduces the certainty of recommendations. Besides, the risk of bias (RoB) measured by RoB2 and ROBINS-I framework for individual studies was generally low to moderate. The outcomes deducted from observational studies could not infer causality and can only imply associations. The study protocol is publicly available on PROSPERO (CRD42020186527).

          Conclusions

          In this NMA, we found that anti-inflammatory agents (corticosteroids, tocilizumab, anakinra, and IVIG), convalescent plasma, and remdesivir were associated with improved outcomes of hospitalized COVID-19 patients. Hydroxychloroquine did not provide clinical benefits while posing cardiac safety risks when combined with azithromycin, especially in the vulnerable population. Only 29% of current evidence on pharmacological management of COVID-19 is supported by moderate or high certainty and can be translated to practice and policy; the remaining 71% are of low or very low certainty and warrant further studies to establish firm conclusions.

          Abstract

          In this meta-analysis, Min Seo Kim and colleagues synthesise results from randomized trials and observational studies on COVID-19 treatments.

          Author summary

          Why was the study done?
          • Numerous clinical trials and observational studies have investigated various pharmacological agents as potential treatments for Coronavirus Disease 2019 (COVID-19), but systematic synthesis of this large body of information is not readily available.

          • Results from these studies are heterogeneous and sometimes even contradictory to one another, making it difficult for clinicians to determine which treatments are truly effective.

          • Level of evidence behind these drugs are diverse and must be classified into categories to effectively inform policy and practice.

          What did the researchers do and find?
          • In randomized controlled trials (RCTs), remdesivir and corticosteroid were shown to reduce COVID-19 aggravation and mortality rates.

          • In the whole dataset, including data from RCTs and observational studies, anti-inflammatory agents (corticosteroid, tocilizumab, anakinra, and IVIG), convalescent plasma, and remdesivir were associated with improved clinical outcomes of COVID-19.

          • Hydroxychloroquine provides no benefit in mitigating COVID-19 disease course while posing safety risks, especially to vulnerable populations.

          What do these findings mean?
          • These findings could help prioritize further research on drugs of possible benefit.

          • Only 29% of current evidence on pharmacological management of COVID-19 is based on moderate/high evidence certainty and can be reflected in practice and policy; remaining 71% are of low or very low evidence certainty and warrant further studies to establish firm conclusions.

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          Most cited references85

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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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              COVID-19: consider cytokine storm syndromes and immunosuppression

              As of March 12, 2020, coronavirus disease 2019 (COVID-19) has been confirmed in 125 048 people worldwide, carrying a mortality of approximately 3·7%, 1 compared with a mortality rate of less than 1% from influenza. There is an urgent need for effective treatment. Current focus has been on the development of novel therapeutics, including antivirals and vaccines. Accumulating evidence suggests that a subgroup of patients with severe COVID-19 might have a cytokine storm syndrome. We recommend identification and treatment of hyperinflammation using existing, approved therapies with proven safety profiles to address the immediate need to reduce the rising mortality. Current management of COVID-19 is supportive, and respiratory failure from acute respiratory distress syndrome (ARDS) is the leading cause of mortality. 2 Secondary haemophagocytic lymphohistiocytosis (sHLH) is an under-recognised, hyperinflammatory syndrome characterised by a fulminant and fatal hypercytokinaemia with multiorgan failure. In adults, sHLH is most commonly triggered by viral infections 3 and occurs in 3·7–4·3% of sepsis cases. 4 Cardinal features of sHLH include unremitting fever, cytopenias, and hyperferritinaemia; pulmonary involvement (including ARDS) occurs in approximately 50% of patients. 5 A cytokine profile resembling sHLH is associated with COVID-19 disease severity, characterised by increased interleukin (IL)-2, IL-7, granulocyte-colony stimulating factor, interferon-γ inducible protein 10, monocyte chemoattractant protein 1, macrophage inflammatory protein 1-α, and tumour necrosis factor-α. 6 Predictors of fatality from a recent retrospective, multicentre study of 150 confirmed COVID-19 cases in Wuhan, China, included elevated ferritin (mean 1297·6 ng/ml in non-survivors vs 614·0 ng/ml in survivors; p 39·4°C 49 Organomegaly None 0 Hepatomegaly or splenomegaly 23 Hepatomegaly and splenomegaly 38 Number of cytopenias * One lineage 0 Two lineages 24 Three lineages 34 Triglycerides (mmol/L) 4·0 mmol/L 64 Fibrinogen (g/L) >2·5 g/L 0 ≤2·5 g/L 30 Ferritin ng/ml 6000 ng/ml 50 Serum aspartate aminotransferase <30 IU/L 0 ≥30 IU/L 19 Haemophagocytosis on bone marrow aspirate No 0 Yes 35 Known immunosuppression † No 0 Yes 18 The Hscore 11 generates a probability for the presence of secondary HLH. HScores greater than 169 are 93% sensitive and 86% specific for HLH. Note that bone marrow haemophagocytosis is not mandatory for a diagnosis of HLH. HScores can be calculated using an online HScore calculator. 11 HLH=haemophagocytic lymphohistiocytosis. * Defined as either haemoglobin concentration of 9·2 g/dL or less (≤5·71 mmol/L), a white blood cell count of 5000 white blood cells per mm3 or less, or platelet count of 110 000 platelets per mm3 or less, or all of these criteria combined. † HIV positive or receiving longterm immunosuppressive therapy (ie, glucocorticoids, cyclosporine, azathioprine).
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: SoftwareRole: SupervisionRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: InvestigationRole: ResourcesRole: SupervisionRole: Writing – original draft
                Role: Data curationRole: ResourcesRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Med
                PLoS Med
                plos
                plosmed
                PLoS Medicine
                Public Library of Science (San Francisco, CA USA )
                1549-1277
                1549-1676
                30 December 2020
                December 2020
                : 17
                : 12
                : e1003501
                Affiliations
                [1 ] Korea University, College of Medicine, Seoul, Republic of Korea
                [2 ] Cheongsan Public Health Center, Wando, Republic of Korea
                [3 ] Ajou University, School of Medicine, Suwon, Republic of Korea
                [4 ] So Ahn Public Health Center, Wando, Republic of Korea
                [5 ] Gangneung Prison Medical Department, Ministry of Justice, Republic of Korea
                [6 ] Department of Pharmacology, Pusan National University, School of Medicine, Yangsan, Republic of Korea
                [7 ] Gene and Cell Therapy Research Center for Vessel-associated Diseases, School of Medicine, Pusan National University, Yangsan, Republic of Korea
                Universitair Medisch Centrum Utrecht, NETHERLANDS
                Author notes

                The authors have declared that no competing interests exist.

                ‡ MSK and THH share last authorship on this work.

                Author information
                https://orcid.org/0000-0003-2115-7835
                https://orcid.org/0000-0003-2773-9756
                Article
                PMEDICINE-D-20-03136
                10.1371/journal.pmed.1003501
                7794037
                33378357
                0c25407b-3734-4288-9346-375ce5aa5dfd
                © 2020 Kim et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 1 July 2020
                : 10 December 2020
                Page count
                Figures: 5, Tables: 2, Pages: 28
                Funding
                This study was supported by the National Research Foundation of Korea (NRF) grant, funded by the Korean government (MSIP) (NRF-2015R1A5A2009656), received by TH. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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