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Category: Project

Description: ABSTRACT BACKGROUND: There is no consensus on the efficacy of tocilizumab for the treatment of the new coronavirus. OBJECTIVE: To evaluate the effectiveness and safety of tocilizumab for treating COVID-19. METHODS: This is a systematic review of randomized controlled trials on tocilizumab for COVID-19 treatment using searches on MEDLINE via PubMed, EMBASE, CENTRAL, IBECS following Cochrane Recommendations.   INTRODUCTION Systematic reviews evaluating the effects of tocilizumab for the treatment of COVID-19 considering only RCTs are urgently needed. OBJECTIVES The aim of this review is to evaluate the effectiveness and safety of tocilizumab for treating COVID-19 METHODS Inclusion criteria • Randomized controlled trials • Participants with COVID-19 diagnosed by the following methods: rRT-PCR (polymerase chain reaction); serum immunoglobulin M (IgM) antibody assay; or clinical evaluation (typical computed tomographic [CT] scan with signs of pneumonia). • Tocilizumab used alone or in combination with standard care or other interventions. Primary Outcome • Mortality. Secondary Outcomes • Need of mechanical ventilation; • Days until discharge from hospital; • Adverse events. Search methods • Published and unpublished trials, without restrictions on language, year or publication status. • Electronic databases: MEDLINE via PubMed, EMBASE, CENTRAL (Cochrane Library) and BVS portal. Study selection, data extraction and assessment of risk of bias and certainty of evidence • Two authors for performing study selection, data extraction, assessment of risk of bias using risk of bias (RoB 2.0) toll as per the Cochrane recommendations and certainty of evidence using GRADE (Grading of Recommendations Assessment, Development and Evaluations) approach, with a third author consultation in the presence of any disagreements. Measures of treatment effect • Relative risks (RR) with their 95% confidence intervals (CI) for pooling dichotomous outcomes from two or more homogeneous studies, using the generic inverse variance method with the random-effects model.