标题：抗 SARS-CoV-2 单克隆抗体和/或抗病毒药物治疗的住院 COVID-19 患者死亡和有创机械通气风险的系统评价和网络荟萃分析方案 解析：原文的标题较长，在翻译时，采用“正题+副题”的形式，正题突出研究对象“抗 SARS-CoV-2 单克隆抗体和/或抗病毒药物治疗的住院 COVID-19 患者”，副题则是对研究内容的补充说明。
Death and invasive mechanical ventilation risk in hospitalized COVID-19 patients treated with anti-SARS-CoV-2 monoclonal antibodies and/or antiviral agents: A systematic review and network meta-analysis protocol.
机构信息
Department of Community Medicine, R. G. Kar Medical College, Kolkata, West Bengal, India.
出版信息
PLoS One. 2022 Jun 17;17(6):e0270196. doi: 10.1371/journal.pone.0270196. eCollection 2022.
BACKGROUND
The ongoing COVID-19 pandemic has claimed >4 million lives globally, and these deaths often occurred in hospitalized patients with comorbidities. Therefore, the proposed review aims to distinguish the inpatient mortality and invasive mechanical ventilation risk in COVID-19 patients treated with the anti-SARS-CoV-2 monoclonal antibodies and/or the antiviral agents.
METHODS
A search in PubMed, Embase, and Scopus will ensue for the publications on randomized controlled trials testing the above, irrespective of the publication date or geographic boundary. Risk of bias assessment of the studies included in the review will occur using the Cochrane risk of bias tool for randomized trials (RoB 2). Frequentist method network meta-analyses (NMA) will compare each outcome's risk across both types of anti-SARS-CoV-2 agents in one model and each in separate models. Additional NMA models will compare these in COVID-19 patients who were severely or critically ill, immunocompromised, admitted to the intensive care unit, diagnosed by nucleic acid amplification test, not treated with steroids, <18 years old, and at risk of infection due to variants of concern. The plan of excluding non-hospitalized patients from the proposed review is to minimize intransitivity risk. The acceptance of the network consistency assumption will transpire if the local and overall inconsistency assessment indicates no inconsistency. For each NMA model, the effect sizes (risk ratio) and their 95% confidence intervals will get reported in league tables. The best intervention prediction and quality of evidence grading will happen using the surface under the cumulative ranking curve values and the Grading of Recommendations Assessment, Development and Evaluation-based Confidence in Network Meta-Analysis approach, respectively. Sensitivity analysis will repeat the preliminary NMA while excluding the trials at high risk of bias. The Stata statistical software (v16) will be used for analysis. The statistical significance will get determined at p<0.05 and 95% confidence interval.
TRIAL REGISTRATION
PROSPERO Registration No: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021277663.
背景
持续的 COVID-19 大流行已在全球范围内导致超过 400 万人死亡，这些死亡通常发生在合并症的住院患者中。因此，本研究旨在区分 COVID-19 患者接受抗 SARS-CoV-2 单克隆抗体和/或抗病毒药物治疗后的住院死亡率和有创机械通气风险。
方法
在 PubMed、Embase 和 Scopus 中搜索测试上述药物的随机对照试验出版物，无论出版日期或地理边界如何。使用 Cochrane 随机对照试验偏倚风险工具（RoB 2）对纳入研究进行偏倚风险评估。频率主义方法网络荟萃分析（NMA）将在一个模型中比较两种类型的抗 SARS-CoV-2 药物在每种结局风险上的差异，并在单独的模型中分别比较。其他 NMA 模型将在严重或危重症、免疫功能低下、入住重症监护病房、核酸扩增试验诊断、未接受类固醇治疗、<18 岁以及因关注变异而有感染风险的 COVID-19 患者中比较这些药物。从拟议的研究中排除非住院患者的计划是为了最大程度地减少不传递性风险。如果局部和总体不一致性评估表明没有不一致性，则接受网络一致性假设。对于每个 NMA 模型，将以列线图的形式报告效应大小（风险比）及其 95%置信区间。使用累积排序曲线值下的最佳干预预测和证据质量分级，并使用基于推荐评估、制定和评估的网络荟萃分析方法的信心等级，分别进行最佳干预预测和证据质量分级。敏感性分析将在排除高偏倚风险试验的情况下重复初步 NMA。使用 Stata 统计软件（v16）进行分析。统计显著性将在 p<0.05 和 95%置信区间确定。
试验注册
PROSPERO 注册编号：https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021277663。
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