Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China; Department of Social Science and Health Management, School of Public Health, Lanzhou University, Lanzhou, China; Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province, Lanzhou, China.
Gansu Provincial Hospital, Lanzhou, China.
Ann Palliat Med. 2021 Feb;10(2):1488-1493. doi: 10.21037/apm-20-1130. Epub 2020 Nov 24.
The coronavirus disease 2019 (COVID-19) pandemic continues to grow worldwide, and systematic reviews (SRs)/meta-analyses (MAs) on COVID-19 can efficiently guide evidence-based clinical practice. However, SRs/MAs with weaknesses can mislead clinical practice and pose harm to patients, and too many useless SRs/MAs could pose confusion and waste sources. A "living" overview of SRs/MAs aims to provide an open, accessible and frequently updated resource summarizing the highest-level evidence of COVID-19, that can help evidence-users to quickly identify trusted evidence to guide the practice. This study aims to systematically give an overview SRs/MAs of COVID-19, assess their quality, and identify the best synthesis of evidence.
Databases including Medline, EMBASE, Web of Science, China National Knowledge Infrastructure (CNKI), China Biology Medicine (CBM) and WanFang were systematically searched on May 1, 2020 using relevant terms for identify SRs/MAs related to COVID-19. The study selection, data extraction and quality assessment will be performed by independent reviewers, and results will be crosschecked. The authoritative tools (AMSTAR-2, PRISMA and its extensions) will be used to assess the methodological quality and reporting quality of included SRs/MAs, and potential influence factors will be explored. The consistency of conclusions will be compared among reviews and the best evidence will be summarized. In addition, we will conduct exploratory meta-analyses (MAs) of individual studies when applicable. Data will be reported as number with (or) percentage, risk ratio (RR) or odds ratio (OR), mean difference (MD) or standardized mean difference (SMD) with 95% confidence interval (CI) according to the specific results. R3.6.1 and Microsoft Excel 2016 will be used to analyze and manage data.
The results of this overview will be submitted to a peer-reviewed journal for publication.
In this study, we will present for the first time, an overview of SRs/MAs, which provides a comprehensive, dynamic evidence landscape on prevalence, prevention, diagnosis, treatment, and prognosis of COVID-19.
2019 年冠状病毒病（COVID-19）大流行在全球范围内持续蔓延，COVID-19 的系统评价（SR）/荟萃分析（MA）可以有效地指导循证临床实践。然而，存在缺陷的 SR/MA 可能会误导临床实践并对患者造成伤害，而过多无用的 SR/MA 可能会造成混淆和浪费资源。SR/MA 的“实时”概述旨在提供一个开放、可及且经常更新的资源，总结 COVID-19 的最高级别证据，帮助证据使用者快速识别可信证据以指导实践。本研究旨在系统地概述 COVID-19 的 SR/MA，评估其质量，并确定最佳证据综合。
我们于 2020 年 5 月 1 日系统地检索了 Medline、EMBASE、Web of Science、中国知网（CNKI）、中国生物医学文献数据库（CBM）和万方数据库，使用相关术语来确定与 COVID-19 相关的 SR/MA。研究选择、数据提取和质量评估将由独立评审员进行，结果将进行交叉核对。将使用权威工具（AMSTAR-2、PRISMA 及其扩展）评估纳入的 SR/MA 的方法学质量和报告质量，并探讨潜在的影响因素。将比较综述之间结论的一致性，并总结最佳证据。此外，在适用的情况下，我们将对个别研究进行探索性荟萃分析（MA）。根据具体结果，数据将以数字（或）百分比、风险比（RR）或比值比（OR）、均值差（MD）或标准化均数差（SMD）和 95%置信区间（CI）表示。将使用 R3.6.1 和 Microsoft Excel 2016 来分析和管理数据。
该概述的结果将提交给同行评议期刊发表。
在本研究中，我们将首次呈现 COVID-19 的 SR/MA 概述，全面、动态地展示 COVID-19 的患病率、预防、诊断、治疗和预后的证据格局。
