Coronavirus disease (COVID 2019): protocol for a living overview of systematic reviews

Cuncun Lu; Tingting Lu; Bei Pan; Qi Wang; Liangying Hou; Qian Zhang; Yunhua Wang; Yihui Wang; Xiuxia Li; Yue Ruan; Lian Chen; Honghao Lai; Tianzhu Qin; Long Ge; Kehu Yan

doi:10.21037/apm-20-1130

Coronavirus disease (COVID 2019): protocol for a living overview of systematic reviews

Ann Palliat Med. 2021 Feb;10(2):1488-1493. doi: 10.21037/apm-20-1130. Epub 2020 Nov 24.

Authors

Cuncun Lu¹, Tingting Lu¹, Bei Pan², Qi Wang³, Liangying Hou⁴, Qian Zhang⁵, Yunhua Wang⁶, Yihui Wang⁶, Xiuxia Li⁷, Yue Ruan⁵, Lian Chen⁵, Honghao Lai⁶, Tianzhu Qin⁶, Long Ge⁸, Kehu Yan⁹

Affiliations

¹ 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.
² Gansu Provincial Hospital, Lanzhou, China.
³ Department of Social Science and Health Management, School of Public Health, Lanzhou University, Lanzhou, China; Evidence-Based Social Science Research Center, School of Public Health, Lanzhou University, Lanzhou, China.
⁴ 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.
⁵ Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou, China.
⁶ Department of Social Science and Health Management, School of Public Health, Lanzhou University, Lanzhou, China.
⁷ 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; Evidence-Based Social Science Research Center, School of Public Health, Lanzhou University, Lanzhou, China.
⁸ 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; Evidence-Based Social Science Research Center, School of Public Health, Lanzhou University, Lanzhou, China. gelong2009@163.com.
⁹ Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China; Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province, Lanzhou, China; Evidence-Based Social Science Research Center, School of Public Health, Lanzhou University, Lanzhou, China. kehuyangebm2006@126.com.

PMID: 33353349
DOI: 10.21037/apm-20-1130

Abstract

Background: 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.

Methods: 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.

Results: The results of this overview will be submitted to a peer-reviewed journal for publication.

Discussion: 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.

Keywords: AMSTAR-2; COVID-19; PRISMA; evidence quality; living overview; meta-analysis; systematic review (SR).

MeSH terms

COVID-19* / diagnosis
COVID-19* / epidemiology
COVID-19* / prevention & control
COVID-19* / therapy
Databases, Bibliographic
Humans
Meta-Analysis as Topic
Research Design*
Systematic Reviews as Topic