Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel.
PLoS One. 2022 Apr 5;17(4):e0266232. doi: 10.1371/journal.pone.0266232. eCollection 2022.
As of July 2021, there has been more than 185 million documented cases of the novel coronavirus (SARS-CoV-2) infections and more than 4 million deaths globally. Despite more than 90% of documented cases being classified as "recovered" from SARS-CoV-2 infection, a proportion of patients reported a wide variety of persisting symptoms after the initial onset or acute phase of the infection, often referred to as "Long Covid". As data on the symptomatology of post-acute SARS-CoV-2 infection gradually becomes available, there is an urgent need to organise and synthesise the data in order to define what constitutes Long Covid and assist with its management in clinical and community settings.
This protocol follows the Preferred Reporting Items for Systematic Review and Meta-analysis Protocols (PRISMA-P) guidelines. A comprehensive literature search strategy will be developed in accordance with the Cochrane highly sensitive search guidelines. The following electronic databases will be searched for studies to include in the systematic review and meta-analysis: MEDLINE (via PubMed), Scopus, Google Scholar, Web of Science (Web of Knowledge), Science direct, EMBASE, Mednar, Psych INFO, and EBSCOhost. Dual screening will be applied at every screening stage. Two reviewers will independently screen titles, abstracts and full text of potentially eligible studies following the predefined inclusion and exclusion criteria in order to select studies to include in the review. As heterogeneity is anticipated between the included studies, data will be pooled in a meta-analysis using a random effects model. A clustering analytic approach will be applied to identify symptoms groupings and assign the symptoms into clusters. R statistical software will be used for the meta-analysis. Highly heterogenous data will be synthesised narratively. The studies will be assessed, for quality using quality assessment tools appropriate for each study design. Two reviewers will independently undertake the quality of studies assessments.
Findings of the systematic review will be disseminated through a peer-reviewed publication and presentation of findings at conferences, workshops and government and private sector stakeholder engagement meetings.
PROSPERO registration number: CRD4202126589. https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD4202126589.
截至 2021 年 7 月，全球已记录超过 1.85 亿例新型冠状病毒（SARS-CoV-2）感染病例和超过 400 万例死亡。尽管超过 90%的已记录病例被归类为从 SARS-CoV-2 感染中“康复”，但一部分患者在感染的初始发作或急性期后报告了各种各样的持续症状，通常称为“长新冠”。随着关于急性 SARS-CoV-2 感染后症状学的数据逐渐可用，迫切需要对数据进行组织和综合，以确定什么是长新冠，并协助在临床和社区环境中对其进行管理。
本方案遵循系统评价和荟萃分析计划的首选报告项目（PRISMA-P）指南。将根据 Cochrane 高度敏感搜索指南制定全面的文献搜索策略。将在以下电子数据库中搜索研究，以纳入系统评价和荟萃分析：MEDLINE（通过 PubMed）、Scopus、Google Scholar、Web of Science（Web of Knowledge）、Science Direct、EMBASE、Mednar、Psych INFO 和 EBSCOhost。在每个筛选阶段都将进行双重筛选。两位审查员将根据预先确定的纳入和排除标准，独立筛选可能符合条件的研究的标题、摘要和全文，以选择纳入审查的研究。由于预计纳入研究之间存在异质性，将使用随机效应模型对数据进行汇总荟萃分析。将应用聚类分析方法来识别症状分组，并将症状分配到聚类中。将使用 R 统计软件进行荟萃分析。高度异质的数据将进行叙述性综合。将使用适合每种研究设计的质量评估工具评估研究的质量。两位审查员将独立进行研究质量评估。
系统评价的结果将通过同行评议出版物发表，并在会议、研讨会以及政府和私营部门利益相关者参与会议上展示研究结果。
PROSPERO 注册号：CRD4202126589。https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD4202126589。
