Risk factors for mortality in patients with Coronavirus disease 2019 (COVID-19) infection: a systematic review and meta-analysis of observational studies

  1. Mohammad Parohan
  2. Sajad Yaghoubi
  3. Asal Seraji
  4. Mohammad Hassan Javanbakht
  5. Payam Sarraf
  6. Mahmoud Djalali

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Abstract

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  1. Version published to 10.1080/13685538.2020.1774748
  2. ScreenIT

    SciScore for 10.1101/2020.04.09.20056291: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableTo identify potential sources of heterogeneity, we did subgroup analysis according to the predefined criteria as follows: age (≥65 vs. <65), gender (male vs. female), hypertension (yes vs. no), diabetes (yes vs. no), COPD (yes vs. no) and CVDs (yes vs. no).

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Search strategy: We performed a literature search using the online databases of Web of Science, PubMed, Scopus, Cochrane Library and Google scholar for relevant publications up to 1 May 2020.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Cochrane Library
    suggested: (Cochrane Library, RRID:SCR_013000)
    Google scholar
    suggested: (Google Scholar, RRID:SCR_008878)
    The following medical subject headings (MeSH) and non-MeSH keywords were used in our search strategy: (“novel coronavirus” OR “severe acute respiratory syndrome coronavirus 2” OR “SARS-CoV-2” OR “COVID-19” OR “2019-nCoV”)
    MeSH
    suggested: (MeSH, RRID:SCR_004750)
    To facilitate the screening process of articles from databases, all literature searches were downloaded into an EndNote library (version X8, Thomson Reuters, Philadelphia, USA).
    EndNote
    suggested: (EndNote, RRID:SCR_014001)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    The present study has some limitations. First, interpretation of our meta-analysis findings might be limited by the small sample size. However, by including studies conducted in different designated hospitals for COVID-19, we believe our findings are representative of cases in Wuhan, China. Second, our meta-analysis did not include data such as smoking history and body mass index, which are potential risk factors for disease severity and mortality.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.04.09.20056291v2 on medRxiv
  4. Version published to 10.1101/2020.04.09.20056291v1 on medRxiv