Predictive symptoms and comorbidities for severe COVID-19 and intensive care unit admission: a systematic review and meta-analysis

  1. Vageesh Jain
  2. Jin-Min Yuan

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Abstract

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  1. ScreenIT

    SciScore for 10.1101/2020.03.15.20035360: (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 variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Identification of relevant existing literature was performed by an online search in three databases: MEDLINE, EMBASE and Global Health, for studies published from 1stJanuary 2019 to 5thMarch 2020.
    MEDLINE
    suggested: (MEDLINE, RRID:SCR_002185)
    EMBASE
    suggested: (EMBASE, RRID:SCR_001650)
    From each study, various details including the study population, investigated predictive symptoms or comorbidities, and the definitions used to measure outcomes, were extracted into Microsoft excel.
    Microsoft excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    The predictive value of symptoms and comorbidities for each of severe disease and ICU admission was estimated with random effects meta-analysis in STATA.
    STATA
    suggested: (Stata, RRID:SCR_012763)

    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:
    Limitations: The foremost limitation of this study was an inability to carry out a multivariable analysis to account for the presence of several symptoms, comorbidities and potential confounders. Although this outbreak has seen the evolution of linked data and large datasets (19) which would be suitable for multivariable analysis, these currently lack the quality of published data: there are large amounts of missing data, a narrow range of collected variables, and uncertainty about data collection methods and consistency. Our univariable analysis is therefore valuable in evaluating specific individual symptoms and comorbidities predictive for COVID-19 severity using high-quality evidence in the form of peer-reviewed studies. Secondly, the studies included here were all from China, so the generalisability of findings to other countries and populations is not clear. The Chinese may differ to other populations in terms of their health-seeking behaviour, symptom reporting, prevalence of different comorbidities, as well as their access to high quality health services. Nonetheless, given the current dearth of contextually specific evidence available, our findings will help to inform future research and actions in other countries as the outbreak develops. Finally, it was not possible to account for the timing of presentation in the statistical analysis. If a patient presented after many days of being symptomatic, this may have affected disease severity, compared with an earlier clin...

    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.

  2. Version published to 10.1007/s00038-020-01390-7
  3. Version published to 10.1101/2020.03.15.20035360v1 on medRxiv