Risk stratification of hospitalized COVID-19 patients through comparative studies of laboratory results with influenza

  1. Yang Mei
  2. Samuel E. Weinberg
  3. Lihui Zhao
  4. Adam Frink
  5. Chao Qi
  6. Amir Behdad
  7. Peng Ji

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Abstract

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  1. Version published to 10.1016/j.eclinm.2020.100475
  2. ScreenIT

    SciScore for 10.1101/2020.05.18.20101709: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The study protocol was approved by the institutional review board at Northwestern University.
    Consent: The requirement for informed consent was waived approved by the review board.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical analyses were performed using the Prism software (version 8·0) and R (version 3·5·1).
    Prism
    suggested: (PRISM, RRID:SCR_005375)

    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:
    Our study has its limitations. First, the patients were limited to a single healthcare system in Chicago metropolitan area. Second, due to the lack of sufficient number of influenza patients with over 7 days of laboratory data sets in our hierarchical analysis, we could not perform the same risk stratification in these patients. It remains to be determined whether similar laboratory patterns as in COVID-19 cluster 1 are also present in severe hospitalized influenza patients. Third, because of the same reason, several laboratory parameters did not show statistical significance between COVID-19 and influenza, although they appeared to be apparently different. Overall, our findings provide values to predict risk groups for further management in hospitalized COVID-19 patients in the western population. Further prospective studies using independent groups will be informative to confirm these findings.

    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 10.1101/2020.05.18.20101709v1 on medRxiv