Laboratory Findings Associated With Severe Illness and Mortality Among Hospitalized Individuals With Coronavirus Disease 2019 in Eastern Massachusetts

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Abstract

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  1. SciScore for 10.1101/2020.05.04.20090555: (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 Partners HealthCare Human Research Committee.
    Consent: No participant contact was required in this study which relied on secondary use of data produced by routine clinical care, allowing waiver of requirement for informed consent as detailed by 45 CFR 46.116.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.


    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:
    We note multiple limitations that likely diminish model performance. First, as these are open hospital systems rather than closed networks, lack of documented prior diagnoses does not preclude their presence for individuals who may receive care elsewhere. For this reason we excluded hospital transfers, as prior documentation of comorbidity is likely to be biased. However, such missing data are likely to diminish predictive power of any given diagnosis, such that our model performance estimates are likely to be conservative. In addition, many laboratory values are highly non-normal, such that incorporation of more specific transformations or cut-points could likely improve model performance; we elected to incorporate standard high/low flags plus continuous measures, rather than adopting specific transformations for each value which would risk overfitting or diminish generalizability but likely extract additional information. Efforts to aggregate laboratory data across international health systems will provide an opportunity to explore such transformations if individual-level data becomes accessible9. Despite these limitations, our analyses suggest the utility of laboratory values in combination with documented comorbidities and sociodemographic features in identifying individuals at particularly high risk for more severe hospital course. Notably, by validating in distinct hospitals (albeit within a single geographic region), our estimates of model performance are likely to be ...

    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.