Factors associated with clinical severity in emergency department patients presenting with symptomatic SARS‐CoV‐2 infection

  1. Sophia Newton
  2. Benjamin Zollinger
  3. Jincong Freeman
  4. Seamus Moran
  5. Alexandra Helfand
  6. Kayla Authelet
  7. Matthew McHarg
  8. Nataly Montano Vargas
  9. Robert Shesser
  10. Joanna S. Cohen
  11. Derek A.T. Cummings
  12. Yan Ma
  13. Andrew C. Meltzer

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Abstract

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  1. Version published to 10.1002/emp2.12453
  2. ScreenIT

    SciScore for 10.1101/2020.12.08.20246017: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The Institutional Review Board at The George Washington University in Washington, DC approved the study with a waiver of consent on March 30, 2020.
    Consent: The Institutional Review Board at The George Washington University in Washington, DC approved the study with a waiver of consent on March 30, 2020.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All data was entered into a REDCap database.
    REDCap
    suggested: (REDCap, RRID:SCR_003445)

    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:
    Strengths /Limitations: One strength of our study is the diverse characteristics of the patient population in terms of age, race, ethnicity, and health insurance status. In addition, we only included patients with symptoms of a viral-like illness and a laboratory-confirmed positive PCR test. Moreover, the dataset was relatively complete with few missing elements. Finally, we have high confidence in the data abstraction methods and utilized easily abstracted variables in order to remove ambiguity or subjectivity present in typical chart abstraction. This study has several important limitations. First, this study was reliant on documentation in the EHR of a single ED. In addition, we did not collect detailed socioeconomic data such as household income, educational attainment, and presence of primary care. Finally, we were unable to capture the severity and treatment status of comorbid conditions from chart review and, therefore, our results may not reflect the impact of underlying health conditions on the severity of COVID-19 for individual patients.

    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 10.1101/2020.12.08.20246017 on medRxiv