The CCEDRRN COVID-19 Mortality Score to predict death among nonpalliative patients with COVID-19 presenting to emergency departments: a derivation and validation study

  1. Corinne M. Hohl
  2. Rhonda J. Rosychuk
  3. Patrick M. Archambault
  4. Fiona O’Sullivan
  5. Murdoch Leeies
  6. Éric Mercier
  7. Gregory Clark
  8. Grant D. Innes
  9. Steven C. Brooks
  10. Jake Hayward
  11. Vi Ho
  12. Tomislav Jelic
  13. Michelle Welsford
  14. Marco L.A. Sivilotti
  15. Laurie J. Morrison
  16. Jeffrey J. Perry

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

No abstract available

Article activity feed

  1. Version published to 10.9778/cmajo.20210243
  2. ScreenIT

    SciScore for 10.1101/2021.07.28.21261283: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: (15) This study was approved by the research ethics boards of all participating institutions with a waiver of informed consent for enrolment.
    Consent: (15) This study was approved by the research ethics boards of all participating institutions with a waiver of informed consent for enrolment.
    Sex as a biological variablenot detected.
    RandomizationModel development and validation: We randomly assigned participating sites to derivation or validation, with the goal of assigning 75% of eligible patients and outcome events to derivation, and the remaining to validation.
    BlindingWe evaluated the inter-rater agreement of key predictor variables by comparing data collected retrospectively with prospective data.(15) The clinical prediction score was developed after all chart abstractions were complete; research assistants were thus unaware of which clinical variables would be candidate predictor variables.
    Power Analysisnot detected.
    Cell Line AuthenticationAuthentication: Model development and validation: We randomly assigned participating sites to derivation or validation, with the goal of assigning 75% of eligible patients and outcome events to derivation, and the remaining to validation.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data collection: Trained research assistants abstracted data from electronic and paper-based medical records into a central, web-based REDCap database (Vanderbilt University; Nashville, TN, USA), and captured demographics, vital signs, symptoms, and comorbidities, COVID-19 exposure risk, diagnostic test results, and patient outcomes.
    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04702945RecruitingCanadian COVID-19 Emergency Department Registry

    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.

    Results from scite Reference Check: We found no unreliable references.

    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 10.1101/2021.07.28.21261283v1 on medRxiv