Development and Validation of a Web-Based Severe COVID-19 Risk Prediction Model

  1. Sang H. Woo
  2. Arturo J. Rios-Diaz
  3. Alan A. Kubey
  4. Dianna R. Cheney-Peters
  5. Lily L. Ackermann
  6. Divya M. Chalikonda
  7. Chantel M. Venkataraman
  8. Joshua M. Riley
  9. Michael Baram

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Abstract

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  1. Version published to 10.1016/j.amjms.2021.04.001
  2. ScreenIT

    SciScore for 10.1101/2020.07.16.20155739: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The Thomas Jefferson University Institutional Review Board approved this study and waived informed consent from study participants.
    Consent: The Thomas Jefferson University Institutional Review Board approved this study and waived informed consent from study participants.
    RandomizationThe eligible sample (n=415) was randomly split into a derivation group (75%; n=311) and a validation group (25%; n=104).
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Scikit-Learn, an open source library was used for machine learning modeling and AUC.
    Scikit-Learn
    suggested: (scikit-learn, RRID:SCR_002577)
    Python (version 3.6.6), Statsmodels (version 0.9.0, for regression), and RStudio (version 1.1.463) were used for statistical analysis.
    Python
    suggested: (IPython, RRID:SCR_001658)
    RStudio
    suggested: (RStudio, RRID:SCR_000432)

    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:
    This study is not without limitations. First, the data was extracted retrospectively, relied on EHR provider documentation, and was limited to variables contained in the EHR. To help ensure data validity, the variables and outcomes of interest were extracted by physician-investigators and validated by an independent researcher. Second, the sample size is relatively small compared to larger studies from China. These models had excellent performance during the internal validation process, therefore, we chose to prioritize the dissemination given the urgent need of prediction models tailored specifically to the U.S. to care for patients suffering from COVID-19. Third, given the rapidly changing “standard of care” for COVID-19 and institutional efforts to educate clinicians in near real-time, there was likely significant practice variation both within each hospital and between hospitals between March 1, 2020 and April 30, 2020 that might affect outcomes. Nonetheless, we have provided a mobile-friendly model for prediction of severe COVID-19 upon presentation.

    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 10.1101/2020.07.16.20155739 on medRxiv