Performance of prediction models for short-term outcome in COVID-19 patients in the emergency department: a retrospective study

  1. Paul M. E. L. van Dam
  2. Noortje Zelis
  3. Sander M. J. van Kuijk
  4. Aimée E. M. J. H. Linkens
  5. Renée A. G. Brüggemann
  6. Bart Spaetgens
  7. Iwan C. C. van der Horst
  8. Patricia M. Stassen

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Abstract

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  1. Version published to 10.1080/07853890.2021.1891453
  2. ScreenIT

    SciScore for 10.1101/2020.11.25.20238527: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The medical ethics committee of the MUMC+ approved this study (METC 2020-1572).
    Consent: Informed consent was obtained from all individual participants.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Prediction models: We searched PubMed for studies on prediction models focusing on patients with COVID-19 using a combination of methodological search terms (prognostic, prediction model, score, regression) and COVID-19 search terms (COVID-19, SARS-CoV-2, coronavirus).
    PubMed
    suggested: (PubMed, RRID:SCR_004846)

    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 had several limitations. First, our study was performed in a single medical center, which may limit the generalizability of the results. However, our cohort of patients with COVID-19 was relatively large and has been recruited in one of the most heavily affected areas of the Netherlands. Furthermore, by validating all prediction models in the same cohort, there were no differences in the patient sample, and we could truly compare the scores.37 Second, the process of selecting prediction models for our analysis might have been incomplete. We chose prediction models that were feasible in our ED setting, which may be different for other EDs. Third, in a subgroup of patients with preexisting frailty or severe comorbidity, it was decided to initiate conservative care only (35.2% had treatment restrictions). As these decisions may be different in other countries, we decided to study MCU/ICU admissions as a composite outcome only. In addition, we decided to calculate an AUC for 14-day and 30-day mortality in the 261 patients without treatment restrictions (Supplementary Table). We found comparable AUCs for both outcomes (AUC of 0.82 and 0.84 for the RISE UP, respectively). We therefore found no evidence for differences in performance of the models between patients with and without treatment restrictions. Last, the number of ICU and MCU admissions in our study was relatively low (16.4% and 11.9%, respectively). In our cohort, 23.8% of the patients were discharged home and t...

    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 10.1101/2020.11.25.20238527v1 on medRxiv