Performance of Three Measures of Comorbidity in Predicting Critical COVID-19: A Retrospective Analysis of 4607 Hospitalized Patients

  1. David Monterde
  2. Gerard Carot-Sans
  3. Miguel Cainzos-Achirica
  4. Sònia Abilleira
  5. Marc Coca
  6. Emili Vela
  7. Montse Clèries
  8. Damià Valero-Bover
  9. Josep Comin-Colet
  10. Luis García-Eroles
  11. Pol Pérez-Sust
  12. Miquel Arrufat
  13. Yolanda Lejardi Estevez
  14. Jordi Piera-Jiménez

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Abstract

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  1. Version published to 10.2147/rmhp.s326132
  2. ScreenIT

    SciScore for 10.1101/2021.07.02.21259898: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: The independent ethics committee of the Bellvitge Biomedical Research Institute
    Consent: (IDIBELL) approved the study protocol and waived the need for informed consent, as the data were generated as part of routine clinical care and fully de-identified for analytic purposes.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The independent ethics committee of the Bellvitge Biomedical Research Institute
    Bellvitge Biomedical Research Institute
    suggested: (Bellvitge Biomedical Research Institute, RRID:SCR_003917)

    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:
    Study Limitations: One of the limitations of Queralt DxS is the lack validation in settings other than Catalonia, however, we hope that making the software publicly available for research purposes will facilitate external validation moving forward. Moreover, it is worth mentioning that the weights used to estimate the relative contribution of each diagnostic group to health risk were calculated using data collected during years 2018 and 2019, before the COVID-19 pandemic. Hence, although the source population of the 8 relevant hospitals has remained unchanged since then, the sample of patients admitted with COVID-19 included in this study and their clinical and sociodemographic profile do differ from those used when developing the Queralt Indices, including Queralt DxS. Therefore, the current analysis may be interpreted as a pseudo-validation of Queralt DxS in a Catalan subpopulation hospitalized with COVID-19. Also, as often occurs in retrospective analyses, our dataset was limited to the data recorded in electronic records during routine care. Nonetheless, the universal coverage of our healthcare system, and the cross-linking of healthcare data from the primary care and specialized settings allowed us to access various sources and very comprehensive health data from our study population and consider all possible diagnostics present on admission, as well as basic demographic data such as age and sex. Conversely, we were not able to include some pre-admission variables such a...

    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.

    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 10.1101/2021.07.02.21259898v1 on medRxiv