Evaluating a Widely Implemented Proprietary Deterioration Index Model among Hospitalized Patients with COVID-19

  1. Karandeep Singh
  2. Thomas S. Valley
  3. Shengpu Tang
  4. Benjamin Y. Li
  5. Fahad Kamran
  6. Michael W. Sjoding
  7. Jenna Wiens
  8. Erkin Otles
  9. John P. Donnelly
  10. Melissa Y. Wei
  11. Jonathon P. McBride
  12. Jie Cao
  13. Carleen Penoza
  14. John Z. Ayanian
  15. Brahmajee K. Nallamothu

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Abstract

No abstract available

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  1. Version published to 10.1513/annalsats.202006-698oc
  2. Version published to 10.1101/2020.04.24.20079012v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.04.24.20079012: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The study was approved by the Institutional Review Board of the University of Michigan Medical School.
    RandomizationThe AUC represents the probability of correctly ranking two randomly chosen individuals (one who experienced the event and one who did not).
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our study should be interpreted in the context of the following limitations. Our evaluation was limited by its focus on a single academic medical center and a relatively small number of patients. However, our cohort of nearly 400 patients was diverse in sex and race and larger than many earlier reports. As compared to a recently described large cohort of 5,700 patients hospitalized with COVID-19 in New York, our study cohort had a higher proportion of African Americans (43% vs. 23%) and patients with chronic kidney disease (38% vs. 5%), congestive heart failure (21% vs. 7%), hypertension (75% vs. 57%), and similar proportions of women (43% vs. 40%), diabetes (42% vs. 34%), and obesity (42% vs. 42%).2 Our proposed EDI thresholds may be influenced by local factors, including patterns of COVID-19 testing, triage, and decision-making about hospital admissions and hospital-to-hospital transfers that contributed to our study cohort. These EDI thresholds should be validated in other settings to assess their generalizability.

    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.

  4. Version published to 10.1101/2020.04.24.20079012v1 on medRxiv