Clinical Trends Among U.S. Adults Hospitalized With COVID-19, March to December 2020

  1. Shikha Garg
  2. Kadam Patel
  3. Huong Pham
  4. Michael Whitaker
  5. Alissa O’Halloran
  6. Jennifer Milucky
  7. Onika Anglin
  8. Pam D. Kirley
  9. Arthur Reingold
  10. Breanna Kawasaki
  11. Rachel Herlihy
  12. Kimberly Yousey-Hindes
  13. Amber Maslar
  14. Evan J. Anderson
  15. Kyle P. Openo
  16. Andrew Weigel
  17. Kenzie Teno
  18. Patricia A. Ryan
  19. Maya L. Monroe
  20. Libby Reeg
  21. Sue Kim
  22. Kathryn Como-Sabetti
  23. Erica Bye
  24. Sarah Shrum Davis
  25. Nancy Eisenberg
  26. Alison Muse
  27. Grant Barney
  28. Nancy M. Bennett
  29. Christina B. Felsen
  30. Laurie Billing
  31. Jess Shiltz
  32. Melissa Sutton
  33. Nasreen Abdullah
  34. H. Keipp Talbot
  35. William Schaffner
  36. Mary Hill
  37. Ryan Chatelain
  38. Jonathan Wortham
  39. Christopher Taylor
  40. Aron Hall
  41. Alicia M. Fry
  42. Lindsay Kim
  43. Fiona P. Havers

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Abstract

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  1. Version published to 10.7326/m21-1991
  2. ScreenIT

    SciScore for 10.1101/2021.04.21.21255473: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: This activity was reviewed by CDC and was conducted consistent with applicable federal law and CDC policy (see e.g., 45 C.F.R. part 46.102(l)(2), 21 C.F.R. part 56; 42 U.S.C. §241(d); 5 U.S.C. §552a; 44 U.S.C. §3501 et seq.) Sites participating in COVID-NET obtained approval from their respective state and local Institutional Review Boards, as applicable.
    Sex as a biological variablenot detected.
    RandomizationDetailed clinical data are collected for a random sample of cases aged ≥18 years stratified by age and surveillance site.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Experimental Models: Organisms/Strains
    SentencesResources
    Data on race and ethnicity were categorized as follows: non-Hispanic White (White), non-Hispanic Black (Black), Hispanic or Latino (Hispanic), non-Hispanic Asian or Pacific Islander (Asian/PI), non-Hispanic American Indian or Alaska Native (AI/AN) and people of more than one race/ethnicity.
    non-Hispanic White
    suggested: None
    Software and Algorithms
    SentencesResources
    All analyses were conducted using SAS 9.4 software (SAS Institute Inc.
    SAS
    suggested: (SASqPCR, RRID:SCR_003056)
    SAS Institute
    suggested: (Statistical Analysis System, RRID:SCR_008567)

    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 analysis has several limitations. Because COVID-NET covers approximately 10% of the U.S. population, our findings may not be generalizable to the entire country. This analysis presented data for the entire network and did not account for differences across sites; several factors, including peaks in hospitalization rates and the racial/ethnic distribution of cases varied by site. Since SARS-CoV-2 testing was conducted at the discretion of healthcare providers, COVID-NET may not have captured all COVID-19-associated hospitalizations. Changes in testing practices over the course of the pandemic may have influenced trends as sicker patients were more likely to be tested early in the pandemic when testing capacity was limited. The relative standard errors for some estimates were >0.3, particularly for interventions or outcomes with low prevalence, such as RRT or in-hospital deaths among patients aged 18-49 years. Lastly, sample sizes were not sufficient to fully explore differences in interventions and outcomes by race/ethnicity or to produce estimates for all racial/ethnic groups including AI/AN and Asian/PI patients. In future analyses, COVID-NET will combine additional months of data to characterize trends in severe outcomes among these racial/ethnic groups. This analysis describes changes in characteristics, interventions and outcomes among U.S. adults hospitalized with COVID-19 over the first 10 months of the pandemic. Large declines in the percentage of patients with CO...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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 10.1101/2021.04.21.21255473v1 on medRxiv