Anemia during SARS-CoV-2 infection is associated with rehospitalization after viral clearance

  1. Patrick J. Lenehan
  2. Eshwan Ramudu
  3. A.J. Venkatakrishnan
  4. Gabriela Berner
  5. Reid McMurry
  6. John C. O'Horo
  7. Andrew D. Badley
  8. William Morice
  9. John Halamka
  10. Venky Soundararajan

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

No abstract available

Article activity feed

  1. Version published to 10.1016/j.isci.2021.102780
  2. Version published to 10.1101/2020.12.02.20242958v4 on medRxiv
  3. Version published to 10.1101/2020.12.02.20242958v3 on medRxiv
  4. Version published to 10.1101/2020.12.02.20242958v2 on medRxiv
  5. ScreenIT

    SciScore for 10.1101/2020.12.02.20242958: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Study Approval: This retrospective study was reviewed and approved by the Mayo Clinic Institutional Review Board (IRB 20-003278) as a minimal risk study.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableBinary variables were assigned as follows: anemia: 0 = no anemia or mild anemia, 1 = moderate or severe anemia; AKI: 0 = no AKI or stage 1 AKI, 1 = stage 2+ AKI; sex: 0 = female, 1 = male; ICU admission during interval: 0 = not admitted to ICU, 1 = admitted to ICU.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All of the tests described above were applied using the SciPy package (Virtanen et al., 2020) in Python (version 3.5).
    SciPy
    suggested: (SciPy, RRID:SCR_008058)
    Python
    suggested: (IPython, RRID:SCR_001658)

    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:
    Limitations of the Study: This study has several important limitations to consider. First, this analysis considers only patients within one EHR system; while this system does contain patient data from multiple sites of clinical care in distinct geographic locations (Minnesota, Arizona, Florida), there are still likely underlying biases in important factors such as patient demographics and tendencies around the ordering of laboratory tests by clinicians. Such biases would prevent the studied cohort and their associated data points from serving as true representative samples of all COVID-19 patients. Second, the analyzed cohort was relatively small (n = 382) as most patients diagnosed with COVID-19 do not subsequently receive two confirmatory negative PCR tests, and only a subset of these 382 patients possessed data for each lab test of interest. Finally, the definition of the SARS-CoV-2+ window is imperfect as the true date of viral clearance for a given patient would likely precede their first negative PCR test by an unknown amount of time. Resource Availability: After publication, the data will be made available to others upon reasonable requests to the corresponding author (venky{at}nference.net). A proposal with a detailed description of study objectives and the statistical analysis plan will be needed for evaluation of the reasonability of requests. Deidentified data will be provided after approval from the corresponding author and Mayo Clinic.

    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.

  6. Version published to 10.1101/2020.12.02.20242958v1 on medRxiv