Characterization of Patients Who Return to Hospital Following Discharge from Hospitalization for COVID-19

  1. Sulaiman S. Somani
  2. Felix Richter
  3. Valentin Fuster
  4. Jessica K. De Freitas
  5. Nidhi Naik
  6. Keith Sigel
  7. The Mount Sinai COVID Informatics Center
  8. Erwin P Bottinger
  9. Matthew A. Levin
  10. Zahi Fayad
  11. Allan C. Just
  12. Alexander W. Charney
  13. Shan Zhao
  14. Benjamin S. Glicksberg
  15. Anuradha Lala
  16. Girish N. Nadkarni

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Abstract

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  1. Version published to 10.1007/s11606-020-06120-6
  2. ScreenIT

    SciScore for 10.1101/2020.05.17.20104604: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The Mount Sinai Institutional Review Board approved this research under a broad research protocol for patient-level data analysis.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Analyses were performed using R (R Foundation) and Python (Python Software Foundation).
    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:
    Several limitations of this study warrant mention. First, small sample sizes restricted statistical power and prevented multivariable analysis to adequately control for non-normal distributions and feature collinearities.11,12 Larger sample sizes may allow the development of such multivariable models to address potentially confounding factors and are actively being pursued.13 Second, although the MSHS reflects a large and diverse cohort, clinical management varies across hospitals and continues to evolve. Additionally, many of these discharges were in the earlier period of the COVID-19 pandemic when hospital capacity was strained. Thus, generalizability may be limited due to a possible temporal bias, necessitating extension of current studies to longer time periods. Third, readmission over a 30-day horizon may permit comparative analyses with readmission rates for other diseases to inform impact on systems-level operations. Finally, the number of readmissions may have been underreported due to presentation to hospitals outside of the Mount Sinai Health system that could not be tracked via EHR.

    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 10.1101/2020.05.17.20104604v1 on medRxiv