Multisystem Inflammatory Syndrome in Children: Survey of Protocols for Early Hospital Evaluation and Management

  1. Matthew L. Dove
  2. Preeti Jaggi
  3. Michael Kelleman
  4. Mayssa Abuali
  5. Jocelyn Y. Ang
  6. Wassim Ballan
  7. Sanmit K. Basu
  8. M. Jay Campbell
  9. Sathish M. Chikkabyrappa
  10. Nadine F. Choueiter
  11. Katharine N. Clouser
  12. Daniel Corwin
  13. Amy Edwards
  14. Shira J. Gertz
  15. Rod Ghassemzadeh
  16. Rima J. Jarrah
  17. Sophie E. Katz
  18. Stacie M. Knutson
  19. Joseph D. Kuebler
  20. Jennifer Lighter
  21. Christine Mikesell
  22. Kanokporn Mongkolrattanothai
  23. Ted Morton
  24. Natasha A. Nakra
  25. Rosemary Olivero
  26. Christina M. Osborne
  27. Laurie E. Panesar
  28. Sarah Parsons
  29. Rupal M. Patel
  30. Jennifer Schuette
  31. Deepika Thacker
  32. Adriana H. Tremoulet
  33. Navjyot K. Vidwan
  34. Matthew E. Oster

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Abstract

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  1. Version published to 10.1016/j.jpeds.2020.10.026
  2. ScreenIT

    SciScore for 10.1101/2020.07.29.20164459: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: No patient data were collected as part of this inquiry, and this study was considered Non-Human Subjects Research by the CHOA Institutional Review Board.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    14,15 REDCap is a secure, web-based software platform designed to support data capture for research studies, providing 1) an intuitive interface for validated data capture; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for data integration and interoperability with external sources.
    REDCap
    suggested: (REDCap, RRID:SCR_003445)
    We performed descriptive statistics to summarize quantitative elements via SAS 9.4 and Microsoft Excel.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)

    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 study is not without its limitations. First, we did not provide a definition for severity of illness, as severity can differ between institutions. Therefore, what may be considered a moderate case at one center may be severe at another; this difference should be acknowledged when interpreting the treatment options. Second, there was a wide variation in experience in managing MIS-C; there were 6 centers with experience treating >25 patients, and 2 centers with no experience treating MIS-C patients. Thus, some protocols may be based on experience whereas others may be based on personal opinion. We attempted to overcome this limitation by comparing the protocols at those centers with more experience vs. those without. Finally, it is important to recognize that this study captures what centers have recommended for the evaluation and management of MIS-C at their institution, not what has actually been done for those patients. Indeed, protocols may serve as a framework for managing patients with MIS-C, but care may be individualized as dictated by patient signs, symptoms, and response to treatment.

    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 10.1101/2020.07.29.20164459v1 on medRxiv