Quantitative chest computed tomography combined with plasma cytokines predict outcomes in COVID-19 patients

  1. Guillermo Carbonell
  2. Diane Marie Del Valle
  3. Edgar Gonzalez-Kozlova
  4. Brett Marinelli
  5. Emma Klein
  6. Maria El Homsi
  7. Daniel Stocker
  8. Michael Chung
  9. Adam Bernheim
  10. Nicole W. Simons
  11. Jiani Xiang
  12. Sharon Nirenberg
  13. Patricia Kovatch
  14. Sara Lewis
  15. Miriam Merad
  16. Sacha Gnjatic
  17. Bachir Taouli

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Abstract

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  1. Version published to 10.1016/j.heliyon.2022.e10166
  2. ScreenIT

    SciScore for 10.1101/2021.10.11.21264709: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Cohort Design: This retrospective single-center study was approved by the Mount Sinai Health System Institution Review Board (IRB).
    Consent: A waiver of informed consent was obtained from the IRB to query patient’s electronic medical record (EMR).
    Field Sample Permit: All research methods were carried out in accordance with relevant human subjects research guidelines and regulations.
    Sex as a biological variablenot detected.
    RandomizationA second reader (B.M., PGY-5 radiology resident) performed manual corrections on 15 patients randomly selected to assess inter-observer variability.
    BlindingCT qualitative score: Image analysis was performed by two independent experienced readers (M.C. and A.B., fellowship-trained cardio-thoracic radiologists, both with 5 years of experience) who were blinded to the clinical and laboratory data, but aware of COVID-19 diagnosis.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Clinical and laboratory data: Demographic and clinical data was extracted from the Epic electronic health record (Verona, WI) for the identified patients using Epic Hyperspace (August 2019), Epic Clarity (February 2020) and Epic Caboodle (February 2020) databases via connecting to Oracle (18c Enterprise Edition Release 18.0.0.0.0) and SQL server
    August
    suggested: None
    The tables were read-in and written using R packages tidyverse (v 1.3), reshape2 (v 1.4), rms (v 6.1), glmnet (4.1), ggplot2 (v 2.0) (29) and readxl (v 1.3.1).
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

    Results from scite Reference Check: We found no unreliable references.

    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 10.1101/2021.10.11.21264709v1 on medRxiv