Visual scoring of chest CT at hospital admission predicts hospitalization time and intensive care admission in Covid-19

  1. Erik Ahlstrand
  2. Sara Cajander
  3. Per Cajander
  4. Edvin Ingberg
  5. Erika Löf
  6. Matthias Wegener
  7. Mats Lidén

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Abstract

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  1. Version published to 10.1080/23744235.2021.1910727
  2. ScreenIT

    SciScore for 10.1101/2020.10.30.20222471: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Ethics: The Swedish Ethical Review Authority approved the study protocol and waived the informed consent requirement for this retrospective study, reference number 2020-02515.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    For antibody testing, the Diasorin (Saluggia, Italy) Liaison XL test for SARS-CoV-2 IgG was used, in combination with Euroimmun (Lübeck, Germany) SARS-CoV-2 IgG ELISA for confirmation in weakly positive samples to increase specificity.
    SARS-CoV-2 IgG
    suggested: None
    Software and Algorithms
    SentencesResources
    Outcome measures: Statistics: Matlab R2020a (The MathWorks Inc., Natick, MA) was used for statistics.
    Matlab
    suggested: (MATLAB, RRID:SCR_001622)

    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:
    The study has several limitations. Consistent with the inclusion criteria, the results only apply to in-patients. The use of clinically provided scores by multiple readers is a limitation, but also a strength in the study. Visual scoring is subjective and prone to interobserver variation, which reduces the precision of the provided scores. On the other hand, the scores used in the study are a reasonable estimate of the precision in a clinical scenario. Since the ÖCoS scores were provided in clinical routine, the reviewers were not formally blinded, but the main study outcomes of HFD60 and ICU admittance were naturally unknown to reviewers by the time of chest CT evaluation. Furthermore, we only included laboratory confirmed cases of covid-19, and consequently a few covid-19 cases are likely to have been excluded from the analysis21. In conclusion, concise visual scoring of chest CT at hospital admission and at ICU transfer in clinical routine predicts the clinical outcome of covid-19, especially in patients <70 years. In situations where adjuvant treatments and hospital beds are limited, we believe that scoring of chest CT is informative and a valuable tool for clinical decision making.

    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 10.1101/2020.10.30.20222471v1 on medRxiv