Remdesivir-based therapy improved recovery of patients with COVID-19 in the SARSTer multicentre, real-world study

  1. Robert Flisiak
  2. Dorota Zarębska-Michaluk
  3. Aleksandra Berkan-Kawińska
  4. Magdalena Tudrujek-Zdunek
  5. Magdalena Rogalska
  6. Anna Piekarska
  7. Dorota Kozielewicz
  8. Krzysztof Kłos
  9. Marta Rorat
  10. Beata Bolewska
  11. Anna Szymanek-Pasternak
  12. Włodzimierz Mazur
  13. Beata Lorenc
  14. Regina Podlasin
  15. Katarzyna Sikorska
  16. Barbara Oczko-Grzesik
  17. Cezary Iwaszkiewicz
  18. Bartosz Szetela
  19. Paweł Pabjan
  20. Małgorzata Pawłowska
  21. Krzysztof Tomasiewicz
  22. Joanna Polańska
  23. Jerzy Jaroszewicz

Reviewed by

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

No abstract available

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.10.30.20215301: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Univariate comparisons were calculated by GraphPad Prism 5.1 (GraphPad Software, Inc., La Jolla, CA).
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)
    Logistic regression models were calculated by use of R and MATLAB (MathWorks, MA, USA).
    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:
    Patients were assessed daily using an ordinal scale and the primary outcome measure was the first day on which the patient reached one of the three lowest levels in the scale meaning “not hospitalized with or without limitation of activities” and “hospitalized not requiring supplemental oxygen and ongoing medical care”. However, the trial is not completed and final data are still expected [17]. Also, data from the compassionate-use programme confirmed the benefit of RDV treatment in patients with severe COVID-19, including those on invasive ventilation, reporting a 68% rate of clinical recovery [23]. Unlike the above-mentioned studies, no positive impact of RDV relative to placebo was documented in patients with the severe form of COVID-19 with baseline oxygen saturation < 95% included in the randomized, double-blind multicentre phase III clinical trial conducted in Wuhan [14]. However, it should be noted that enrolment was prematurely terminated due to control of the epidemic in Wuhan, and hence the study was underpowered. The current analysis demonstrated benefit from RDV treatment as compared to LPV/r in terms of secondary end-points. Among them, a statistically significant difference was found for rate of no improvement within 28 days in the ordinal scale, whereas for the remaining parameters, including death rate, the need for constant oxygen therapy and its duration, the need for mechanical ventilation, rate of positive RT PCR for SARS-CoV-2 after 30 days, and hospitali...

    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.

  2. Version published to 10.20452/pamw.15735
  3. Version published to 10.1101/2020.10.30.20215301 on medRxiv