Remdesivir for the treatment of COVID-19: a systematic review and meta-analysis

  1. Todd C. Lee
  2. Srinivas Murthy
  3. Olivier Del Corpo
  4. Julien Senécal
  5. Guillaume Butler-Laporte
  6. Zahra N. Sohani
  7. James M. Brophy
  8. Emily G. McDonald

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Abstract

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  1. Version published to 10.1016/j.cmi.2022.04.018
  2. ScreenIT

    SciScore for 10.1101/2022.01.22.22269545: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    RandomizationSearch Strategy, Study selection, and Data Extraction: We searched PubMed from January 1st, 2020, to January 21, 2022 in order to identify randomized controlled trials comparing remdesivir to placebo or standard of care.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Search Strategy, Study selection, and Data Extraction: We searched PubMed from January 1st, 2020, to January 21, 2022 in order to identify randomized controlled trials comparing remdesivir to placebo or standard of care.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Assessment of Bias: Two independent reviewers assessed each study for bias using the Cochrane risk-of-bias 2 tool for randomized trials.
    Cochrane risk-of-bias
    suggested: None
    A DerSimonian–Laird random effects meta-analysis on the risk ratio (RR) scale was used to undertake our frequentist analysis using the metan8 command in STATA version 17 (STATACorp, USA).
    STATA
    suggested: (Stata, RRID:SCR_012763)
    STATACorp
    suggested: None

    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:
    There are limitations to this analysis, the principal one being that the standard of care for Covid-19 continues to evolve at a staggering pace. Earlier in the pandemic, trial participants were less likely to receive treatments now known to reduce adverse outcomes including steroids, monoclonal antibodies, immunomodulatory therapies, or therapeutic anticoagulation. Additionally, very few of the participants included in this analysis were vaccinated against Covid-19 and all results predate the delta and omicron variants. Whether there will be additional large randomized controlled trials of remdesivir in vaccinated patients with newer variants remains to be seen and so inferring a precise magnitude of benefit of remdesivir in these populations is challenging. A final limitation we wish to note is a small lack of granularity with respect to oxygen requirements for a handful of patients; in this case, an individual patient meta-analysis could provide more precise results and transparent data reporting and sharing is welcomed. The strengths of this analysis are the avoidance of duplicated patients despite the inclusion of published SOLIDARITY country-level studies, our a priori decision to stratify the analysis by oxygen requirements, and the added bayesian analysis to contextualize the probability of a reduction in mortality from remdesivir.

    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 10.1101/2022.01.22.22269545v1 on medRxiv