Association between antidepressant use and reduced risk of intubation or death in hospitalized patients with COVID-19: results from an observational study

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Abstract

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  1. Sobia Haqqi

    Review 1: "SSRIs and SNRIs and Risk of Death or Intubation in COVID-19: Results from an Observational Study"

    Paper claims that antidepressants lower the risk of death or intubation in patients with COVID-19. The findings suggest antidepressants may contribute to the treatment of COVID-19, but it would require extensive research to validate the claims.

  2. Prabin Shrestha

    Review 2: "SSRIs and SNRIs and Risk of Death or Intubation in COVID-19: Results from an Observational Study"

    Paper claims that antidepressants lower the risk of death or intubation in patients with COVID-19. The findings suggest antidepressants may contribute to the treatment of COVID-19, but it would require extensive research to validate the claims.

  3. SciScore for 10.1101/2020.07.09.20143339: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This observational study using routinely collected data received approval from the Institutional Review Board of the AP-HP clinical data warehouse (decision CSE-20-20_COVID19, IRB00011591).
    Consent: AP-HP clinical Data Warehouse initiative ensures patients’ information and informed consent regarding the different approved studies through a transparency portal in accordance with European Regulation on data protection and authorization n°1980120 from National Commission for Information Technology and Civil Liberties (CNIL).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Medications and their mode of administration (i.e., dosage, frequency, date, condition of intake) were identified from medication administration data or scanned handwritten medical prescriptions, through two deep learning models based on BERT contextual embeddings,19 one for the medications and another for their mode of administration.
    BERT
    suggested: (BERT, RRID:SCR_018008)
    3 (R Project for Statistical Computing).
    R Project for Statistical
    suggested: (R Project for Statistical Computing, RRID:SCR_001905)

    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:
    Additional limitations of our study include missing data for some baseline characteristic variables, including baseline clinical and biological severity of COVID-19, which may be explained by the overwhelming of all hospital units during the COVID-19 peak incidence, and potential for inaccuracies in the electronic health records in this context, such as the possible lack of documentation of illnesses or medications, or the misidentification of treatments’ mode of administration (e.g., dosage, frequency), especially for hand-written medical prescriptions. Furthermore, type I error inflation due to multiple testing may have occurred in our study. However, our analyses were exploratory, and results were similar across different statistical approaches. Finally, despite the multicenter design, our results may not be generalizable to other settings or regions. In this multicenter observational retrospective study involving patients admitted to the hospital with COVID-19, SSRI use at usual antidepressant doses during the visit was associated with lower risk of intubation or death. Double-blind controlled randomized clinical trials of these medications for COVID-19 are needed.

    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.
    • No funding statement was detected.
    • 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.