Association between Hydroxyzine Use and Reduced Mortality in Patients Hospitalized for Coronavirus Disease 2019: Results from a multicenter observational study

  1. Nicolas Hoertel
  2. Marina Sánchez
  3. Raphaël Vernet
  4. Nathanaël Beeker
  5. Antoine Neuraz
  6. Carlos Blanco
  7. Mark Olfson
  8. Cédric Lemogne
  9. Pierre Meneton
  10. Christel Daniel
  11. Nicolas Paris
  12. Alexandre Gramfort
  13. Guillaume Lemaitre
  14. Elisa Salamanca
  15. Mélodie Bernaux
  16. Ali Bellamine
  17. Anita Burgun
  18. Frédéric Limosin

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Objective

To examine the association between hydroxyzine use and mortality in patients hospitalized for COVID-19, based on its anti-inflammatory and antiviral properties.

Design

Multicenter observational retrospective cohort study.

Setting

Greater Paris University hospitals, France.

Participants

7,345 adults hospitalized for COVID-19 between 24 January and 1 April 2020, including 138 patients (1.9%) who received hydroxyzine during the visit at a mean dose of 49.8 mg (SD=51.5) for an average of 22.4 days (SD=25.9).

Data source

Assistance Publique-Hôpitaux de Paris Health Data Warehouse.

Main outcome measures

The study endpoint was death. We compared this endpoint between patients who received hydroxyzine and those who did not in time-to-event analyses adjusting for patient characteristics (such as age, sex, and comorbidities), clinical and biological markers of disease’s severity, and use of other medications. The primary analysis was a multivariable Cox model with inverse probability weighting. Sensitivity analyses included a multivariable Cox model and a univariate Cox regression model in a matched analytic sample in a 1:1 ratio.

Results

Over a mean follow-up of 20.3 days (SD=27.5), 994 patients (13.5%) had a primary end-point event. The primary multivariable analysis with inverse probability weighting showed a significant association between hydroxyzine use and reduced mortality (HR, 0.42; 95% CI, 0.25 to 0.71; p=0.001) with a significant dose-effect relationship (HR, 0.10; 95% CI, 0.02 to 0.45; p=0.003). This association was similar in sensitivity analyses. In secondary analyses conducted among subsamples of patients, we found a significant association between hydroxyzine use and a faster decrease in biological inflammatory markers associated with COVID-19-related mortality, including neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-C-reactive protein ratio (LCRP), and circulating interleukin 6 levels (IL-6) (all p<0.016), with a significant dose-effect relationship for NLR and LCRP (both p<0.037).

Conclusions

In this retrospective observational study, hydroxyzine use was associated with reduced mortality in patients hospitalized for COVID-19. This association may be partially mediated by specific anti-inflammatory properties of H1 antihistamines. Double-blind controlled randomized clinical trials of hydroxyzine for COVID-19 are needed to confirm these results.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.10.23.20154302: (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 consent regarding the different approved studies and data opt-out service 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). 2.2.
    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 hand-written medical prescriptions, through two deep learning models based on BERT contextual embeddings,18 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:
    Our study has several limitations. First, there are two possible major potential inherent biases in observational studies: unmeasured confounding and confounding by indication. We tried to minimize the effects of confounding in different ways. First, we used a multivariable regression model with inverse probability weighting to minimize the effects of confounding by indication.21 22 Second, we performed sensitivity analyses, including a multivariable Cox regression models and an univariate Cox regression model in a matched analytic sample, that showed similar results. Third, although some amount of unmeasured confounding may remain, our analyses adjusted for numerous potential confounders and a quantitative bias analysis with observed imbalances suggested that residual confounding is unlikely to affect our results. Fourth, the significant dose-effect relationship further supported our conclusion. Fifth, the use of an active comparator, zopiclone, yielded similar results. Finally, the association was only observed in patients who received hydroxyzine during the visit and not in those who received it only within the 3 months before hospital admission. A second limitation includes missing data for some baseline characteristic variables, including baseline clinical and biological markers of 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 th...

    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.

  2. Version published to 10.1101/2020.10.23.20154302v1 on medRxiv
  3. Version published to 10.1101/2020.10.23.20154302v2 on medRxiv