The effect of antidepressants on the severity of COVID-19 in hospitalized patients: A systematic review and meta-analysis

  1. Hosein Nakhaee
  2. Moein Zangiabadian
  3. Reza Bayati
  4. Mohammad Rahmanian
  5. Amir Ghaffari Jolfayi
  6. Sakineh Rakhshanderou

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Abstract

Clinical Depression and the subsequent low immunity is a comorbidity that can act as a risk factor for the severity of COVID-19 cases. Antidepressants such as Selective serotonin reuptake inhibitor and Serotonin-norepinephrine reuptake inhibitors are associated with immune-modulatory effects, which dismiss inflammatory responses and reduce lung tissue damage. The current systematic review and meta-analysis aims to evaluate the effect of antidepressant drugs on the prognosis and severity of COVID-19 in hospitalized patients.

Methods

A systematic search was carried out in PubMed/Medline, EMBASE, and Scopus up to June 14, 2022. The following keywords were used: "COVID-19", "SARS-CoV-2", "2019-nCoV", "SSRI", "SNRI", “TCA”, “MAOI”, and “Antidepressant”. A fixed or random-effect model assessed the pooled risk ratio (RR) with 95% CI. We considered P < 0.05 as statistically significant for publication bias. Data were analyzed by Comprehensive Meta-Analysis software, Version 2.0 (Biostat, Englewood, NJ).

Results

Fourteen studies were included in our systematic review. Five of them were experimental with 2350, and nine of them were observational with 290,950 participants. Eight out of fourteen articles revealed the effect of antidepressants on reducing the severity of COVID-19. Selective serotonin reuptake inhibitors drugs, including Fluvoxamine, Escitalopram, Fluoxetine, and Paroxetine, and among the Serotonin-norepinephrine inhibitors medications Venlafaxine, are reasonably associated with reduced risk of intubation or death. Five studies showed no significant effect, and only one high risk of bias article showed the negative effect of antidepressants on the prognosis of Covid-19. The meta-analysis of clinical trials showed that fluvoxamine could significantly decrease the severity outcomes of COVID-19 (RR: 0.763; 95% CI: 0.602–0.966, I2: 0.0)

Findings

Most evidence supports that the use of antidepressant medications, mainly Fluvoxamine, may decrease the severity and improve the outcome in hospitalized patients with SARS-CoV-2. Some studies showed contradictory findings regarding the effects of antidepressants on the severity of COVID-19. Further clinical trials should be conducted to clarify the effects of antidepressants on the severity of COVID-19.

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  1. Version published to 10.1371/journal.pone.0267423
  2. ScreenIT

    SciScore for 10.1101/2022.04.11.22273709: (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.
    Randomizationnot detected.
    BlindingQuality assessment: Two blinded reviewers assessed the quality of the studies using three different assessment tools (checklists): two for observational studies (case controls and cohorts) and one for experimental studies (14).
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Search strategy: We searched Pubmed/Medline, Embase and Scopus for clinical studies reporting the effect of anti-depressants on reducing severity of hospitalized patients with covid-19, published up to January 16, 2022.
    Pubmed/Medline
    suggested: None
    Embase
    suggested: (EMBASE, RRID:SCR_001650)
    We used the following MeSH terms: “‘antidepressive agents’
    MeSH
    suggested: (MeSH, RRID:SCR_004750)
    Study Selection: The records found through database searching were merged, and the duplicates were removed using EndNote X8 (Thomson Reuters, Toronto, ON, Canada).
    EndNote
    suggested: (EndNote, RRID:SCR_014001)

    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:
    Limitations: Most of the included articles in this study were not specific about antidepressant drugs and didn’t evaluate each drug separately. They mainly studied antidepressants in groups like SSRIs, SNRIs, and TCAs. We reviewed antidepressants but could only arrange a meta-analysis for fluvoxamine which was studied in three clinical trial which two of them had a high risk of bias. Our meta-analysis was limited to a small population of about 2000 persons. We also generally expressed our results about severity and outcome and couldn’t arrange a subgroup analysis for each outcome or sex group. Suggestions: As there is strong evidence of the link between antidepressant use and improving outcomes of covid-19, it seems legible to conduct more research on the subject, aiming to find more therapeutic options to treat covid-19. Future studies should focus on antidepressants separately and be more specific about the outcome in different patient groups. We also need to arrange more clinical trials with larger populations to confirm the efficacy of a candidate certainly. SSRIs such as Fluoxetine and Fluvoxamine are supported by stronger evidence and could be favorable options for future research programs.

    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 10.1101/2022.04.11.22273709v1 on medRxiv