Differential effect of corticosteroid treatment on Influenza, SARS, MERS, and SARS-CoV-2 patients: A meta-analysis and systematic review

This article has been Reviewed by the following groups

Read the full article

Abstract

Background

Corticosteroid has been used to manage inflammation caused by many diseases including respiratory viral infections. Many articles are available to support the good and bad side of this steroid use but remain inconclusive. To find some evidence about the safety of the drug, we investigated the effect of corticosteroids on the mortality of patients with respiratory viral infections including SARS-CoV-2, SARS, MERS, and Influenza.

Method

We searched articles in PubMed, Scopus, Cochrane, Medline, Google Scholar, and Web of Science records using keywords “corticosteroid” or “viral infection” or “patients” or “control study”. Mortality was the primary outcome.

Result

Our selected 24 studies involving 16633 patients were pooled in our meta-analysis. Corticosteroid use and overall mortality were not significantly associated (P=0.176), but in subgroup analysis, corticosteroid use was significantly associated with lower mortality in the case of SARS (P=0.003) but was not significantly associated with mortality for Influenza (H1N1) (P=0.260) and SARS-CoV-2 (P=0.554). Further analysis using study types of SARS-CoV-2, we found that corticosteroid use was not significantly associated with mortality in the case of retrospective cohort studies (P=0.256) but was significantly associated with lower mortality in the case of randomized control trials (P=0.005). Our findings uncover how the outcome of particular drug treatment for different diseases with comparable pathogenesis may not be similar and, RCTs are sometimes required for robust outcome data.

Conclusion

At the beginning of the COVID-19 pandemic, data of corticosteroid use from other viral infections along with COVID-19 observational and retrospective cohort studies created confusion of its effect, but randomized control trials showed that corticosteroid can be used to treat COVID-19 patients.

Article activity feed

  1. SciScore for 10.1101/2021.03.22.21254104: (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 variableAuthors were asked to extract information on authors, title, year of publication, number of patient in the treatment arm, number of patient in the control arm, a corticosteroid used, placebo-control or not, type of study, the mortality rate (control/corticosteroid), secondary infection rate (bacterial), secondary infection rate (viral), male/female (% of the total patient), male/female (% of control/corticosteroid group), the mean age of the total patient, mean age of the dead patient, length of hospital stay, length of ICU stay, dead patients underlying health conditions, comorbidities of patients, name of viral infection, length of mechanical ventilation, Viral/RNA clearance time/viral shedding duration, and the median daily dosage of corticosteroid.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    2.1 Articles search strategies: A literature search was performed in PubMed, Scopus, Cochrane Central Register of Controlled Trials (CENTRAL), Medline, Google Scholar, and Web of Science records using keywords such as: “corticosteroid” or “viral infection” or “patients” or “control study”.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Cochrane Central Register of Controlled Trials
    suggested: (Cochrane Central Register of Controlled Trials, RRID:SCR_006576)
    Medline
    suggested: (MEDLINE, RRID:SCR_002185)
    Google Scholar
    suggested: (Google Scholar, RRID:SCR_008878)
    2.6 Statistical Analysis: All statistical analysis was performed by using statistical software STATA 13 [29].
    STATA
    suggested: (Stata, RRID:SCR_012763)

    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:
    Regardless of these outcomes, our study has some limitations. First, the strength of the conclusion made by the studies before the COVID-19 pandemic is not so strong because there was only one RCT study in our analysis, all studies that we include in this review are observational studies having many lurking variables. Now that RCTs showed a beneficial effect of corticosteroid, the deleterious effect of it from the cohort studies becomes questionable. Second, we could not separate patients who obtained corticosteroids for underlying disease (e.g., COPD). We also could not perform subgroup analysis based on doses of steroid received, early or late steroid use, or other factors. Data on dose, duration, timing, types, and rationales of corticosteroid administration and the timing and duration of antiviral therapy were very insufficient. Before starting this review one of the goals was to see what happens if corticosteroid is applied after cytokine storm vs before cytokine storm but due to data limitation, it was not possible to analyze and opens a new avenue to investigate in the future. Third, the baseline criteria of the patients can control outcomes and differences among the studies included in our analysis. For example, an association had been observed between fewer secondary infections and younger age and fewer underlying diseases.

    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.