Observational Study of the Efficiency of Treatments in Patients Hospitalized with Covid-19 in Madrid

  1. Nikolas Bernaola
  2. Raquel Mena
  3. Ander Bernaola
  4. Cesar Carballo
  5. Antonio Lara
  6. Concha Bielza
  7. Pedro Larrañaga

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Background

Many different treatments were heavily administered to patients with COVID-19 during the peak of the pandemic in Madrid without robust evidence supporting them.

Methods

We examined the association between sixteen treatments in four groups (steroids, antivirals, antibiotics and immunomodulators) and intubation or death. Data were obtained from patients that were admitted to an HM hospital with suspicion of COVID-19 until 24/04/2020, excluding unconfirmed diagnosis, those who were admitted before the epidemic started in Madrid, had an outcome that was not discharge or death or died within 24 hours of presentation. We compared outcomes between treated and untreated patients using propensity-score caliper matching.

Results

Of 2,307 patients in the dataset, 679 were excluded. Of the remaining 1,645 patients, 263 (16%) died and 311 (18.9%) died or were intubated. Except for hydroxychloroquine and prednisone, patients that were treated with any of the medications were more likely to go through an outcome of death or intubation at baseline. After propensity matching we found an association between treatment with hydroxychloroquine and prednisone and better outcomes (hazard ratios with 95% CI of 0.83 ± 0.06 and 0.85 ± 0.03). Results were similar in multiple sensitivity analyses.

Conclusions

In this multicenter study of patients admitted with COVID-19 hydroxychloroquine and prednisone administration was found to be associated with improved outcomes. Other treatments were associated with no effect or worse outcomes. Randomized, controlled trials of these medications in patients with COVID-19 are needed to avoid heavy administration of treatments with no strong evidence to support them.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.07.17.20155960: (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 variablenot detected.

    Table 2: Resources

    No key resources detected.

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

  2. ScreenIT

    SciScore for 10.1101/2020.07.17.20155960: (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 variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All the analyses were done using Python v3.6.1 with the sklearn, numpy, pandas and scipy libraries.
    Python
    suggested: (IPython, SCR_001658)
          <div style="margin-bottom:8px">
        <div><b>scipy</b></div>
        <div>suggested: (SciPy, <a href="https://scicrunch.org/resources/Any/search?q=SCR_008058">SCR_008058</a>)</div>
      </div>
    </td></tr></table>

    Data from additional tools added to each annotation on a weekly basis.

    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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.07.17.20155960 on medRxiv