A Competing Risk Analysis of Early COVID-19 Treatments

  1. Gilberto Gonzalez-Arroyo
  2. Mario F. Rodriguez-Moran
  3. Maricela Garcia-Arreola
  4. Karla Guadalupe Lopez-Lopez
  5. Tonatihu Ortiz-Castillo
  6. Salvador Gomez-Garcia
  7. Cleto Alvarez-Aguilar
  8. Anel Gomez-Garcia
  9. Arturo Lopez-Pineda

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Abstract

Introduction

The advent of the SARS-CoV-2 virus posed formidable challenges on a global scale. In the year 2020, existing treatments were not tailored specifically to combat this novel virus, and the absence of a developed vaccine added to the complexity. Clinical guidelines underwent rapid evolution during the initial months of the pandemic, leaving uncertainty about the efficacy of various drug combinations in treating the disease. This study delves into an analysis of outcomes during the early stages of the pandemic within the Mexican Institute of Social Security (IMSS), the largest healthcare system in Mexico.

Material and Methods

In this retrospective observational study, we examined the medical records of 130,216 COVID-19 patients treated in two Mexican states throughout the year 2020. We conducted a competing risk analysis, considering death and recovery as potential outcomes. This was further complemented by a Cox-regression and Kaplan-Meier analysis. To enhance predictive insights, machine learning models were constructed to forecast outcomes at 10, 20, and 30 days.

Results

Our analysis revealed a heightened prevalence of comorbidities, including obesity, diabetes, and heart disease, aligning with Mexico’s established epidemiological profile. Mortality patterns indicated occurrences approximately 15-20 days from the onset of symptoms. Notably, patients undergoing treatment with cephalosporin in conjunction with neuraminidase inhibitors (NAIs) exhibited the poorest survival rates, whereas those receiving adamantane, fluoroquinolone, or penicillin demonstrated the most favorable survival outcomes.

Conclusions

The identified associations caution against the utilization of specific treatment combinations, providing crucial insights for refining the country’s clinical guidelines and optimizing patient care strategies.

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  1. Version published to 10.1101/2021.12.16.21267866v3 on medRxiv
  2. Version published to 10.1101/2021.12.16.21267866v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.12.16.21267866: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Institutional Review Board (IRB) approval was obtained by IMSS National Bioethics Committee and IMSS National Research Committee, under protocol numbers R-2021-1912-014, R-2020-785-058.
    Sex as a biological variablenot detected.
    RandomizationAll missing data was considered to be missing completely at random (MCAR), which is a reasonable assumption for observational studies and the potential loss to follow-up for a variety of reasons.
    Blindingnot detected.
    Power Analysisnot 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: We detected the following sentences addressing limitations in the study:
    However, our study had several limitations regarding access to data in real-time, and more information directly from the electronic health record. In the future, a more robust pipeline should include a biosurveillance mechanism to estimate the risk of each treatment pattern.

    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.

  4. Version published to 10.1101/2021.12.16.21267866v1 on medRxiv