Clinical course and severity outcome indicators among COVID-19 hospitalized patients in relation to comorbidities distribution: Mexican cohort

  1. COVID-19 Hospitalizations in Mexico
  2. Genny Carrillo
  3. Nina Méndez-Domínguez
  4. Kassandra Desire Santos-Zaldivar
  5. Andrea Rochel-Pérez
  6. Mario Azuela-Morales
  7. Osman Cuevas-Koh
  8. Alberto Álvarez-Baeza

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Abstract

Introduction

COVID-19 affected worldwide, causing to date, around 500,000 deaths. In Mexico, by April 29, the general case fatality was 6.52%, with 11.1% confirmed case mortality and hospital recovery rate around 72%. Once hospitalized, the odds for recovery and hospital death rates depend mainly on the patients’ comorbidities and age. In Mexico, triage guidelines use algorithms and risk estimation tools for severity assessment and decision-making. The study’s objective is to analyze the underlying conditions of patients hospitalized for COVID-19 in Mexico concerning four severity outcomes.

Materials and Methods

Retrospective cohort based on registries of all laboratory-confirmed patients with the COVID-19 infection that required hospitalization in Mexico. Independent variables were comorbidities and clinical manifestations.

Dependent variables were four possible severity outcomes

(a) pneumonia, (b) mechanical ventilation (c) intensive care unit, and (d) death; all of them were coded as binary Results: We included 69,334 hospitalizations of laboratory-confirmed and hospitalized patients to June 30, 2020. Patients were 55.29 years, and 62.61% were male. Hospital mortality among patients aged<15 was 9.11%, 51.99% of those aged >65 died. Male gender and increasing age predicted every severity outcome. Diabetes and hypertension predicted every severity outcome significantly. Obesity did not predict mortality, but CKD, respiratory diseases, cardiopathies were significant predictors.

Conclusion

Obesity increased the risk for pneumonia, mechanical ventilation, and intensive care admittance, but it was not a predictor of in-hospital death. Patients with respiratory diseases were less prone to develop pneumonia, to receive mechanical ventilation and intensive care unit assistance, but they were at higher risk of in-hospital death.

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  1. ScreenIT

    SciScore for 10.1101/2020.07.31.20165480: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Ethical approval and Institutional Review Board for this study were exempted by Universidad Marista de Merida Board of Ethics because this study derives from Open Access anonymized dataset provided under the Mexican board of health through its General Directorate of Health Information webpage.
    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: We detected the following sentences addressing limitations in the study:
    Limitations: Limitations of the present study derive from its retrospective design: first are those resulting from data obtained from the epidemiologic surveillance system Open Access datasets that may or may not include reporting or coding defects. Nevertheless, all information is validated and ratified at different administrative levels. The study does not allow us to analyze any acute comorbidities that could have happened simultaneously in hospitalized patients. Lastly, as in any other health system with COVID-19 sentinel surveillance, we may have unintentionally excluded patients hospitalized but were not tested for COVID-19. Implications and recommendations: Clinical practice guidelines, triage instruments, and algorithms have been precise in guiding hospital care for patients with pneumonia, diabetes, immune impairment, and CPK. However, they could still improve hospital assistance and define resource allocation for patients with algorithms for respiratory conditions, including asthma and COPD.

    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

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  2. Version published to 10.1101/2020.07.31.20165480 on medRxiv