Clinical and Demographic Characteristics of COVID-19 Patients Admitted in a Tertiary Care Hospital in the Dominican Republic

  1. David De Luna
  2. Yori Roque
  3. Nicolás Batlle
  4. Katherine Gómez
  5. Miguelina Jáquez
  6. Brinia Cabrera
  7. Lissa de la Cruz
  8. Osvaldo Tavárez
  9. Rossy Belliard
  10. José Javier Sanchez

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Abstract

To present clinical and demographic characteristics of COVID-19 patients admitted to Hospital Metropolitano de Santiago in Dominican Republic, we analyzed electronic medical records of all hospitalized patients clinically admitted as viral pneumonia through March - April, 2020. Of 374 patients, 150 (40.1%) laboratory confirmed, were included in this study. Most of the patients were men (104 / 69.3%) with a median (IQR 44 - 66) age of 54. Hypertension (83 / 55.3%) and diabetes mellitus (49 / 32.7%) were the most common comorbidities, whereas fever (120 / 80%), cough (79 / 52.7%) and fatigue (60 / 40%) were the most common presenting symptoms. 28 (18.7%) patients required admission to the intensive care unit, of them, 26 patients (17.3%) required mechanical ventilation. The overall mortality rate was 10.7% Higher levels of inflammatory markers were associated with longer length of stay (LOS). This findings indulge information that could contribute to stratify patients at higher risk of complications.

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  1. ScreenIT

    SciScore for 10.1101/2020.12.11.20247437: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The study was approved by the institutional review board (IRB) of Pontificia Universidad Católica Madre y Maestra (PUCMM).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The data was analyzed in SPSS version 25.0.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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. Version published to 10.1101/2020.12.11.20247437v1 on medRxiv