Clinical characteristics and outcomes of patients with COVID-19 and ARDS admitted to a third level health institution in Mexico City

  1. Carmen M. Hernández Cárdenas
  2. Carlos Torruco Sotelo
  3. Felipe Jurado
  4. Héctor Serna-Secundino
  5. Cristina Aguilar
  6. José G. García-Olazarán
  7. Diana Hernández García
  8. Gustavo Lugo Goytia

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Abstract

Background

In December 2019, the first cases of severe pneumonia associated with a new coronavirus were reported in Wuhan, China. Severe respiratory failure requiring intensive care was reported in up to 5% of cases. There is, however, limited information available in Mexico.

Objectives

The purpose of this study was to describe the clinical manifestations, and outcomes in a COVID-19 cohort attended to from March to May 2020 in our RICU. In addition, we explored the association of clinical variables with mortality.

Methods

The first consecutive patients admitted to the RICU from March 3, 2020, to Jun 24, 2020, with confirmed COVID-19 were investigated. Clinical and laboratory data were obtained. Odds ratios (ORs) were calculated using a logistic regression model. The survival endpoint was mortality at discharge from the RICU.

Results

Data from 68 consecutive patients were analyzed. Thirty-eight patients survived, and 30 died (mortality: 44.1 %). Of the 16 predictive variables analyzed, only 6 remained significant in the multivariate analysis [OR (95% confidence interval)]: no acute kidney injury (AKI)/AKI 1: [.61 (.001;.192)]; delta lymphocyte count: [.061 (.006;.619)]; delta ventilatory ratio: [8.19 (1.40;47.8)]; norepinephrine support at admission: [34.3 (2.1;550)]; body mass index: [1.41 (1.09;1.83)]; and bacterial coinfection: [18.5 (1.4;232)].

Conclusions

We report the characteristics and outcome of patients with ARDS and COVID-19. We found six independent factors associated with the mortality risk: delta lymphocyte count, delta ventilatory ratio, BMI, norepinephrine support, no AKI/AKI 1, and bacterial coinfection.

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  1. ScreenIT

    SciScore for 10.1101/2020.09.12.20193409: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The studies were granted exemption by the hospital institutional review boards.
    Consent: The requirement for informed consent was also waived.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    SPSS version 21 (IBM Statistics, Armonk, New York) was used to analyze the data.
    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: We detected the following sentences addressing limitations in the study:
    Study limitations: One limitation of the study is the small size of the cohort, which originated from a single third-level center in Mexico; the results may not then be extrapolated with certainty to other non-similar populations. However, our results are generally consistent with those reported in other larger cohorts by researchers in China, Europe, and North America. Although the statistical logistic regression model showed a good discrimination performance, it should not be used for prediction purposes, as it requires internal and external evaluations as well as a larger number of patients. However, the association of the independent variables with the risk of mortality may help in better understanding the pathophysiological response of the organism to infection by SARS-CoV-2. In subsequent studies, these variables can be considered as potential predictors in the development of prognostic models. Some of the strengths of the study are that it investigated a prospective cohort with a well-defined zero time for each patient and that the data were obtained in real time. In summary, we described the clinical, laboratory, and radiological characteristics in a prospective cohort of critically ill patients with COVID-19 and the independent factors associated with mortality. Based on this information, it is possible to suggest some management recommendations in patients with COVID-19 who require intensive care; respiratory management based on low tidal volumes and adjustment of p...

    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 10.1101/2020.09.12.20193409v1 on medRxiv