Predictors of mortality in hospitalized COVID-19 patients in Athens, Greece

  1. Dimitrios Giannoglou
  2. Evangelia Meimeti
  3. Xenia Provatopoulou
  4. Konstantinos Stathopoulos
  5. Ioannis –Kriton Roukas
  6. Petros Galanis

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Abstract

Background

The epidemic of COVID-19 has rapidly spread worldwide, with millions of confirmed cases and related deaths. Numerous efforts are being made to clarify how the infection progresses and potential factors associated with disease severity and mortality. We investigated the mortality in Greek hospitalized COVID-19 patients and also the predictors of this mortality.

Methods

Study population included 512 COVID-19 patients admitted to the hospitals of the Attica region of Greece. Patients’ demographic characteristics, comorbidities, allergies, previous vaccination for seasonal influenza virus, admission to ICU, intubation, and death were recorded. Potential predictors of in-hospital mortality were identified by regression analysis.

Results

The mean age of hospitalized patients was 60.4 years, and was higher in patients who deceased. The most common comorbidities were respiratory diseases, hypertension, gastrointestinal disorders, dyslipidemia, mental health diseases, asthma, diabetes mellitus and cardiovascular diseases. The need for ICU care and intubation was significantly higher among patients who died. The mortality rate was 15.8% (81 out of 512). Age ≥65 years, cancer, chronic kidney disease, endocrine diseases, central nervous system disorders, anemia, and intubation were independently associated with increased in-hospital mortality, while allergies and previous influenza vaccination were associated with decreased in-hospital mortality.

Conclusion

Our finding of a beneficial effect of allergies and influenza vaccination against COVID-19 infection merits further investigation, as it may shed light in the mechanisms underlying disease progression and severity. Most importantly, it may assist in the implementation of efficient protective measures and public healthcare policies.

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  1. ScreenIT

    SciScore for 10.1101/2020.10.12.20211193: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Data were provided by the 1st Regional Health Authority of Attica, following the approval of the protocol by the Scientific Committee of the Greek Ministry of Health (27628 / 23-06-2020), ensuring legality of conduct, compliance with medical ethical standards and scientific validity.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    IBM SPSS Statistics for Windows, Version 21.0.
    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: We detected the following sentences addressing limitations in the study:
    Our study bears limitations, particularly with regard to the relative small sample size. Nevertheless, we considered the total population of the Athens metropolitan area that required hospitalization for infection by COVID-19 during the period 21/2/2020 and 30/6/2020. Our analysis considered all variables regarding patients’ demographic characteristics and comorbidities that were systematically recorded in a national database. Discrepancies between studies from different countries and populations in data collection and reporting greatly limit the ability of reliable comparisons between findings. This is further reinforced by the intrinsic differences between population groups as well as by the different policies applied in each country with respect to the hospital admission and care of patients with COVID-19. In our study, we applied multivariable regression analysis to identify the risk factors associated with mortality among hospitalized patients with COVID-19. As this methodology can only adjust for measured confounders, there may be other unreported confounding factors that cannot be identified thus requiring further studies. In conclusion, our novel finding is that allergies and previous influenza vaccination decrease mortality in COVID-19 patients. Further studies elucidating the effect of existing allergies and vaccination status may shed light in the immune responses associated with COVID-19 infection and assist in the identification of the underlying mechanisms. Most...

    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 10.1101/2020.10.12.20211193v1 on medRxiv