Prevalence and risk factors for mortality related to COVID-19 in a severely affected area of Madrid, Spain

  1. Ramón Pérez-Tanoira
  2. Felipe Pérez-García
  3. Juan Romanyk
  4. Peña Gómez-Herruz
  5. Teresa Arroyo
  6. Rosa González
  7. Lourdes Lledó García
  8. Cristina Verdú Expósito
  9. José Sanz Moreno
  10. Isabel Gutiérrez
  11. Abel Uribe Mathews
  12. Esther López Ramos
  13. Lara Maceda Garcia
  14. Daniel Troncoso
  15. Juan Cuadros-González

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Abstract

BACKGROUND

The coronavius disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reached Spain by 31 January 2020, in April 2020, the Comunidad de Madrid suffered one of the world’s highest crude mortality rate ratios. This study aimed to detect risk factors for mortality in patients with COVID-19.

METHODS

Our cohort were all consecutive adult patients (≥18 years) with laboratory-confirmed COVID-19 at a secondary hospital in Madrid, March 3-16, 2020. Clinical and laboratory data came from electronic clinical records and were compared between survivors and non-survivors, with outcomes followed up until April 4. Univariable and multivariable logistic regression methods allowed us to explore risk factors associated with in-hospital death.

FINDINGS

The cohort comprised 562 patients with COVID-19. Clinical records were available for evaluation for 392 patients attended at the emergency department of our hospital, of whom 199 were discharged, 85 remained hospitalized and 108 died during hospitalization. Among 311 of the hospitalized patients, 34.7% died. Of the 392 patients with records, the median age was 71.5 years (50.6-80.7); 52.6% were men. 252 (64.3%) patients had a comorbidity, hypertension being the most common: 175 (44.6%), followed by other cardiovascular disease: 102 (26.0%) and diabetes: 97 (24.7%). Multivariable regression showed increasing odds of in-hospital death associated with age over 65 (odds ratio 8.32, 95% CI 3.01–22.96; p<0.001), coronary heart disease (2.76, 1.44-5.30; 0.002), and both lower lymphocyte count (0.34, 0.17–0.68; 0.002) and higher LDH (1.25, 1.05-1.50; 0.012) per 1-unit increase and per 100 units respectively.

INTERPRETATION

COVID-19 was associated in our hospital at the peak of the pandemic with a crude mortality ratio of 19.2% and a mortality ratio of 34.7% in admitted patients, considerably above most of the ratios described in the Chinese series. These results leave open the question as to which factors, epidemiological or intrinsically viral, apart from age and comorbidities, can explain this difference in excess mortality.

FUNDING

None.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.25.20112912: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical analysis was performed with SPSS
    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 research may have some limitations. Firstly, due to the retrospective study design, not all laboratory tests were done in all patients, including lactate dehydrogenase, IL-6, serum ferritin, and d-dimer. Their role may therefore be underestimated in predicting in-hospital death. An additional limitation is that some patients were transferred to another hospital, so we could not follow their outcomes. Thirdly, the number of cases may be underestimated due to frequency of collection of respiratory samples and a relatively low positive rate of detection of SARS-CoV-2 RNA in throat swabs.

    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 10.1101/2020.05.25.20112912 on medRxiv