Hospital readmissions of discharged patients with COVID-19

  1. Lina Marcela Parra Ramírez
  2. Mireia Cantero Caballero
  3. Ignacio Morrás de la Torre
  4. Alberto Vallejo Plaza
  5. Itziar Diego Yagüe
  6. Elena Jimenez Tejero
  7. Elena Múñez Rubio
  8. Ángel Asensio
  9. Ana Fernández Cruz
  10. Antonio Ramos Martínez

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Abstract

Background

COVID-19 infection has led to an overwhelming effort by health institutions to meet the high demand for hospital admissions.

Aim

To analyse the clinical variables associated with readmission of patients who had previously been discharged after admission for COVID-19.

Design and methods

We studied a retrospective cohort of patients with laboratory-confirmed SARS-CoV-2 infection who were admitted and subsequently discharged alive. We then conducted a nested case-control study paired (1:1 ratio) by age, sex and period of admission.

Results

Out of 1368 patients who were discharged during the study period, 61 patients (4.4%) were readmitted. Immunocompromised patients were at increased risk for readmission. There was also a trend towards a higher probability of readmission in hypertensive patients (p=0.07). Cases had had a shorter hospital stay and a higher prevalence of fever during the 48 hours prior to discharge. There were no significant differences in oxygen levels measured at admission and discharge by pulse oximetry intra-subject or between the groups. Neutrophil/lymphocyte ratio at hospital admission tended to be higher in cases than in controls (p=0.06). The motive for readmission in 10 patients (16.4%), was a thrombotic event in venous or arterial territory (p<0.001). Neither glucocorticoids nor anticoagulants prescribed at hospital discharge were associated with a lower readmission rate.

Conclusions

The rate of readmission after discharge from hospital for COVID-19 was low. Immunocompromised patients and those presenting with fever during the 48 hours prior to discharge are at greater risk of readmission to hospital.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.31.20118455: (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
    Univariate and multivariate logistic regression was undertaken with Stata 13.0 software (StataCorp, College Station, US).
    StataCorp
    suggested: (Stata, RRID:SCR_012763)

    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:
    This study has several limitations that should be highlighted. First, the small sample size may have prevented the identification of differences in some variables. Secondly, it is a single-centre study having its own diagnostic and therapeutic peculiarities. Thirdly, we cannot completely exclude that some of the control patients had been admitted to a private hospital, whose information is not collected in the public health informatics system. However, this is not likely to have occurred. And finally, it should be noted that the demand for hospital admissions and the learning curve of the disease has changed over the course of the epidemic, which may have had a varying impact on patient discharge. In summary, we report a low rate of readmission after discharge from hospital for COVID-19. Immunocompromised patients and those presenting with fever during the 48 hours prior to discharge are at greater risk of readmission to hospital. Given the possibility of further outbreaks of the disease, further research should be encouraged to refine the risk factors for hospital readmission that could help to safely discharge these patients.

    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 10.1101/2020.05.31.20118455v1 on medRxiv