The association between mechanical ventilator compatible bed occupancy and mortality risk in intensive care patients with COVID-19: a national retrospective cohort study

  1. Harrison Wilde
  2. Thomas Mellan
  3. Iwona Hawryluk
  4. John M. Dennis
  5. Spiros Denaxas
  6. Christina Pagel
  7. Andrew Duncan
  8. Samir Bhatt
  9. Seth Flaxman
  10. Bilal A. Mateen
  11. Sebastian J. Vollmer

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Background

The literature paints a complex picture of the association between mortality risk and ICU strain.

In this study, we sought to determine if there is an association between mortality risk in intensive care units (ICU) and occupancy of beds compatible with mechanical ventilation, as a proxy for strain.

Methods

A national retrospective observational cohort study of 89 English hospital trusts (i.e. groups of hospitals functioning as single operational units). Seven thousand one hundred thirty-three adults admitted to an ICU in England between 2 April and 1 December, 2020 (inclusive), with presumed or confirmed COVID-19, for whom data was submitted to the national surveillance programme and met study inclusion criteria. A Bayesian hierarchical approach was used to model the association between hospital trust level (mechanical ventilation compatible), bed occupancy, and in-hospital all-cause mortality. Results were adjusted for unit characteristics (pre-pandemic size), individual patient-level demographic characteristics (age, sex, ethnicity, deprivation index, time-to-ICU admission), and recorded chronic comorbidities (obesity, diabetes, respiratory disease, liver disease, heart disease, hypertension, immunosuppression, neurological disease, renal disease).

Results

One hundred thirty-five thousand six hundred patient days were observed, with a mortality rate of 19.4 per 1000 patient days. Adjusting for patient-level factors, mortality was higher for admissions during periods of high occupancy (> 85% occupancy versus the baseline of 45 to 85%) [OR 1.23 (95% posterior credible interval (PCI): 1.08 to 1.39)]. In contrast, mortality was decreased for admissions during periods of low occupancy (< 45% relative to the baseline) [OR 0.83 (95% PCI 0.75 to 0.94)].

Conclusion

Increasing occupancy of beds compatible with mechanical ventilation, a proxy for operational strain, is associated with a higher mortality risk for individuals admitted to ICU. Further research is required to establish if this is a causal relationship or whether it reflects strain on other operational factors such as staff. If causal, the result highlights the importance of strategies to keep ICU occupancy low to mitigate the impact of this type of resource saturation.

Article activity feed

  1. Version published to 10.1186/s12916-021-02096-0
  2. ScreenIT

    SciScore for 10.1101/2021.01.11.21249461: (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

    No key resources detected.

    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:
    Strengths and Limitations: The strengths of this study are the national cohort of patient-level data with extensive capture of admissions,[20] coupled with a rigorous modelling method (eTable 4 & eMethods). Limitations include a lack of physiological data, limiting our ability to adjust for differences in severity upon admission. However, it is worth noting that previous studies using linked CHESS data from the first wave of the pandemic did not find between centre variation in severity scores (e.g. mean APACHE-II) to be associated with mortality risk.[21] Moreover, the characterisation of operational strain as a function of surge occupancy likely fails to fully reflect the complexity of using non-specialist staff and other resource allocation issues present when ‘creating’ new ICU beds (see eTable 5 using an alternative definition of occupancy based on baseline capacity; mortality risk given this linear continuous factor was 1.27 (95% PCI: 1.13 – 1.43). Finally, we lack clear 30-day outcome data for discharged and transferred individuals, and thus were forced to model under a naïve assumption that these individuals survived, which may have impacted our estimates of the risk. Implications for Policy Makers and Clinicians: In summary, our study highlights the importance of public health interventions (such as expeditious vaccination programmes and non-pharmacological interventions), to control both incidence and prevalence of COVID-19, and therefore actively manage ICU occupan...

    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.

  3. Version published to 10.1101/2021.01.11.21249461v2 on medRxiv
  4. ScreenIT

    SciScore for 10.1101/2021.01.11.21249461: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementEthics Approval The study was reviewed and approved by the Warwick Biosciences Research Ethics Committee (BSREC 120/19-20-V1.1) and sponsorship is being provided by University of Warwick (SOC.28/19-20).Randomizationnot detected.Blindingnot detected.Power Analysisnot detected.Sex as a biological variable] Age Group 18 - 24 25 - 34 35 - 44 45 - 54 55 - 64 65 - 74 75 - 84 85 - 99 Sex Female Male Legend: Continuous covariates are presented with their median and interquartile range, whilst categorical covariates are presented with their frequency and within column percentage prevalence.

    Table 2: Resources

    No key resources detected.

    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:

    Strengths and Limitations The strengths of this study are the national cohort of patient-level data with near-perfect capture of admissions,[20] coupled with a rigorous modelling method (eTable 4 & eMethods). Limitations include a lack of physiological data, limiting our ability to adjust for differences in severity upon admission. Moreover, the characterisation of operational strain as a function of surge occupancy likely fails to fully reflect the complexity of using non-specialist staff and other resource allocation issues present when ‘creating’ new ICU beds (see eTable 5 using an alternative definition of occupancy based on baseline capacity; mortality risk given this linear continuous factor was 1.08 (95% PCI: 0.98 – 1.26). Finally, we lack clear 30-day outcome data for discharged and transferred individuals, and thus were forced to model under a naïve assumption that these individuals survived. Implications for Policy Makers and Clinicians In summary, our study highlights the importance of public health interventions (such as expeditious vaccination programmes and non-pharmacological interventions), to control both incidence and prevalence of COVID-19, and therefore actively manage ICU occupancy, as there is evidence of direct harm to patients as a consequence of saturation. This is especially relevant given the identification of a new strain of COVID-19 with a potentially increased risk of transmission,[21] coupled with observations that second wave-related operationa...

    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.

    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.

  5. Version published to 10.1101/2021.01.11.21249461v1 on medRxiv