Preventing disproportionate mortality in ICU overload situations: Empirical evidence from the first COVID-19 wave in Europe

  1. Paul Buijs
  2. Rodolfo Catena
  3. Matthias Holweg
  4. Taco van der Vaart

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Abstract

Avoiding overloading the healthcare system remains a central issue during the COVID-19 pandemic. The logic of preventing such overload situations is intuitive since the level and quality of critical care is a function of the available capacity to provide it. Where this capacity is no longer available due to a surge in admissions, patient outcomes will invariably deteriorate in the long run – ultimately leading to disproportionate mortality. In this paper, we study the three worst affected regions in Italy, the Netherlands, and Germany during the first COVID-19 wave in the spring of 2020. We report on quantitative analyses that show how mortality rises non-linearly as the proportion of COVID-19 patients in the ICU increases. We identify changes to the patient-staff ratio, increasing exhaustion and infection levels amongst staff, as well as equipment shortages, as likely causes driving this rise in mortality. We explore these findings further with interviews of key stakeholders in the respective healthcare systems. Our results demonstrate that the common approach of managing COVID-19 surges by stretching ICU capacity in hotspot regions may be detrimental to patient outcomes. Instead, we posit that transferring patients proactively out of developing hotspots to less affected regions, well before high ICU workload situations emerge, will improve overall patient outcomes.

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    SciScore for 10.1101/2021.05.03.21255735: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot 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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

    Results from scite Reference Check: We found no unreliable references.

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  2. Version published to 10.1101/2021.05.03.21255735v1 on medRxiv