How should hospitals manage the backlog of patients awaiting surgery following the COVID-19 pandemic? A demand modelling simulation case study for carotid endarterectomy

  1. Guy Martin
  2. Jonathan Clarke
  3. Sheraz Markar
  4. Alexander W Carter
  5. Sam Mason
  6. Sanjay Purkayastha
  7. Ara Darzi
  8. James Kinross
  9. PanSurg Collaborative

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Abstract

Background The COVID-19 pandemic presents unparalleled challenges for the delivery of safe and effective care. In response, many health systems have chosen to restrict access to surgery and reallocate resources; the impact on the provision of surgical services has been profound, with huge numbers of patient now awaiting surgery at the risk of avoidable harm. The challenge now is how do hospitals transition from the current pandemic mode of operation back to business as usual, and ensure that all patients receive equitable, timely and high-quality surgical care during all phases of the public health crisis. Aims and Methods This case study takes carotid endarterectomy as a time-sensitive surgical procedure and simulates 400 compartmental demand modelling scenarios for managing surgical capacity in the UK for two years following the pandemic. Results A total of 7,69 patients will require carotid endarterectomy. In the worst-case scenario, if no additional capacity is provided on resumption of normal service, the waiting list may never be cleared, and no patient will receive surgery within the 2-week target; potentially leading to >1000 avoidable strokes. If surgical capacity is doubled after 1-month of resuming normal service, it will still take more than 6-months to clear the backlog, and 30.8% of patients will not undergo surgery within 2-weeks, with an average wait of 20.3 days for the proceeding 2 years. Conclusions This case study for carotid endarterectomy has shown that every healthcare system is going to have to make difficult decisions for balancing human and capital resources against the needs of patients. It has demonstrated that the timing and size of this effort will critically influence the ability of these systems to return to their baseline and continue to provide the highest quality care for all. The failure to sustainably increase surgical capacity early in the post-COVID-19 period will have significant long-term negative impacts on patients and is likely to result in avoidable harm.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.29.20085183: (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
    8 (Python Software Foundation, www.python.org) using the pandas, numpy, matplotlib and scikit.learn libraries.
    Python
    suggested: (IPython, RRID:SCR_001658)
    matplotlib
    suggested: (MatPlotLib, RRID:SCR_008624)

    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:
    The model presented does have limitations. It assumes all patients remain eligible for intervention and survive to undergo their procedure irrespective of delay. Within the CEA cohort there is likely to be in the region of a 10–20% annual dropout rate due to death or further stroke that makes someone ineligible for surgery, with the greatest risk seen in the first three months[31,32]. This would act to reduce the number of patients requiring intervention, and thus the time to return to baseline, but the impact will be minimal; importantly, the model represents the worst-case scenario for which health systems should plan for, and act on. In addition, this is a generalised model based on national figures. As such, there is a requirement for individual organisations to take into account local factors such as the direct disruption caused by COVID-19, competing demands of surgical and supporting services, and wider infrastructure and staffing resource constraints when designing, modelling and implementing local recovery plans. To further refine the models and identify contextual solutions, discrete event simulations could be employed to incorporate probabilistic payoffs on decisions around additional factors such as mortality rates, the social acceptability of delays and impact of local and national prioritisation and planning decisions. It is also important to assess the financial and human capital implications of seeking to increase surgical capacity. To address the avoidable bu...

    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

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  2. Version published to 10.1101/2020.04.29.20085183v1 on medRxiv