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The Value of Triage during Periods of Intense COVID-19 Demand: Simulation Modeling Study

Author

Listed:
  • Richard M. Wood

    (Modelling and Analytics, UK National Health Service (BNSSG CCG), UK
    Centre for Healthcare Innovation and Improvement (CHI2), School of Management; University of Bath, Bath, UK
    Health Data Research UK South West Better Care Partnership, UK)

  • Adrian C. Pratt

    (Modelling and Analytics, UK National Health Service (BNSSG CCG), UK)

  • Charlie Kenward

    (Clinical Effectiveness, UK National Health Service (BNSSG CCG), UK)

  • Christopher J. McWilliams

    (Health Data Research UK South West Better Care Partnership, UK
    Department of Engineering Mathematics, University of Bristol, UK)

  • Ross D. Booton

    (Bristol Veterinary School, University of Bristol, UK)

  • Matthew J. Thomas

    (Intensive Care Medicine, Bristol Medical School, University of Bristol, UK)

  • Christopher P. Bourdeaux

    (Health Data Research UK South West Better Care Partnership, UK
    Intensive Care Medicine, Bristol Medical School, University of Bristol, UK)

  • Christos Vasilakis

    (Centre for Healthcare Innovation and Improvement (CHI2), School of Management; University of Bath, Bath, UK
    Health Data Research UK South West Better Care Partnership, UK)

Abstract

Background During the COVID-19 pandemic, many intensive care units have been overwhelmed by unprecedented levels of demand. Notwithstanding ethical considerations, the prioritization of patients with better prognoses may support a more effective use of available capacity in maximizing aggregate outcomes. This has prompted various proposed triage criteria, although in none of these has an objective assessment been made in terms of impact on number of lives and life-years saved. Design An open-source computer simulation model was constructed for approximating the intensive care admission and discharge dynamics under triage. The model was calibrated from observational data for 9505 patient admissions to UK intensive care units. To explore triage efficacy under various conditions, scenario analysis was performed using a range of demand trajectories corresponding to differing nonpharmaceutical interventions. Results Triaging patients at the point of expressed demand had negligible effect on deaths but reduces life-years lost by up to 8.4% (95% confidence interval: 2.6% to 18.7%). Greater value may be possible through “reverse triage†, that is, promptly discharging any patient not meeting the criteria if admission cannot otherwise be guaranteed for one who does. Under such policy, life-years lost can be reduced by 11.7% (2.8% to 25.8%), which represents 23.0% (5.4% to 50.1%) of what is operationally feasible with no limit on capacity and in the absence of improved clinical treatments. Conclusions The effect of simple triage is limited by a tradeoff between reduced deaths within intensive care (due to improved outcomes) and increased deaths resulting from declined admission (due to lower throughput given the longer lengths of stay of survivors). Improvements can be found through reverse triage, at the expense of potentially complex ethical considerations.

Suggested Citation

  • Richard M. Wood & Adrian C. Pratt & Charlie Kenward & Christopher J. McWilliams & Ross D. Booton & Matthew J. Thomas & Christopher P. Bourdeaux & Christos Vasilakis, 2021. "The Value of Triage during Periods of Intense COVID-19 Demand: Simulation Modeling Study," Medical Decision Making, , vol. 41(4), pages 393-407, May.
    • Handle: RePEc:sae:medema:v:41:y:2021:i:4:p:393-407
    DOI: 10.1177/0272989X21994035
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    References listed on IDEAS

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    1. Richard M Wood & Christopher J McWilliams & Matthew J Thomas & Christopher P Bourdeaux & Christos Vasilakis, 2020. "COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care," Health Care Management Science, Springer, vol. 23(3), pages 315-324, September.
    2. Amin Mahmoudian-Dehkordi & Somayeh Sadat, 2017. "Sustaining critical care: using evidence-based simulation to evaluate ICU management policies," Health Care Management Science, Springer, vol. 20(4), pages 532-547, December.
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    Cited by:

    1. Christina C. Bartenschlager & Milena Grieger & Johanna Erber & Tobias Neidel & Stefan Borgmann & Jörg J. Vehreschild & Markus Steinbrecher & Siegbert Rieg & Melanie Stecher & Christine Dhillon & Maria, 2023. "Covid-19 triage in the emergency department 2.0: how analytics and AI transform a human-made algorithm for the prediction of clinical pathways," Health Care Management Science, Springer, vol. 26(3), pages 412-429, September.

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