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A dynamic mathematical model of red blood cell clinical demand to assess the impact of prolonged blood shortages and transfusion restriction policies

Authors

  • Zoe K. McQuilten,

    Corresponding author
    1. Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
    2. Australian Red Cross Blood Service, Melbourne, Victoria, Australia
    • Address reprint requests to: Zoe K. McQuilten, Level 6, 99 Commercial Road, Melbourne, Victoria 3004, Australia; e-mail: zoe.mcquilten@monash.edu.

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  • Geoff Mercer,

    1. National Centre for Epidemiology and Population Health, Research School of Population Health, Australian National University, Canberra, Australian Capital Territory, Australia
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  • Louise Phillips,

    1. Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
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  • Thiansiri Luangwilai,

    1. National Centre for Epidemiology and Population Health, Research School of Population Health, Australian National University, Canberra, Australian Capital Territory, Australia
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  • Richard Brown,

    1. Australian Red Cross Blood Service, Melbourne, Victoria, Australia
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  • Ieva Ozolins,

    1. Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
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  • Allen C. Cheng,

    1. Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
    2. Department of Infectious Diseases, Alfred Health, Melbourne, Victoria, Australia
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  • Erica M. Wood

    1. Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
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  • ZM is the recipient of an Australian National Health and Medical Research Council (NHMRC) Postgraduate scholarship (APP1017942). This project was partly funded by the Australian Red Cross Blood Service. The Australian government fully funds the Australian Red Cross Blood Service for the provision of blood products and services to the Australian community.

Abstract

Background

Estimating change in clinical demand for red blood cells (RBCs) from a disaster, as well as triaging introduced in response, is essential to plan effectively for a major blood shortage. We aimed to develop a RBC demand model to assess the impact of restriction policies on RBC use and patient outcomes.

Study Design and Methods

A compartmental dynamic model was developed in which patients require RBCs acutely (within 1 hr), urgently (24 hr), semiurgently (1-7 days), or nonurgently; outcomes included death or remaining at or transitioning to more or less urgent categories. A mathematical model was developed with transitions governed by differential equations and calibrated to a baseline scenario of adequate blood supply (using population-based hospital data sets, registries, and RBC issues). Distribution into urgency categories was based on a prospective study of 5132 randomly selected RBC units. Scenarios when the blood supply is limited compared to baseline were investigated. Transition rates between urgency categories under these scenarios were established by clinician survey.

Results

In the baseline 21-day scenario, patients requiring the most RBCs were other surgery (2162, 22%), medical anemia (1916, 12%), malignant hematology (1092, 16%), and gastrointestinal hemorrhage (1115, 8%). A policy of withholding RBCs for all nonurgent indications results in an estimated reduction of only 1007 (11.2%) RBC units and, if extended to semiurgent, a reduction of 2567 (28.5%) RBC units.

Conclusions

Based on this model, restrictions that withhold transfusion from nonurgent patients have minimal impact on RBC demand and may not be sufficient to address changed demand and/or decreased supply during a prolonged disaster.

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