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Personalized ESA doses for anemia management in hemodialysis patients with end‐stage renal disease

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  • James Rogers
  • Edward J. Gallaher
  • David Dingli

Abstract

End‐stage renal disease (ESRD) patients receiving hemodialysis are typically anemic due to a lack of erythropoietin (EPO), a hormone required for red blood cell (RBC) production. EPO replacement therapy is complex, often resulting in oscillating hemoglobin (Hb) levels. EPO dosing protocols do not account for endogenous delays and appear to contribute to, if not cause, Hb variability. The Mayo Clinic Anemia Management System (MCAMS) is based on a physiological simulation of the production and lifespan of RBCs, driven by EPO dosage schedules. Patient‐specific parameters were found such that simulated Hb levels matched historical Hb levels in response to historical EPO doses. The calibrated model was used to identify new dosing regimens that stabilize Hb. MCAMS has been used to recommend individualized EPO dosage regimens to maintain stable Hb levels within a desired target range for more than 600 patients, improving patient outcomes while reducing medication costs by about 40 percent. © 2018 System Dynamics Society

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  • James Rogers & Edward J. Gallaher & David Dingli, 2018. "Personalized ESA doses for anemia management in hemodialysis patients with end‐stage renal disease," System Dynamics Review, System Dynamics Society, vol. 34(1-2), pages 121-153, January.
    • Handle: RePEc:bla:sysdyn:v:34:y:2018:i:1-2:p:121-153
    DOI: 10.1002/sdr.1606
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    References listed on IDEAS

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    Cited by:

    1. Lane, David C. & Rouwette, Etiënne A.J.A., 2023. "Towards a behavioural system dynamics: Exploring its scope and delineating its promise," European Journal of Operational Research, Elsevier, vol. 306(2), pages 777-794.
    2. Orkun İrsoy & Şanser Güz & Naz Beril Akan & Gönenç Yücel, 2020. "Dynamic trade‐offs in granulocyte colony‐stimulating factor (G‐CSF) administration during chemotherapy," System Dynamics Review, System Dynamics Society, vol. 36(4), pages 397-446, October.
    3. John Pastor Ansah & Keith Low Sheng Hng & Salman Ahmad & Cheryl Goh, 2021. "Evaluating the impact of upstream and downstream interventions on chronic kidney disease and dialysis care: a simulation analysis," System Dynamics Review, System Dynamics Society, vol. 37(1), pages 32-58, January.
    4. Özge Karanfil & Niyousha Hosseinichimeh & Jim Duggan, 2020. "System dynamics and bio‐medical modeling," System Dynamics Review, System Dynamics Society, vol. 36(4), pages 389-396, October.
    5. Crystle J. Kelly & James Rogers & Edward J. Gallaher, 2021. "Insights from system dynamics that support experimental research: an exemplar of the NMDA receptor," System Dynamics Review, System Dynamics Society, vol. 37(2-3), pages 197-211, April.
    6. John Sterman, 2018. "System dynamics at sixty: the path forward," System Dynamics Review, System Dynamics Society, vol. 34(1-2), pages 5-47, January.
    7. Zamra Sajid & Morten Andersen & Johnny T. Ottesen, 2020. "System dynamics of cancer in erythropoiesis with multiple EPO feedbacks," System Dynamics Review, System Dynamics Society, vol. 36(4), pages 447-466, October.
    8. James Rogers & Edward J. Gallaher & David Dingli, 2022. "Agency in physiological dynamics," System Dynamics Review, System Dynamics Society, vol. 38(2), pages 169-189, April.

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