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Optimizing the simultaneous management of blood pressure and cholesterol for type 2 diabetes patients

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  • Mason, J.E.
  • Denton, B.T.
  • Shah, N.D.
  • Smith, S.A.

Abstract

We present a Markov decision process (MDP) model to determine the optimal timing of blood pressure and cholesterol medications. We study the use of our model for a high-risk population of patients with type 2 diabetes; however, the model and methods we present are applicable to the general population. We compare the optimal policies based on our MDP to published guidelines for initiation of blood pressure and cholesterol medications over the course of a patient’s lifetime. We also present a bicriteria analysis that illustrates the trade off between quality-adjusted life years and costs of treatment.

Suggested Citation

  • Mason, J.E. & Denton, B.T. & Shah, N.D. & Smith, S.A., 2014. "Optimizing the simultaneous management of blood pressure and cholesterol for type 2 diabetes patients," European Journal of Operational Research, Elsevier, vol. 233(3), pages 727-738.
    • Handle: RePEc:eee:ejores:v:233:y:2014:i:3:p:727-738
    DOI: 10.1016/j.ejor.2013.09.018
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    Cited by:

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    4. Boloori, Alireza & Saghafian, Soroush & Chakkera, Harini A. A. & Cook, Curtiss B., 2017. "Data-Driven Management of Post-transplant Medications: An APOMDP Approach," Working Paper Series rwp17-036, Harvard University, John F. Kennedy School of Government.
    5. Daniel R. Jiang & Warren B. Powell, 2015. "An Approximate Dynamic Programming Algorithm for Monotone Value Functions," Operations Research, INFORMS, vol. 63(6), pages 1489-1511, December.
    6. Keshtkaran, Mahsa & Churilov, Leonid & Hearne, John & Abbasi, Babak & Meretoja, Atte, 2016. "Validation of a decision support model for investigation and improvement in stroke thrombolysis," European Journal of Operational Research, Elsevier, vol. 253(1), pages 154-169.
    7. Lauren E. Cipriano & Thomas A. Weber, 2018. "Population-level intervention and information collection in dynamic healthcare policy," Health Care Management Science, Springer, vol. 21(4), pages 604-631, December.
    8. Daniel F. Otero-Leon & Mariel S. Lavieri & Brian T. Denton & Jeremy Sussman & Rodney A. Hayward, 2023. "Monitoring policy in the context of preventive treatment of cardiovascular disease," Health Care Management Science, Springer, vol. 26(1), pages 93-116, March.
    9. Yu, Haiyan & Yang, Ching-Chi & Yu, Ping, 2023. "Constrained optimization for stratified treatment rules in reducing hospital readmission rates of diabetic patients," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1355-1364.
    10. Anthony Bonifonte & Turgay Ayer & Benjamin Haaland, 2022. "An Analytics Approach to Guide Randomized Controlled Trials in Hypertension Management," Management Science, INFORMS, vol. 68(9), pages 6634-6647, September.
    11. Shafaei Bajestani, Narges & Vahidian Kamyad, Ali & Nasli Esfahani, Ensieh & Zare, Assef, 2018. "Prediction of retinopathy in diabetic patients using type-2 fuzzy regression model," European Journal of Operational Research, Elsevier, vol. 264(3), pages 859-869.
    12. Alireza Boloori & Soroush Saghafian & Harini A. Chakkera & Curtiss B. Cook, 2020. "Data-Driven Management of Post-transplant Medications: An Ambiguous Partially Observable Markov Decision Process Approach," Manufacturing & Service Operations Management, INFORMS, vol. 22(5), pages 1066-1087, September.
    13. Diana M. Negoescu & Kostas Bimpikis & Margaret L. Brandeau & Dan A. Iancu, 2018. "Dynamic Learning of Patient Response Types: An Application to Treating Chronic Diseases," Management Science, INFORMS, vol. 64(8), pages 3469-3488, August.
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