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Optimizing the Start Time of Statin Therapy for Patients with Diabetes

Author

Listed:
  • Brian T. Denton

    (North Carolina State University, Edward P. Fitts Department of Industrial & Systems Engineering, Raleigh, North Carolina, bdenton@ ncsu.edu)

  • Murat Kurt

    (University of Pittsburgh, Department of Industrial Engineering, Pittsburgh, Pennsylvania)

  • Nilay D. Shah

    (Mayo Clinic College of Medicine, Division of Health Care Policy & Research, Rochester, Minnesota)

  • Sandra C. Bryant

    (Mayo Clinic College of Medicine, Division of Biostatistics and Biomedical Informatics, Rochester, Minnesota)

  • Steven A. Smith

    (Mayo Clinic College of Medicine, Division of Health Care Policy & Research, Rochester, Minnesota, Mayo Clinic College of Medicine, Division of Endocrinology, Diabetes, Nutrition, & Metabolism, Rochester, Minnesota)

Abstract

Background . Clinicians often use validated risk models to guide treatment decisions for cardiovascular risk reduction. The most common risk models for predicting cardiovascular risk are the UKPDS, Framingham, and Archimedes models. In this article, the authors propose a model to optimize the selection of patients for statin therapy of hypercholesterolemia, for patients with type 2 diabetes, using each of the risk models. For each model, they evaluate the role of age, gender, and metabolic state on the optimal start time for statins. Method . Using clinical data from the Mayo Clinic electronic medical record, the authors construct a Markov decision process model with health states composed of cardiovascular events and metabolic factors such as total cholesterol and high-density lipoproteins. They use it to evaluate the optimal start time of statin treatment for different combinations of cardiovascular risk models and patient attributes. Results . The authors find that treatment decisions depend on the cardiovascular risk model used and the age, gender, and metabolic state of the patient. Using the UKPDS risk model to estimate the probability of coronary heart disease and stroke events, they find that all white male patients should eventually start statin therapy; however, using Framingham and Archimedes models in place of UKPDS, they find that for male patients at lower risk, it is never optimal to initiate statins. For white female patients, the authors also find some patients for whom it is never optimal to initiate statins. Assuming that age 40 is the earliest possible start time, the authors find that the earliest optimal start times for UKPDS, Framingham, and Archimedes are 50, 46, and 40, respectively, for women. For men, the earliest optimal start times are 40, 40, and 40, respectively. Conclusions . In addition to age, gender, and metabolic state, the choice of cardiovascular risk model influences the apparent optimal time for starting statins in patients with diabetes.

Suggested Citation

  • Brian T. Denton & Murat Kurt & Nilay D. Shah & Sandra C. Bryant & Steven A. Smith, 2009. "Optimizing the Start Time of Statin Therapy for Patients with Diabetes," Medical Decision Making, , vol. 29(3), pages 351-367, May.
  • Handle: RePEc:sae:medema:v:29:y:2009:i:3:p:351-367
    DOI: 10.1177/0272989X08329462
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    References listed on IDEAS

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    1. Philip Clarke & Alastair Gray & Rury Holman, 2002. "Estimating Utility Values for Health States of Type 2 Diabetic Patients Using the EQ-5D (UKPDS 62)," Medical Decision Making, , vol. 22(4), pages 340-349, August.
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    Cited by:

    1. Nisha Nataraj & Julie Simmons Ivy & Fay Cobb Payton & Joseph Norman, 2018. "Diabetes and the hospitalized patient," Health Care Management Science, Springer, vol. 21(4), pages 534-553, December.
    2. 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.
    3. Mabel C. Chou & Mahmut Parlar & Yun Zhou, 2017. "Optimal Timing to Initiate Medical Treatment for a Disease Evolving as a Semi-Markov Process," Journal of Optimization Theory and Applications, Springer, vol. 175(1), pages 194-217, October.
    4. 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.
    5. Jingyu Zhang & Brian T. Denton & Hari Balasubramanian & Nilay D. Shah & Brant A. Inman, 2012. "Optimization of Prostate Biopsy Referral Decisions," Manufacturing & Service Operations Management, INFORMS, vol. 14(4), pages 529-547, October.
    6. 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.
    7. Dan Andrei Iancu & Nikolaos Trichakis & Do Young Yoon, 2021. "Monitoring with Limited Information," Management Science, INFORMS, vol. 67(7), pages 4233-4251, July.
    8. Jonathan E. Helm & Mariel S. Lavieri & Mark P. Van Oyen & Joshua D. Stein & David C. Musch, 2015. "Dynamic Forecasting and Control Algorithms of Glaucoma Progression for Clinician Decision Support," Operations Research, INFORMS, vol. 63(5), pages 979-999, October.
    9. Nazila Bazrafshan & M. M. Lotfi, 2020. "A finite-horizon Markov decision process model for cancer chemotherapy treatment planning: an application to sequential treatment decision making in clinical trials," Annals of Operations Research, Springer, vol. 295(1), pages 483-502, December.
    10. Kotas, Jakob & Ghate, Archis, 2018. "Bayesian learning of dose–response parameters from a cohort under response-guided dosing," European Journal of Operational Research, Elsevier, vol. 265(1), pages 328-343.
    11. 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.
    12. 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.
    13. Wesley J. Marrero & Mariel S. Lavieri & Jeremy B. Sussman, 2021. "Optimal cholesterol treatment plans and genetic testing strategies for cardiovascular diseases," Health Care Management Science, Springer, vol. 24(1), pages 1-25, March.

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