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Shah, Nilay D.

Mayo Clinic

Mayo Clinic

Citations 38

Optimizing Statin Treatment Decisions for Diabetes Patients in the Presence of Uncertain Future Adherence
Mason, Jennifer, England, Darin, Denton, Brian T., Smith, Steven A., Kurt, Murat , Shah, Nilay D., Denton, Brian T.

Medical Decision Making | April 29, 2011 | Volume: 32 | Issue: 1 | Pages: 154-166

10.1177/0272989x11404076 Get It

Background. Statins are an important part of the treatment plan for patients with type 2 diabetes. However, patients who are prescribed statins often take less than the prescribed amount or stop taking the drug altogether. This suboptimal adherence may decrease the benefit of statin initiation. Objective. To estimate the influence of adherence on the optimal timing of statin initiation for patients with type 2 diabetes. Method. The authors use a Markov decision process (MDP) model to optimize the treatment decision for patients with type 2 diabetes. Their model incorporates a Markov model linking adherence to treatment effectiveness and long-term health outcomes. They determine the optimal time of statin initiation that minimizes expected costs and maximizes expected quality-adjusted life years (QALYs). Results. In the long run, approximately 25% of patients remain highly adherent to statins. Based on the MDP model, generic statins lower costs in men and result in a small increase in costs in women relative to no treatment. Patients are able to noticeably increase their expected QALYs by 0.5 to 2 years depending on the level of adherence. Conclusions. Adherence-improving interventions can increase expected QALYs by as much as 1.5 years. Given suboptimal adherence to statins, it is optimal to delay the start time for statins; however, changing the start time alone does not lead to significant changes in costs or QALYs.
Projecting future drug expenditures—2011
Doloresco, Fred, Fominaya, Cory E. , Schumock, Glen T., Vermeulen, Lee C. , Matusiak, Linda M., Hunkler, Robert J., Shah, Nilay D., Hoffman, James M.

American Journal of Health-System Pharmacy | April 7, 2011 | Volume: 68 | Issue: 10 | Pages: 921-932

10.2146/ajhp100712 Get It

Projecting future drug expenditures—2010
Hoffman, James M. , Doloresco, Fred, Vermeulen, Lee C. , Shah, Nilay D., Matusiak, Linda M., Hunkler, Robert J., Schumock, Glen T.

American Journal of Health-System Pharmacy | May 18, 2010 | Volume: 67 | Issue: 11 | Pages: 919-928

10.2146/ajhp100068 Get It

Changes in Glitazone Use Among Office-Based Physicians in the U.S., 2003–2009
Cohen, Andrew J. , Rabbani, Atonu , Shah, Nilay D., Alexander, G. Caleb

Diabetes Care | January 27, 2010 | Volume: 33 | Issue: 4 | Pages: 823-825

10.2337/dc09-1834 Get It

OBJECTIVE Little is known regarding recent changes in glitazone use. RESEARCH DESIGN AND METHODS Interrupted time series analyses of nationally representative office-visit data using IMS Health's National Disease and Therapeutic Index. RESULTS From 2003 through 2005, glitazone use increased steadily. From February 2005 to January 2007, rosiglitazone use decreased by 16% (95% CI −20 to −11) annually; pioglitazone use increased at an annual rate of 14% (9–18). During a period of Food and Drug Administration (FDA) advisories, rosiglitazone use declined sharply from 0.42 million monthly treatment visits (February 2007) to 0.13 million monthly visits (May 2008). Pioglitazone use remained stable, accounting for ∼5.8 million physician visits (77% of all glitazone use) where a treatment was used during the final 12 months of observation. CONCLUSIONS The combined effect of scientific publications, advisories, and media exposure was associated with a substantial decrease in rosiglitazone use. Despite a class-level FDA advisory, pioglitazone use was not similarly affected.
Optimizing the Start Time of Statin Therapy for Patients with Diabetes
Denton, Brian T., Kurt, Murat , Shah, Nilay D., Bryant, Sandra C. , Smith, Steven A., Denton, Brian T.

Medical Decision Making | May 8, 2009 | Volume: 29 | Issue: 3 | Pages: 351-367

10.1177/0272989x08329462 Get It

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.
Projecting future drug expenditures—2009
Hoffman, James M. , Shah, Nilay D., Vermeulen, Lee C. , Doloresco, Fred, Martin, Patrick K., Blake, S.J. Richard A., Matusiak, Linda M., Hunkler, Robert J., Schumock, Glen T.

American Journal of Health-System Pharmacy | January 29, 2009 | Volume: 66 | Issue: 3 | Pages: 237-257

10.2146/ajhp080636 Get It

Projecting future drug expenditures—2008
Hoffman, James M. , Shah, Nilay D., Vermeulen, Lee C. , Doloresco, Fred, Grim, Penny, Hunkler, Robert J., Hontz, Karrie M., Schumock, Glen T.

American Journal of Health-System Pharmacy | January 23, 2008 | Volume: 65 | Issue: 3 | Pages: 234-253

10.2146/ajhp070629 Get It

Projecting future drug expenditures—2007
Hoffman, James M. , Shah, Nilay D., Vermeulen, Lee C. , Schumock, Glen T., Grim, Penny, Hunkler, Robert J., Hontz, Karrie M.

American Journal of Health-System Pharmacy | January 23, 2007 | Volume: 64 | Issue: 3 | Pages: 298-314

10.2146/ajhp060545 Get It