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Dynamic Decision Models for Managing the Major Complications of Diabetes

Kurt, Murat (2013) Dynamic Decision Models for Managing the Major Complications of Diabetes. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Diabetes is the sixth-leading cause of death and a major cause of cardiovascular and renal diseases in the U.S.
In this dissertation, we consider the major complications of diabetes and develop dynamic decision models for two important timing problems:
Transplantation in prearranged paired kidney exchanges (PKEs) and statin initiation.

Transplantation is the most viable renal replacement therapy for end-stage renal disease (ESRD) patients, but there is a severe disparity between the demand and supply of kidneys for transplantation. PKE, a cross-exchange of kidneys between incompatible patient-donor pairs, overcomes many difficulties in matching patients with incompatible donors. In a typical PKE, transplantation surgeries take place simultaneously so that no donor may renege after her intended recipient receives the kidney. We consider two autonomous patients with probabilistically evolving health statuses in a PKE, and model their transplant timing decisions as a discrete-time non-zero-sum stochastic game. We explore necessary and sufficient conditions for patients' decisions to form a stationary-perfect equilibrium, and formulate a mixed-integer linear programming (MIP) representation of equilibrium constraints to characterize a socially optimal stationary-perfect equilibrium. We calibrate our model using large scale clinical data. We quantify the social welfare loss due to patient autonomy and demonstrate that the objective of maximizing the number of transplants may be undesirable.

Patients with Type 2 diabetes have higher risk of
heart attack and stroke, and if not treated these risks are confounded by lipid abnormalities. Statins
can be used to treat such abnormalities, but their use may lead to adverse outcomes.
We consider the question of when to initiate statin therapy for patients with Type 2 diabetes. We
formulate a Markov decision process (MDP) to maximize
the patient's quality-adjusted life years (QALYs) prior to the first heart attack or stroke. We derive sufficient conditions for the optimality of control-limit
policies with respect to patient's lipid-ratio (LR) levels and age and parameterize our model using clinical data. We compute the
optimal treatment policies and illustrate the importance of individualized treatment factors
by comparing their performance to those of the guidelines in use
in the U.S.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Kurt, Muratmuk7@pitt.eduMUK7
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairSchaefer, Andrew J.schaefer@ie.pitt.eduSCHAEFER
Committee MemberUnver, M.
Committee MemberRoberts, Mark Smroberts@pitt.eduMROBERTS
Committee MemberMaillart, Lisa Mmaillart@pitt.eduMAILLART
Committee MemberSaure, Denis Rdsaure@pitt.eduDSAURE
Committee MemberAkan,
Date: 1 February 2013
Date Type: Publication
Defense Date: 9 November 2012
Approval Date: 1 February 2013
Submission Date: 19 November 2012
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 123
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Industrial Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Operations research, game theory, Markov decision processes, integer programming, diabetes, paired kidney exchange, coronary heart disease, stroke, medical decision making
Date Deposited: 01 Feb 2013 13:19
Last Modified: 15 Nov 2016 14:07


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