Optimal Policies for the Acceptance of Living- and Cadaveric-Donor Livers

by Alagoz, Oguzhan

Abstract (Summary)
Transplantation is the only viable therapy for end-stage liver diseases (ESLD) such as hepatitis B. In the United States, patients with ESLD are placed on a waiting list. When organs become available, they are offered to the patients on this waiting list. This dissertation focuses on the decision problem faced by these patients: which offer to accept and which to refuse? This decision depends on two major components: the patient's current and future health, as well as the current and future prospect for organ offers. A recent analysis of liver transplant data indicates that 60\% of all livers offered to patients for transplantation are refused. This problem is formulated as a discrete-time Markov decision process (MDP). This dissertation analyzes three MDP models, each representing a different situation. The Living-Donor-Only Model considers the problem of optimal timing of living-donor liver transplantation, which is accomplished by removing an entire lobe of a living donor's liver and implanting it into the recipient. The Cadaveric-Donor-Only Model considers the problem of accepting/refusing a cadaveric liver offer when the patient is on the waiting list but has no available living donor. In this model, the effect of the waiting list is incorporated into the decision model implicitly through the probability of being offered a liver. The Living-and-Cadaveric-Donor Model is the most general model. This model combines the first two models, in that the patient is both listed on the waiting list and also has an available living donor. The patient can accept the cadaveric liver offer, decline the cadaveric liver offer and use the living-donor liver, or decline both and continue to wait. This dissertation derives structural properties of all three models, including several sets of conditions that ensure the existence of intuitively structured policies such as control-limit policies. The computational experiments use clinical data, and show that the optimal policy is typically of control-limit type.
Bibliographical Information:

Advisor:Lisa Maillart; Andrew Schaefer; Cindy Bryce; Matthew Bailey; Mainak Mazumdar; Mark Roberts

School:University of Pittsburgh

School Location:USA - Pennsylvania

Source Type:Master's Thesis

Keywords:industrial engineering


Date of Publication:09/13/2004

© 2009 All Rights Reserved.