Optimal Policies for the Acceptance of Living- and Cadaveric-Donor Livers
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
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.
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
Date of Publication:09/13/2004