Article ID: | iaor20081386 |
Country: | United States |
Volume: | 55 |
Issue: | 1 |
Start Page Number: | 24 |
End Page Number: | 36 |
Publication Date: | Jan 2007 |
Journal: | Operations Research |
Authors: | Schaefer Andrew J., Maillart Lisa M., Alagoz Oguzhan, Roberts Mark S. |
Keywords: | programming: dynamic, markov processes |
The only available therapy for patients with end-stage liver disease is organ transplantation. In the United States, patients with end-stage liver disease are placed on a waiting list and offered livers based on location and waiting time, as well as current and past health. Although there is a shortage of cadaveric livers, 45% of all cadaveric liver offers are declined by the first transplant surgeon and/or patient to whom they are offered. We consider the decision problem faced by these patients: Should an offered organ of a given quality be accepted or declined? We formulate a Markov decision process model in which the state of the process is described by patient state and organ quality. We use a detailed model of patient health to estimate the parameters of our decision model and implicitly consider the effects of the waiting list through our patient-state-dependent definition of the organ arrival probabilities. We derive structural properties of the model, including a set of intuitive conditions that ensure the existence of control-limit optimal policies. We use clinical data in our computational experiments, which confirm that the optimal policy is typically of control-limit type.