The Optimal Timing of Living-Donor Liver Transplantation

Authors:
Oguzhan Alagoz;Lisa M. Maillart;Andrew J. Schaefer;Mark S. Roberts
Affiliations:
-;-;-;-
Venue:
Management Science
Year:
2004

Citing 0
Cited 11

Determining the Acceptance of Cadaveric Livers Using an Implicit Model of the Waiting List

Operations Research
Markov decision models for the optimal maintenance of a production unit with an upstream buffer

Computers and Operations Research
Eliciting Patients' Revealed Preferences: An Inverse Markov Decision Process Approach

Decision Analysis
Maximizing the Efficiency of the U.S. Liver Allocation System Through Region Design

Management Science
Optimal Breast Biopsy Decision-Making Based on Mammographic Features and Demographic Factors

Operations Research
Monotone optimal replacement policies for a Markovian deteriorating system in a controllable environment

Operations Research Letters
A Broader View of Designing the Liver Allocation System

Operations Research
Optimization of Prostate Biopsy Referral Decisions

Manufacturing & Service Operations Management
A biologically based discrete-event simulation model of liver transplantation in the United States for pediatric and adult patients

Proceedings of the Winter Simulation Conference
A comparison of decision-maker perspectives for optimal cholesterol treatment

IBM Journal of Research and Development
Optimal Policies for Reducing Unnecessary Follow-Up Mammography Exams in Breast Cancer Diagnosis

Decision Analysis

Quantified Score

Hi-index	0.01

Visualization

Abstract

Living donors are a significant and increasing source of livers for transplantation, mainly because of the insufficient supply of cadaveric organs. We consider the problem of optimally timing a living-donor liver transplant to maximize the patient's total reward, such as quality-adjusted life expectancy. We formulate a Markov decision process (MDP) model in which the state of the process is described by patient health. We derive structural properties of the MDP model, including a set of intuitive conditions that ensure the existence of a control-limit optimal policy. We use clinical data in our computational experiments, which show that the optimal policy is typically of control-limit type.