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

Abstract

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 patientstate-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.

Fairness, Efficiency, and Flexibility in Organ Allocation for Kidney Transplantation

“…David and Yechiali (1985) model the candidate's problem as an optimal stopping problem. Similar acceptance policies are developed by Ahn and Hornberger (1996), Howard (2002), Alagoz (2004) and Alagoz et al (2007). The present paper will test policies on a simulator developed by SRTR for OPTN; this simulator assumes a specific, exogenous acceptance model for patients built from historical data.…”

Might randomization in queue discipline be useful when waiting cost is a concave function of waiting time?

“…(Cf. also Alagoz et al, 2007) Here, the issue is whether conventional measures of efficiency and fairness capture all relevant considerations. Avi-Itzhak and axiomatic proof that variance in waiting time is a good measure of (un)fairness applies only to fairness with respect to the queue discipline.…”

“…The following section examines a slightly less simplistic depiction of a transplant queue for which some randomness might be appealing. That example is only slightly less simplistic since serious analyses of organ assignment and transplant queues are quite elaborate (e.g., Zenios, 2002;Zenios, 2004, 2006;Alagoz et al, 2004Alagoz et al, , 2007, whereas this note is meant to be a think piece that puts forward a contrarian idea, not a detailed analysis of any particular application. The final substantive section advances a similarly simplistic example motivated by an entirely different domain, generic symbol processing or knowledge work, to indicate that the contrarian insight might be relevant beyond just transplant queues.…”

A Modified Policy Iteration Algorithm for Discounted Reward Markov Decision Processes

“…To mention some of them, MDP models have been applied to Inventory Control [4], Queuing Systems [10], Maintenance Management [13], Health Care Management [2] and Transportation Systems [9]. Considering a discrete time MDP with finite state and action spaces under discounted reward optimality criterion.…”