Choosing Among Living-Donor and Cadaveric Livers

Abstract

The only therapy for a patient with end-stage liver disease (ESLD) is liver transplantation, which is performed by using either a cadaveric liver from a deceased donor or a portion of a living-donor's liver. This study addresses the following decision problem for an ESLD patient with an available living donor. Should she have a transplantation now or wait? If she decides to have the transplantation now, should she use her living-donor liver or a cadaveric liver for transplantation? We formulate this problem as a discrete-time, infinite-horizon Markov decision process model and solve it using clinical data. Because living donors are typically related to the recipient, we incorporate a disutility associated with using the living-donor liver as opposed to using a cadaveric liver. We perform a structural analysis of the model, including a set of intuitive conditions that ensure the existence of structured policies such as an at-most-three-region (AM3R) optimal policy. Our computational experiments confirm that the optimal policy is typically of AM3R type.medical decision making, Markov decision processes, control-limit policy, health-care applications, organ transplantation, service operations, optimal stopping, dynamic programming

A time-dependent best choice problem with costs and random lifetime in organ transplants

“…In her model activities (patients) and resources (kidneys) have different values, hence she assumed that the probability that a transplant is successful is the product of the kidney value and the patient value. Alagoz et al [1], [2] also considered a medical application of an optimal stopping problem. They were interested in finding an optimal policy for patients with end-stage liver disease.…”

“…They formulated a Markov decision process model in which the state of the process is described by the patient's condition and the organ's quality. The model with a penalty function depending on the patient's health and involving a living donor was introduced by Alagoz et al [1]. This function is in units of the total expected discounted reward.…”

“…This decision is based on the personal experience of the physician and depends on medical judgment regarding the suitability of the organ being offered for specific candidates. In the litera-ture, some models are presented helping physicians to make these decisions (for example see Alagoz et al [1] or David and Yechiali [11]). However, these works do not take into consideration the patient's aversion to risk.…”

Optimal control of dosage decisions in controlled ovarian hyperstimulation

“…For example, Alagoz et al (2004) uses a Markov decision process (MDP) model to decide on an optimal timing of living-donor liver transplantation. They extend their work to include cadaveric livers (Alagoz et al 2007b(Alagoz et al , 2007a.…”

Integrated testing strategies can be optimal for chemical risk classification

“…We discuss in detail a variety of different applications (Fakih (2006), Kurt et al 130 (2011), Shechter et al (2008), Alagoz et al (2007), Alterovitz et al (2008), Nunes et al (2017) and Sloan (2007)) . As mentioned in the above, the main novelty of this paper, logically consistent aggregation of evidence from different measurements in a non-contradictory way and without a need to resort to empirical estimates still stands.…”