Abstract:
\nEnd-stage renal disease (ESRD) is the ninth-leading cause of death in the\u00a0U.S. Transplantation is the most viable therapy for ESRD patients, but
\nthere is a severe disparity between the demand for kidneys for\u00a0transplantation and the supply. This shortage is further complicated by\u00a0incompatibilities in blood-type and antigen matching between patient-donor\u00a0pairs. Paired kidney exchange (PKE), a cross-exchange of kidneys among\u00a0incompatible patient-donor pairs, overcomes many difficulties in matching\u00a0patients with incompatible donors. PKEs have grown rapidly over the last\u00a0two decades. In a PKE, transplantation surgeries take place\u00a0simultaneously, so that no donor may renege after his\/her intended\u00a0recipient receives the organ. Although others have modeled PKEs, we are\u00a0the first to consider patient autonomy and the timing of the exchange. As\u00a0current PKE practice aims to maximize only the number of transplants, the
\nquestion of determining a more accurate value of a match remains unclear. We consider a cyclic PKE with an arbitrary number of patients and\u00a0construct life-expectancy-based edge weights under patient autonomy.\u00a0Because the patients’ health statuses are dynamic, and transplantation\u00a0surgeries require compatibility between the patients\u2019 willingnesses to\u00a0exchange, we model the patients\u2019 transplant timing decisions as a\u00a0stochastic game in which each patient aims to maximize his\/her life\u00a0expectancy. We explore necessary and sufficient conditions for patients’\u00a0decisions to be a Nash equilibrium, and formulate a mixed-integer linear\u00a0programming representation of equilibrium constraints, which provides a\u00a0characterization of the socially optimal equilibria. We empirically
\nconfirm that randomized strategies do not yield a social welfare gain over\u00a0pure strategies. We also quantify the social welfare loss due to patient\u00a0autonomy and demonstrate that maximizing the number of transplants may be\u00a0undesirable. Our results highlight the importance of the timing of an\u00a0exchange and disease severity on matching patient-donor pairs.\u00a0This is a joint work with Andrew Schaefer from Rice University, Utku Unver\u00a0from Boston College and Mark Roberts from University of Pittsburgh.<\/p>\n
Bio: Murat Kurt is a Scientist of Outcomes Research in Predictive and\u00a0Economic Modeling at Merck Research Labs. He earned his PhD in Industrial\u00a0Engineering at the University of Pittsburgh and served as a faculty in\u00a0Industrial and Systems Engineering at the University at Buffalo prior to\u00a0joining Merck. His research interests involve the implementation of Markov\u00a0decision processes and mathematical programming on medical decision\u00a0making, scheduling and service operations problems. His research in\u00a0applied health sciences span varying contexts from treatment planning for\u00a0Type 2 diabetes and colorectal cancer patients to the timing of kidney\u00a0exchanges for end-stage renal disease patients. His research on healthcare\u00a0applications has been recognized by the IIE’s Pritsker Doctoral
\nDissertation Prize and INFORMS Service Science Section’s Best Paper Awards\u00a0as well as Seth Bonder Scholarship in Applied Health Sciences.<\/p>\n","protected":false},"excerpt":{"rendered":"
Abstract: End-stage renal disease (ESRD) is the ninth-leading cause of death in the\u00a0U.S. Transplantation is the most viable therapy for ESRD patients, but there is a severe disparity between the demand for kidneys for\u00a0transplantation and the supply. This shortage is further complicated by\u00a0incompatibilities in blood-type and antigen matching between patient-donor\u00a0pairs. [\u2026] <\/p>\n