Lung Transplant Allocation Strategies

• Activity Number: 122
• Type: Topic Contributed
• Date/Time: Monday, August 10, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Health Policy Statistics Section
• Author(s): Jingjing Zou* and David Lederer and Daniel Rabinowitz
• Companies: Columbia University and Columbia University and Columbia University
• Keywords: Lung transplantation ; Allocation strategies ; Hamilton-Jacobi-Bellman equations ; Markov Decision Models

Abstract

Currently in the United States, lungs are allocated to transplant candidates based on the Lung Allocation Score (LAS). The LAS is an empirically derived score aimed at increasing total life span pre- and post-transplantation, for patients on lung transplant waiting lists. The goal here is to study optimal allocation strategy development in the context of lung transplantation. Via the Hamilton-Jacobi-Bellman equations, upper bounds as a function of organ arrival rate for the long-term expected average total life are derived, and corresponding to each upper bound is a set of (state, time) pairs at which patients would be optimally transplanted. As organ arrival rate increases, the set expands monotonically, and ranking members of the waiting list according to the arrival rate at which they enter the set provides an allocation strategy that produces long-term expected average total life close to the upper bound. Simulation studies are conducted with model parameters estimated from national lung transplantation data from United Network for Organ Sharing (UNOS). Results suggest that compared to the LAS, the proposed allocation strategy could provide a 7% increase in average total life.