Project Summary/Abstract
The lung transplant allocation system is not guided by an evidence-based strategy that accounts for the complex
interactions of donor and candidate characteristics missing an opportunity to maximize survival benefit from
utilization of the severely limited organ supply. To overcome this deficit, we will develop a donor-candidate risk
prediction system guided by traditional regression-based statistical techniques and modern machine learning
and artificial learning techniques focused on uncovering the impact of donor characteristics, variation in post-
transplant survival, and donor and candidate interactions. This goal will be accomplished by carrying out the
following three aims. In Aim 1, we will test the hypothesis that incorporating donor characteristics improves
accuracy of prognostic models of recipient post-transplant survival. We will use regression-based and machine
learning approaches and compare the accuracy of the resultant survival models. In Aim 2, we will determine how
donor and candidate characteristics interact to introduce variation in post-transplant survival. Regression-based
and machine learning approaches will be used to identify and evaluate interactions, clustering, and effect
modification by waitlist time, illness severity, and functional status. In Aim 3, we will develop a machine learning/
artificial intelligence algorithm to inform organ allocation and acceptance decisions. Survival trade-offs will be
characterized using machine learning models to build an artificial intelligence allocation algorithm which will be
compared to historical decisions. In summary, the current US lung allocation system does not yet consider the
contribution of donor factors to post-transplant risk predictions which may explain why LAS-derived estimates of
survival benefit are inaccurate. Improved risk predictions would permit optimization of donor and candidate
matching to lay the framework for a system based on compatibility which has the potential to improve donor
utilization, waitlist survival, and post-transplant survival. Use of a staged modeling strategy combining traditional
regression-based approaches and modern machine learning and artificial intelligence methods will encourage
innovative solutions to problems in US lung allocation. This proposal's innovation is further augmented by a
uniquely qualified multi-disciplinary research team with expertise in analysis of complex systems and US lung
allocation policies.