Developing a Donor-Candidate Risk Prediction System to Optimize Lung Allocation and Transplant Outcomes

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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.