Development of Machine Learning Composite Measures for Graft Outcome Selection in Pediatric Liver Transplantation

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ABSTRACT Outcomes in pediatric liver transplantation (pLT) are not limited by the donated organ supply. Kids are dying waiting for organs even when these deaths are completely preventable through proper organ selection. Instead of dying, children can live a full and active lifetime with a properly selected liver graft for transplant. Critical to achieving zero waitlist mortality and long-term transplant benefit is the capacity to intervene in a timely manner with a suitable organ and graft type. Decisions to proceed with pLT are complicated, ultimately based on the alignment of transplant team experience, clinical assessment, and organ availability. In an era of organ shortages, the use of technical variant (TV) grafts, including split liver transplantation and living donor liver transplant, has the potential to expand graft choice and enable timelier surgical intervention. Most transplant programs that have prior experience with TV grafts have low patient mortality and excellent transplant outcomes. However, some transplant programs that have limited prior experience with TV grafts have reported many poor outcomes for patients receiving TV transplants. Despite improvements in overall outcomes, national registry data have confirmed significant variation among transplant centers in waitlist mortality, TV graft use, and post-transplant outcomes. Integrally linked to this variation is the intricacy of transplant decision making. Collectively, donor and graft acceptance, prioritization of candidates, and allocation policies depict a complex scenario. More than 100 variables can be considered in a single donor-recipient ‘‘best matching’’ decision, with a risk of subjectivity and mismatch because of human limitations that should not be underestimated. Recognizing these limitations, artificial intelligence classifiers, including machine learning and deep learning, have been recognized for their potential to support or confirm decision making within the field of transplantation. Still, overall data-driven support for optimal graft selection and dissemination of graft decision support is lacking. Opportunities for, and the impact of, discovery are high. This project will result in a composite decision support software tool that uses machine learning to predict and model the best survival for the patient using pre-transplant mortality, post- transplant outcomes, and prior center experience. The decision support tool can be established to supplement current graft selection practices in pLT. We anticipate that modeling based on composite measures will demonstrate equivalent outcomes in recipients of TV grafts. We will develop an algorithm for optimal pediatric graft-type selection that will be commercialized for use through the Starzl Network for Excellence in Pediatric Transplantation and after further multi-center validation it will be available for all pediatric transplant programs. We will accomplish our objective through the following three aims. One, determine the optimal feature space for predictive variables for patient and pLT graft survival. Two, develop survival prediction models, “PSELECT,” for remaining on the waitlist or receiving various graft types. Three, demonstrate the simulated technical feasibility to eliminate the waitlist mortality based on the PSELECT performance on previously held-out data.