Improving Organ Acceptance in Liver Transplant through Data Drive Models
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Liver transplantation (LT) is a lifesaving intervention for patients with decompensated cirrhosis and liver cancer. Decisions on organ transplantation acceptance occur at the transplant center level, with providers at individual centers weighing the risks and benefits of a particular available donor for a potential recipient on the waitlist.
We have shown there is significant heterogeneity in organ acceptance patterns across centers in the US which exacerbates geographic disparities in organ availability. We lack data-driven decision support for organ acceptance which contributes significantly to the variability in utilization.
In this proposal, we will design an implementable computational tool that considers the requirements and preferences of transplant professionals and patients. In Aim 1a, we will use existing data from the United Network of Organ Sharing to develop risk models for patients waitlisted for liver transplantation accounting for the likelihood of organ offers and graft survival after transplantation.
In Aim 1b, we will generate a data-driven optimization model for liver acceptance that incorporates the likelihood of waitlist survival and successful transplantation outcomes.
In Aim 2, we will design both provider and patient facing tools for organ acceptance with input of key stakeholders, including transplant professionals and patients. Our tool will provide transplant professionals and transplant candidates with the expected benefits and risks of liver offer decision making and visualizations of the candidates’ expected survival and outcomes with and without transplants for a particular organ offer, with the goal of improving the consistency of decision making about organ acceptance.
In Aim 3, we will perform an organ acceptance simulation with transplant professionals and patients with and without the tool to determine: (1) the effectiveness of the tool in the heterogeneity in organ acceptance offers; (2) the impact of decisions on anticipated life expectancy; and (3) how the tool impacts the confidence in those decisions.