Switch from urgency- to benefit-based organ allocation


The field of transplantation medicine is dealing with ever-increasing shortage of donor organs, leading to a waiting list for transplantation. Currently, the sickest patients are prioritized for transplantation. Although this system prevents death on the waiting list, it merely shifts mortality from pre- to post-transplantation. Prioritizing the sickest seems intuitively justified, but organs are denied to recipients that would have more benefit.
To resolve this ineffective use of scarce donor organs, we propose benefit-based organ allocation, considering both mortality on the waiting list and after transplantation. To achieve benefit-based allocation, modern machine learning algorithms will be used to develop models predicting outcome of patients on the waiting list, as well as after liver transplantation. These models can be used to calculate, for every donor organ that becomes available, which individual recipient benefits most of the available organ. A Monte Carlo simulation model will be developed that simulates influx of patients on the waiting list and incoming donor livers, as well as outcome on the waiting list and after transplantation using the prediction models. It will be used to quantify the effect of different allocation schemes, including urgency-based and benefit-based allocation, on life-years for the whole group of liver patients.





Prof. dr. H. Putter

Verbonden aan

Universiteit Leiden, Leids Universitair Medisch Centrum, Divisie 5


01/09/2019 tot 30/06/2020