Stochastic modelling and simulation of a kidney transplant waiting list

Abstract

We created a dynamic stochastic model to evaluate the performance of a kidney transplantation system. Our model is applicable in the context of a small country where the legislation requires that a kidney from a deceased donor should be used whenever available. Using a systematic design of simulation experiments, we performed a complex simulation study based on real medical data to explore the impact of factors representing different rates of deceased kidneys harvesting, the proportion of patients with a willing living donor and different allocation policies. On the basis of careful statistical analysis carried out by two different statistical methodologies, ANOVA and bootstrap, we draw some important conclusions about the effects of these factors and recommendations for the medical community. The results of the study clearly demonstrate that in addition to increasing the numbers of kidney donors, deceased as well as living, the introduction of a kidney exchange program leads to further expansion of the numbers of donations and to shortening of waiting time for transplantation. Moreover, we observed that the largest and most counter-intuitive effect on waiting time and transplantation probability was obtained by replacing the currently implemented first-come-first-transplanted allocation policy to a policy that prioritizes the most vulnerable group of patients. This change has led to shortening the waiting time of these patients by enormous 28 months on average while leaving the waiting time of other patients practically the same.