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Stochastic modelling and simulation of a kidney transplant waiting list

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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.

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Notes

  1. The main effect of a factor represents the average change in a response variable due to moving the factor from its “lower” to “higher” level while holding all other factors fixed (Law 2014).

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Acknowledgements

The authors would like to thank Daniel Kuba and Magdaléna Krátka, National Transplant Organization, for providing the details of the composition of the Slovak waiting list. Special thanks go to Jozef Hanč, Institute of Physics, P.J. Šafárik University, for his essential help in simulation data preprocessing and cross-validation for the subsequent statistical analysis and efficient bootstrap computations, using Scientific Python.

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Authors and Affiliations

  1. Institute of Mathematics, Faculty of Science, P.J. Šafárik University, Jesenná 5, Košice, Slovakia

    Katarína Cechlárová, Martina Hančová & Diana Plačková

  2. L. Pasteur University Hospital, Trieda SNP 1, Košice, Slovakia

    Tatiana Baltesová

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  1. Katarína Cechlárová
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  2. Martina Hančová
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  3. Diana Plačková
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  4. Tatiana Baltesová
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Correspondence to Katarína Cechlárová.

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The work has been supported by VEGA grant 1/0311/18, APVV grant APVV-17-0568 and COST action CA15210 ENCKEP.

Appendix

Appendix

Here we present supplementary tables of results collected in our simulation study. Tables 4 and 5 are graphically visualized in plots (Figs. 2 and 3). Each cell in Tables 4 and 5 for any particular group of patients represents the numerical summary by the mean and SD for one of 32 design-point simulation experiments.

Table 6 Multiple comparisons between factor levels for median waiting time (WT) and kidney transplantation (KT) probability with respect to the fixed level model computed from three-way ANOVA and bootstrap
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Cechlárová, K., Hančová, M., Plačková, D. et al. Stochastic modelling and simulation of a kidney transplant waiting list. Cent Eur J Oper Res 29, 909–931 (2021). https://doi.org/10.1007/s10100-021-00742-9

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