Novel allocation strategies can boost kidney exchange programs: a Monte Carlo simulation

Abstract

Kidney exchange programs (KEPs) enhance access to living donor kidney transplantation. Nonetheless, transplant rates in KEP remain low for highly immunized and blood type O patients. In the Netherlands, a novel allocation algorithm is being implemented, allowing ABO-incompatible matching for long waiting patients, next to prioritization and 'low-level' HLA-incompatible matching for selected highly immunized patients. We simulated this novel algorithm along with additional scenarios, by using a retrospective, six-year cohort of Dutch KEP. For each scenario, 30 simulations were repeated with Monte Carlo technique. The novel algorithm increased median KEP transplant rate for incompatible pairs (53% versus 44%, p<0.001) and for difficult-to-match subgroups. HLA-incompatible matching increased transplant rate for selected highly immunized patients significantly, while participation with multiple donors per recipient did not. In additional simulations, including all non-KEP unspecified donors (n=150) for local KEP participation increased transplant rate for incompatible pairs up to 64% (p<0.001). Simulating additional KEP participation by compatible pairs (n=149), on the condition a KEP match should have fewer HLA-mismatches, resulted in 58% being matched in KEP. In conclusion, differential matching algorithms can boost KEP transplant rates, allowing incompatible matching for difficult-to-match subgroups, facilitating participation of unspecified donors, and optimizing the HLA-matching of compatible pairs.