Transfusion · 2025
Genetics by genetic algorithm: Defining an ideal platelet donor population to support patients with HLA-mediated alloimmune refractoriness.
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Blake J T, Rickards N, Mack J, Seftel M, Gupta A
Аннотация
BACKGROUND: Immune factors underlie approximately 20% of patients exhibiting platelet transfusion refractoriness (PTR). PTR can be mitigated by supplying HLA-A and B compatible platelets to these patients. Since 2019, Canadian Blood Services has managed a program to provide compatible platelets to alloimmunized patients. This study evaluates donor pool size and ethnic diversity options to serve Canada's population.
STUDY DESIGN AND METHODS: We adapted simulation-based stem cell matching methods by generating simulated patients and matching them to existing and simulated donors using a Python algorithm. Recruitment scenarios included census-aligned, single-group, and meta-heuristic-optimized distributions to enhance match rates and equity.
RESULTS: More typed donors increase match rates, but rates vary significantly by ethnicity. Non-Black patients achieved 95% coverage with 13,000-18,000 new donors, whereas Black patients required ~55,000 new donors. Single-ethnicity recruitment improved the targeted group's rate but reduced overall match rates and disadvantaged other populations. A Pareto-optimal donor mix-augmenting Black, Hispanic, Asian Pacific Islander, and Native American/First Nations donors while modestly reducing White typing-preserved or improved rates but yielded only modest gains for Black patients. A balanced strategy further prioritizing Black and Hispanic donors with slight reductions in White, Asian, and Native American/First Nations typing achieved improvements for the Black population with minimal losses for others.
DISCUSSION: While Pareto-optimal allocation enhances overall efficiency, targeted trade-offs, focusing on underrepresented groups, are essential to correct persistent disparities and achieve equitable HLA-matched platelet availability.
STUDY DESIGN AND METHODS: We adapted simulation-based stem cell matching methods by generating simulated patients and matching them to existing and simulated donors using a Python algorithm. Recruitment scenarios included census-aligned, single-group, and meta-heuristic-optimized distributions to enhance match rates and equity.
RESULTS: More typed donors increase match rates, but rates vary significantly by ethnicity. Non-Black patients achieved 95% coverage with 13,000-18,000 new donors, whereas Black patients required ~55,000 new donors. Single-ethnicity recruitment improved the targeted group's rate but reduced overall match rates and disadvantaged other populations. A Pareto-optimal donor mix-augmenting Black, Hispanic, Asian Pacific Islander, and Native American/First Nations donors while modestly reducing White typing-preserved or improved rates but yielded only modest gains for Black patients. A balanced strategy further prioritizing Black and Hispanic donors with slight reductions in White, Asian, and Native American/First Nations typing achieved improvements for the Black population with minimal losses for others.
DISCUSSION: While Pareto-optimal allocation enhances overall efficiency, targeted trade-offs, focusing on underrepresented groups, are essential to correct persistent disparities and achieve equitable HLA-matched platelet availability.