Preventing alloimmunization using a new model for matching extensively typed red blood cells

Area of Science:

• Transfusion Medicine
• Immunology
• Bioinformatics

Background:

• Alloimmunization is a significant adverse event in red blood cell (RBC) transfusions due to donor-patient RBC incompatibilities.
• Preventing alloimmunization requires transfusing fully matched RBC units, which is becoming feasible with advances in RBC genotyping.
• The increasing variety of genotyped RBCs necessitates a software-driven approach for optimal patient-product matching.

Purpose of the Study:

• To introduce the MINimize Relative Alloimmunization Risks (MINRAR) model for matching extensively genotyped RBC units to patients.
• To minimize the risk of alloimmunization by incorporating antigen immunogenicity into the matching process.
• To compare the effectiveness of the MINRAR model against a baseline matching strategy (A, B, RhD only).

Main Methods:

• Developed the MINRAR model utilizing antigen immunogenicity to quantify mismatch clinical implications.
• Simulated non-elective transfusions in Caucasian donor and patient populations.
• Compared alloimmunization rates between the MINRAR model and a baseline model matching only A, B, and RhD antigens.

Main Results:

• The MINRAR model reduced expected alloimmunizations by 78.3% compared to the baseline model, even with small RBC inventories.
• A 93.7% reduction in alloimmunization was achieved when using larger RBC inventories with the MINRAR model.
• The model effectively manages the increased complexity of extensively typed RBCs.

Conclusions:

• The MINRAR model provides an effective strategy for matching extensively genotyped RBC units to patients.
• This approach substantially reduces alloimmunization risk without causing additional RBC outdating or shortages.
• Software-driven matching using antigen immunogenicity is crucial for optimizing transfusion safety with advancing genotyping technologies.