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Blood, 15 September 2008, Vol. 112, No. 6, pp. 2180-2181.
Mining the possibilities behind RBC alloimmunizationUNIVERSITY OF MICHIGAN
In this issue of Blood, Higgins and Sloan use clinical data to develop and test mathematical models of human red-cell alloimmunization.
Given the clinical consequences of RBC alloimmunization, several retrospective studies have attempted to elucidate clinical and patient variables to help identify patients at increased risk for RBC alloimmunization. Sickle cell anemia and thalassemias are 2 patient populations with relatively high rates of RBC alloimmunization due, in part, to racial differences in the expression of specific RBC antigens. Patient age, sex, malignancy, diabetes, transplantation, autoimmune disease, and cumulative RBC transfusion history have also been linked to an increased risk of alloimmunization in univariate analysis.5 In murine models, concurrent inflammation appears to be a necessary cofactor for alloantibody formation.6 In this issue of Blood, Higgins and Sloan mined 15 years of patient records encompassing both adult and pediatric populations. Using this data, the authors identified and tested a novel mathematical model for RBC alloimmunization. Overall, the authors identified new RBC alloantibodies in approximately 4% of the general patient population. Contrary to common wisdom, there was only a weak correlation between the number of RBC alloantibodies formed and the total number of RBCs transfused (or transfusion count). Instead, alloimmunized patients appear to represent a distinct population with an inherent increased susceptibility for RBC sensitization. Furthermore, the risk of making additional alloantibodies in these patients is independent of the number of alloantibodies already preformed. Based on their data, the authors proposed a stochastic or "memoryless" model of RBC alloimmunization, in which additional alloantibodies are independently and randomly acquired. The authors calculated that approximately 13% of the general population are antibody responders. For these patients, there is a 30% alloimmunization risk, where the probability of forming n antibodies = (1-0.3)0.3n. The authors were able to confirm the potential validity of their model using their own data and that of others, showing a geometric probability distribution for RBC alloantibodies in both adult and pediatric populations. The strength of their model is evident when alloimmunized patients were analyzed relative to underlying disease. Regardless of diagnostic subgroup, the same geometric probability distribution was identified—even among diagnostic categories previously identified as a risk factor in earlier studies.5 These results strongly suggest that genetic factor(s), not clinical factors, are primarily responsible for the responder phenotype. This appears to contradict recent murine models, which stress the importance of an inflammatory or danger signal.6 However, as noted by the authors, the 30% alloimmunization risk among responders may reflect the need for an inflammatory signal to stimulate an immune response. Their work should stimulate epidemiologic and genetic studies to define and characterize the responder phenotype. Earlier identification of antibody responders could lead to focused, cost-effective transfusion strategies for avoiding RBC alloimmunization in genetically susceptible patients.
Footnotes
Conflict-of-interest disclosure: The authors declare no competing financial interests.
REFERENCES
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| Copyright © 2008 by American Society of Hematology Online ISSN: 1528-0020 | |||||||||
