Background

ABO subgroups or weaker variants of A or B are group A or B subjects whose erythrocytes give a weak or negative reaction serologically with anti-A or Anti – B antisera respectively. Occurrence of these subgroups may lead to an ABO discrepancy which often puts transfusion services in a quandary. ABO subgroups which present as ABO discrepancies can be missed if reverse grouping is not performed.

Aim

This study was planned to estimate the prevalence of different subgroups which can present as an ABO discrepancy in Indian population, and provide an insight to transfusion services for identification of subgroups serologically.

Materials and methods

A cross-sectional, analytical study was performed at a tertiary healthcare based blood bank on whole blood donors and patients from January 2017 to July 2018. All suspected type II and Type IV (with Anti-A1) ABO discrepant samples were projected to an algorithmic testing process, to confirm discrepancy and then narrow down to the probable subgroup.

Results

A total of 33 subgroup discrepancies; 26 of A group and 7 of B group were identified out of 73,380 patient and 35,279 donor samples tested for blood grouping. Following the algorithm, the overall prevalence of weak subgroups which can present as an ABO discrepancy was found to be 1 in 3293 or 0.03% in our population by serological testing. Out of the discrepancies caused by subgroups, the prevalence of subgroups of A were 0.0101%, 0.0018%, 0.0009%, 0.0027%, 0.0027% and 0.0018% for A2 with anti-A1, A3, Aend, Ax, Am and Ael respectively while those of B were 0.009%, 0.0009%, 0.0009% and 0.009% for B3, Bx, Bm and Bel respectively.

Conclusion

Algorithmic approach for resolution of ABO discrepancies caused by subgroups helps in identifying the subgroup which is important because these individuals may be mistyped as group O individuals.

中文翻译：

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ABO子群血清学检查的算法方法，在资源约束设置中表现为ABO差异

背景

ABO的ABO亚组或较弱的变异体是A或B组受试者，其红细胞在血清学上分别与抗A或抗B抗血清产生弱或阴性反应。这些亚组的出现可能导致ABO差异，这常常使输血服务陷入困境。如果不执行反向分组，则可能会错过呈现为ABO差异的ABO子组。

目标

计划进行这项研究以估计在印度人口中可能表现为ABO差异的不同亚组的患病率，并为通过输血服务从血清学上鉴定亚组提供见识。

材料和方法

从2017年1月至2018年7月，在基于三级医疗保健的血库中对全血供者和患者进行了横断面分析研究。所有怀疑的II型和IV型（含抗A1）ABO差异样本均被预测为算法测试过程中，确认差异，然后缩小到可能的子组。

结果

共有33个亚组差异；在73380例患者和35279例供体血样中鉴定出A组26例和B组7例。遵循该算法，通过血清学检测发现，可以表现为ABO差异的弱亚群的总体患病率为3293分之一，占我们人口的0.03％。在亚组引起的差异中，A2的患病率分别为：抗A1，A3，Aend，Ax，Ax，Am和Ael的A2为0.0101％，0.0018％，0.0009％，0.0027％，0.0027％和0.0018％。 B3，Bx，Bm和Bel的B分别为0.009％，0.0009％，0.0009％和0.009％。

结论

解决由亚组引起的ABO差异的算法方法有助于识别亚组，这很重要，因为这些个体可能会被误认为O组个体。