Department of Pathology, Stanford University School of Medicine, Stanford, CA, 94305, United States.
Stanford Blood Center, Stanford Medicine, Stanford, CA 94305, United States.
Transfus Apher Sci. 2021 Feb;60(1):102970. doi: 10.1016/j.transci.2020.102970. Epub 2020 Oct 20.
With more hospitals using low-titer group O whole blood in trauma resuscitation, having an efficient screening method for low-titer donors is critical. Our blood center uses an automated screen for high-titer isohemagglutinins in our platelet donations while collecting detailed donor demographic information. Using this data, we can identify key demographics often associated with titer status, thereby helping develop a donor-triaging method for titering.
Titer results were read with an automated microplate system as either high or low, based on agglutination, with a cutoff equivalent to 1:256 (both anti-A and anti-B). Donor demographic data analyzed included date of donation, blood group, age, gender, and ethnicity.
57,508 donations were collected from 2073 unique donors between 2014 and 2018. We found the following demographics to be correlated with titer status: gender, ABO blood group, age, and ethnicity. Variability in titer status was identified in 215 individuals. This represented around 10 % of the total unique donors and was split equally amongst gender. We also found that donors between the ages of 41-60 ha d the highest likelihood of having variability in titer status, peaking at 13 %, and this proportion declined past age 60.
Titer status is associated with the following donor demographics: gender, ABO type, age, and ethnicity. We also discovered that variability in titer status is correlated with age. In blood centers that do not have automated and routine titer screening procedure, these findings could be used as a method to efficiently identify low-titer donors a-priori.
随着越来越多的医院在创伤复苏中使用低滴度 O 型全血,拥有一种有效的低滴度供者筛选方法至关重要。我们的血液中心在采集血小板捐献物时使用自动化系统来筛选高滴度同种异体血凝素,同时收集详细的供者人口统计学信息。利用这些数据,我们可以确定与滴度状态相关的关键人口统计学因素,从而帮助开发用于滴度筛选的供者分类方法。
根据凝集情况,使用自动化微孔板系统将滴度结果判定为高滴度或低滴度,截断值相当于 1:256(抗 A 和抗 B 均如此)。分析的供者人口统计学数据包括捐献日期、血型、年龄、性别和种族。
2014 年至 2018 年间,从 2073 位独特供者中采集了 57508 份捐献物。我们发现以下人口统计学因素与滴度状态相关:性别、ABO 血型、年龄和种族。在 215 位个体中发现了滴度状态的变异性。这约占总独特供者的 10%,且在性别间平均分配。我们还发现,41-60 岁的供者具有最高的滴度状态变异性可能性,达到 13%,且这一比例在 60 岁后下降。
滴度状态与以下供者人口统计学因素相关:性别、ABO 类型、年龄和种族。我们还发现,滴度状态的变异性与年龄相关。在没有自动化和常规滴度筛选程序的血液中心,这些发现可用于预先识别低滴度供者的方法。