Objective: To explore the causes of platelet aggregation in version 6.4 Trima Accel automated blood collection system and the effect of 2 intervention measures.
Methods: The data on platelet aggregation (n=61) and non-aggregation (n=323) of 61 donors in 2020 were collected and the causes of aggregation were analyzed. Then the 72 donors with platelet aggregation in 2021 were randomized into intervention group A (increasing the anticoagulant-to-blood ratio) and intervention group B (wrapping the donor's arm with an electric blanket to keep warm and improve the blood flow speed). The collection time, average blood flow speed, number of machine alarms, anticoagulant usage, deaggregation and citrate reaction of the two groups were compared.
Results: Platelet aggregation was negatively correlated with the average blood flow speed (r =-0.394) and positively correlated with the collection time (r =0.458). The equations for predicting aggregation and non-aggregation were constructed based on Bayesian and Fisher discriminant analysis, and the predicted accuracy was 77.1%. The comparison of the effects of two intervention measures showed that the average blood flow speed in group B was higher than that in group A; the collection time, number of machine alarms, anticoagulant usage and proportion of citrate reaction in blood donors in group B were all lower than those in Group A, all these differences were significant (P < 0.05). In the entire cohort in 2021, 90.28% of the products were immediately deaggregated after collection, and 9.72% of the products were deaggregated within 4 hours. There was no statistically significant difference in deaggregation between the two intervention groups (P >0.05).
Conclusion: During apheresis platelet collection, the predictive equations for aggregation and non-aggregation can be used to predict the occurrence probability of aggregation, and the intervention can be made in advance. Both intervention measures are effective in reducing platelet aggregation, however, measure B has the advantages of improving the speed of blood collection, shortening the collection time, reducing the alarm frequency and the anticoagulant usage, and reducing the incidence of citrate reaction in blood donors.
题目: 6.4版Trima血细胞分离机采集血小板发生聚集的原因及2种干预措施的对比.
目的: 探讨6.4版Trima血细胞分离机采集血小板发生聚集的原因以及2种干预措施的效果。.
方法: 收集2020年61例献血者血小板捐献聚集次（n=61）与非聚集次（n=323）数据，分析聚集原因；将2021年单采过程中出现聚集的72例次随机分为干预A组和干预B组，干预A组提高抗凝剂与血液的比率，干预B组对献血者捐献侧手臂进行电热毯包裹保暖，提高献血者的血流速度。对比2组采集时间、平均血流速度、机器报警次数、抗凝剂使用量、产品下机解聚、献血者枸橼酸盐反应等情况。.
结果: 聚集与平均血流速度呈负相关（r =-0.394)，与采集时间呈正相关（r =0.458）；经贝叶斯与Fisher判别分析，分别对聚集和非聚集构建了方程式，该方程判别分析预测的正确率为77.1%。2种干预措施效果比较，B组平均血流速度高于A组；采集时间、机器报警次数和抗凝剂使用量、献血者枸橼酸盐反应例数均明显低于A组（P < 0.05）；2021年的整个队列中，90.28%的产品下机立即解聚，9.72%的产品4 h内解聚，2种干预措施组产品下机解聚情况对比差异没有统计学意义（P >0.05）。.
结论: 单采血小板过程中可用构建的聚集和非聚集方程式进行聚集发生概率的预判，并可提前做出干预。2种干预措施对降低血小板聚集都有效，但措施B具有提高采血速度、缩短采集时间、减少报警次数和抗凝剂用量、降低献血者枸橼酸盐反应发生率等优点。.
Keywords: Trima Accel Automated Blood Collection System; platelet aggregation; anticoagulant; citrate reaction; blood flow speed.