国际医药卫生导报 ›› 2025, Vol. 31 ›› Issue (16): 2734-2738.DOI: 10.3760/cma.j.cn441417-20241211-16019

• 临床研究 • 上一篇    下一篇

单采血小板输注患者血小板聚集的风险因子探讨

唐琳1  陈玉平2  巨小英3   

  1. 1宝鸡市人民医院输血科,宝鸡 721000;2江苏力博医药生物技术股份有限公司,无锡 214400;3宝鸡第三医院输血科,宝鸡 721000

  • 收稿日期:2024-12-11 出版日期:2025-08-15 发布日期:2025-08-28
  • 通讯作者: 巨小英,Email:juxiaoying040506@163.com
  • 基金资助:

    江苏省科技成果转化专项资金项目(BA2017047)

Risk factors of platelet aggregation in patients undergoing apheresis platelet transfusion

Tang Lin1, Chen Yuping2, Ju Xiaoying3   

  1. 1 Department of Blood Transfusion, Baoji People's Hospital, Baoji 721000, China; 2 Jiangsu Libo Pharmaceutical Biotechnology Co., Ltd., Wuxi 214400, China; 3 Department of Blood Transfusion, Baoji Third Hospital, Baoji 721000, China

  • Received:2024-12-11 Online:2025-08-15 Published:2025-08-28
  • Contact: Ju Xiaoying, Email: juxiaoying040506@163.com
  • Supported by:

    Project Funded by Scientific and Technological Achievement Transformation Program in Jiangsu (BA2017047)

摘要:

目的 基于反应范围模型分析单采血小板(platelet,PLT)输注患者PLT聚集的风险因子。方法 回顾性分析2021年5月至2024年7月于宝鸡市人民医院行单采PLT输注的341例患者的临床资料,其中男180例,女161例,年龄51(39,65)岁。依据有无发生PLT聚集将患者分为发生组和未发生组。基于患者临床特征以及专家共识、指南在专家指导下设计反应范围模型,对单采PLT输注患者PLT聚集的风险因子进行研究假设。比较发生组和未发生组临床资料。采用logistic回归模型分析单采PLT输注患者PLT聚集的影响因素。采用t检验、Mann-Whitney U检验、χ2检验进行统计分析。结果 341例单采PLT输注患者PLT聚集发生率为11.14%(38/341)。发生组年龄、单采PLT库存时间>3 d占比、PLT输注次数高于未发生组[58.0(40.5,72.0)岁比48.0(38.0,60.0)岁、36.84%(14/38)比20.13%(61/303)、(7.03±1.55)次比(5.10±1.09)次],输注前PLT计数低于未发生组[10.5(7.0,13.0)×109/L比16.0(11.0,20.0)×109/L],差异均有统计学意义(均P<0.05)。logistic回归模型分析结果示,年龄大[比值比(OR)=1.183,95%置信区间(CI)1.019~1.373]、输注前PLT计数低(OR=0.793,95%CI 0.673~0.935)、单采PLT库存时间>3 d(OR=2.793,95%CI 1.079~7.225)、PLT输注次数多(OR=1.511,95%CI 1.174~1.946)均是单采PLT输注患者PLT聚集的风险因素(均P<0.05)。结论 年龄大、输注前PLT计数低、单采PLT库存时间>3 d、PLT输注次数多均是单采PLT输注患者PLT聚集的风险因子。

关键词:

血小板输注, 单采血小板, 血小板聚集, 风险因子, 反应范围模型

Abstract:

Objective To analyze the risk factors of platelet aggregation in patients undergoing apheresis platelet (PLT) transfusion based on the reactive scope model. Methods A retrospective analysis was conducted on the clinical data of 341 patients who underwent apheresis PLT transfusion in Baoji People's Hospital from May 2021 to July 2024, including 180 males and 161 females who were 51 (39, 65) years old. The patients were divided into an occurrence group and a non-occurrence group based on the presence or absence of PLT aggregation. Based on the patients' clinical characteristics, expert consensus, and guidelines, a reactive scope model was designed under expert guidance to study the risk factors of PLT aggregation in the patients. The clinical data of the occurrence group and the non-occurrence group were compared. The influencing factors of PLT aggregation in the patients were analyzed by the logistic regression model. t test, Mann-Whitney U test, and χ2 test were used for the statistical analysis. Results The incidence of PLT aggregation in the 341 patients was 11.14% (38/341). The age, the proportion of single dose PLT inventory time > 3 d, the number of PLT transfusion of the occurrence group were higher than those of the non-occurrence group [58 (40.5, 72.0) years vs. 48.0 (38.0, 60.0) years, 36.84% (14/38) vs. 20.13% (61/303), and (7.03±1.55) times vs. (5.10±1.09) times], and the PLT count before transfusion was lower [10.5 (7.0, 13.0) ×109/L vs. [16.0 (11.0, 20.0) ×109/L], with statistical differences (all P<0.05). The results of logistic regression model analysis showed that older age [odds ratio (OR)=1.183, 95% confidence interval (CI) 1.019-1.373], lower PLT count before transfusion (OR=0.793, 95%CI 0.673-0.935), single dose PLT inventory time > 3 d (OR=2.793, 95%CI 1.079-7.225), and multiple PLT infusions (OR=1.511, 95%CI 1.174-1.946) were all risk factors of PLT aggregation in the patients (all P<0.05). Conclusion Older age, lower PLT count before transfusion, single dose PLT inventory time > 3 d, and multiple PLT infusions are all risk factors of PLT aggregation in patients undergoing apheresis PLT transfusion.

Key words:

Platelet transfusion, Apheresis platelet, Platelet aggregation, Risk factors, Reactive scope model