Definition of Significant Platelet Clumping: Should We Review All Samples With a Platelet Clumping Flag From Automated Hematology Analyzer?

Suhyeon Woo, Bohyun Kim, Nam Hun Heo, Min-Sun Kim, Young Ahn Yoon, Young-Jin Choi
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Abstract

Objectives: This study aimed to determine a definition for significant platelet clumping (PC) and evaluate the performance of the Sysmex XN instrument for detecting platelet clumps.

Methods: For part 1, 372 specimens with a 'PLT_clump?' flag in XN-9000 were classified into five groups according to the average number of PCs. We compared the initial platelet count (measured by XN-9000 using impedance method) and corrected platelet count (counted optically or re-analyzed by XN-9000 using vortexed or re-collected sample) of each group. For part 2, 1000 specimens with a PC flag divided into three subgroups {group N (PC = 0), Y (PC ≥ 1), and Z (microscopic fibrin clot)} and additional two groups {group S (PC(+) specimens without any flag and with flags of other categories) and group NC (negative control)} were collected. Positive predictive value (PPV), negative predictive value (NPV), sensitivity and specificity of PC detection of XN-9000 were obtained and the platelet counts and four indices (PDW, MPV, P_LCR, and PCT) of groups NC, N, Y, Z, and S were compared to detect PC more precisely.

Results: In part 1, all groups showed significant difference between the initial and corrected platelet counts. In part 2, PPV, NPV, prevalence, sensitivity, and specificity were 41.5%, 56.5%, 43.4%, 2.18%, and 98.3%, respectively. The platelet counts and four indices showed statistical differences for detecting PCs, and especially PDW and P_LCR were significantly smaller in group Z than group N or Y.

Conclusions: We suggest the definition of significant PC by the presence of at least three platelets. In addition, utilizing platelet-related indices should be developed to improve the efficiency of the PC detection.

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显著血小板聚集的定义:我们是否应该对自动血液分析仪上所有带有血小板凝集标记的样本进行复查?
研究目的本研究旨在确定明显血小板聚集(PC)的定义,并评估 Sysmex XN 仪器检测血小板聚集的性能:第一部分:根据 PC 的平均数量,将 372 份在 XN-9000 中带有 "PLT_clump? "标志的标本分为五组。我们比较了每组的初始血小板计数(XN-9000 使用阻抗法测量)和校正血小板计数(XN-9000 使用涡旋或重新采集的样本进行光学计数或重新分析)。第二部分收集了 1000 份带有 PC 标志的标本,分为三个亚组{N 组(PC = 0)、Y 组(PC ≥ 1)和 Z 组(显微镜下纤维蛋白凝块)},以及另外两组{S 组(无任何标志和带有其他类别标志的 PC(+) 标本)和 NC 组(阴性对照)}。得出 XN-9000 检测 PC 的阳性预测值(PPV)、阴性预测值(NPV)、灵敏度和特异性,并比较 NC、N、Y、Z 和 S 组的血小板计数和四项指数(PDW、MPV、P_LCR 和 PCT),以更精确地检测 PC:结果:在第一部分中,所有组的初始血小板计数和校正血小板计数均有显著差异。在第二部分中,PPV、NPV、患病率、敏感性和特异性分别为 41.5%、56.5%、43.4%、2.18% 和 98.3%。血小板计数和四项指数在检测 PC 方面存在统计学差异,尤其是 Z 组的 PDW 和 P_LCR 明显小于 N 组或 Y 组:结论:我们建议以至少出现三个血小板来定义明显的 PC。结论:我们建议以至少出现三个血小板来定义明显的 PC,此外,应开发利用血小板相关指数来提高 PC 的检测效率。
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