From the Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu, China (Chen, Mao, Wang, Liao, Zheng).
West China School of Medicine, Sichuan University, Chengdu, China (Deng).
Arch Pathol Lab Med. 2024 Jan 1;148(1):61-67. doi: 10.5858/arpa.2022-0030-OA.
CONTEXT.—: Platelet (PLT) counting with impedance (PLT-I) is widely used but has low specificity. PLT counting with fluorescence (PLT-F), tested by the Sysmex XN series with high specificity, can be a complementary method to PLT-I.
OBJECTIVE.—: To identify red blood cell (RBC)- and PLT-related parameters as potential influencing factors for PLT-I and establish PLT reflex test rules with PLT-F.
DESIGN.—: We prospectively tested both PLT-I and PLT-F in all 3480 samples. In a development data set of 3000 samples, differences between the reflex and nonreflex groups were compared and influencing factors for PLT-I were identified by logistic regression. The area under the receiver operating characteristic (ROC) curve and cutoff values were obtained by ROC curve analysis. Validation was conducted in the remaining 480 samples (validation data set).
RESULTS.—: PLT-F showed comparable results with immunoplatelet counting. In logistic regression, increased micro-RBC absolute count (micro-RBC#), fragmented RBC absolute count (FRC#), PLT distribution width (PDW), mean PLT volume (MPV), PLT-large cell ratio (P-LCR), and immature PLT fraction absolute count (IPF#) were influencing factors for PLT-I. In ROC curve analysis, the cutoff values of micro-RBC#, FRC#, PDW, MPV, and P-LCR were 0.64 × 106/μL, 0.082 × 106/μL, 15.40 fL, 11.15 fL, and 33.95%, respectively. The areas under the ROC curve of micro-RBC# and FRC# were 0.77 and 0.79, respectively.
CONCLUSIONS.—: Micro-RBC#, FRC#, PDW, MPV, P-LCR, and IPF# were factors affecting PLT-I. Among them, micro-RBC# and FRC# were the most impactful factors. From our study results, micro-RBC#, FRC#, MPV, PDW, and P-LCR can be used to establish reflex test rules for PLT counting in clinical work.
血小板(PLT)计数采用阻抗法(PLT-I)应用广泛,但特异性低。采用高特异性 Sysmex XN 系列检测的血小板计数采用荧光法(PLT-F),可以作为 PLT-I 的补充方法。
确定红细胞(RBC)和血小板相关参数作为影响 PLT-I 的潜在因素,并建立与 PLT-F 相关的血小板反射测试规则。
前瞻性检测 3480 份样本的 PLT-I 和 PLT-F。在 3000 份样本的开发数据集,比较反射组和非反射组之间的差异,并通过逻辑回归确定影响 PLT-I 的因素。通过 ROC 曲线分析获得 ROC 曲线下面积和截断值。在其余 480 份样本(验证数据集)中进行验证。
PLT-F 与免疫血小板计数结果具有可比性。在逻辑回归中,增加的微红细胞绝对值(micro-RBC#)、破碎红细胞绝对值(FRC#)、血小板分布宽度(PDW)、平均血小板体积(MPV)、血小板大细胞比(P-LCR)和未成熟血小板分数绝对值(IPF#)是影响 PLT-I 的因素。在 ROC 曲线分析中,micro-RBC#、FRC#、PDW、MPV 和 P-LCR 的截断值分别为 0.64×106/μL、0.082×106/μL、15.40 fL、11.15 fL 和 33.95%。micro-RBC#和 FRC#的 ROC 曲线下面积分别为 0.77 和 0.79。
微红细胞#、FRC#、PDW、MPV、P-LCR 和 IPF#是影响 PLT-I 的因素。其中,微红细胞#和 FRC#是最具影响力的因素。根据我们的研究结果,微红细胞#、FRC#、MPV、PDW 和 P-LCR 可用于在临床工作中建立血小板计数的反射测试规则。