基于患者数据的质量控制方法的实际应用:以钾为例。

Yan Zhang, Hua-Li Wang, Ye-Hong Xie, Da-Hai He, Chao-Qiong Zhou, Li-Rui Kong
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引用次数: 0

摘要

简介:内部质量控制(IQC)是实验室质量控制战略的核心支柱:内部质量控制(IQC)是实验室质量控制战略的核心支柱。内部质量控制的商业材料缺乏与患者样本相同的特性,因此 IQC 增加了实验室检测的成本。基于患者数据的质量控制(PDB-QC)可能是 IQC 的重要补充;生物变异越小,发现错误的能力就越强。研究以血清中的钾浓度为例,比较了 PDB-QC 和 IQC 的错误检测效果:使用间接离子选择电极法测定血清钾浓度。在训练数据库中,使用了 2022 年 4 月至 2022 年 9 月的 23772 个患者生成的数据和 366 个 IQC 数据;在测试数据库中,使用了 2022 年 10 月至 2023 年 1 月的 15351 个患者生成的数据和 246 个 IQC 数据。计算了PDB-QC和IQC的平均值和标准偏差,并绘制了z-分数图进行比较:结果:使用 IQC 检测出了 5 个系统误差和 3 个随机误差。PDB-QC 检测出 9 个系统误差,但没有随机误差。PDB-QC 比 IQC 更早显示出系统误差警告:患者生成数据的日平均值在检测系统性错误的效率和及时性方面优于 IQC,但在检测随机错误方面则逊于 IQC。
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Practical application of the patient data-based quality control method: the potassium example.

Introduction: Internal quality control (IQC) is a core pillar of laboratory quality control strategies. Internal quality control commercial materials lack the same characteristics as patient samples and IQC contributes to the costs of laboratory testing. Patient data-based quality control (PDB-QC) may be a valuable supplement to IQC; the smaller the biological variation, the stronger the ability to detect errors. Using the potassium concentration in serum as an example study compared error detection effectiveness between PDB-QC and IQC.

Materials and methods: Serum potassium concentrations were measured by using an indirect ion-selective electrode method. For the training database, 23,772 patient-generated data and 366 IQC data from April 2022 to September 2022 were used; 15,351 patient-generated data and 246 IQC data from October 2022 to January 2023 were used as the testing database. For both PDB-QC and IQC, average values and standard deviations were calculated, and z-score charts were plotted for comparison purposes.

Results: Five systematic and three random errors were detected using IQC. Nine systematic errors but no random errors were detected in PDB-QC. The PDB-QC showed systematic error warnings earlier than the IQC.

Conclusions: The daily average value of patient-generated data was superior to IQC in terms of the efficiency and timeliness of detecting systematic errors but inferior to IQC in detecting random errors.

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