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现行实验室方案未能检测到批间试剂差异:发现与可能的解决方案。

Failure of current laboratory protocols to detect lot-to-lot reagent differences: findings and possible solutions.

机构信息

Division of Clinical Biochemistry and Immunology, Department of Laboratory, Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.

出版信息

Clin Chem. 2013 Aug;59(8):1187-94. doi: 10.1373/clinchem.2013.205070. Epub 2013 Apr 16.

Abstract

BACKGROUND

Maintaining consistency of results over time is a challenge in laboratory medicine. Lot-to-lot reagent changes are a major threat to consistency of results.

METHODS

For the period October 2007 through July 2012, we reviewed lot validation data for each new lot of insulin-like growth factor 1 (IGF-1) reagents (Siemens Healthcare Diagnostics) at Mayo Clinic, Rochester, MN, and the University of Virginia, Charlottesville, VA. Analyses of discarded patient samples were used for comparison of lots. For the same period, we determined the distributions of reported patient results for each lot of reagents at the 2 institutions.

RESULTS

Lot-to-lot validation studies identified no reagent lot as significantly different from the preceding lot. By contrast, significant lot-to-lot changes were seen in the means and medians of 105 668 reported patient IGF-I results during the period. The frequency of increased results increased nearly 2-fold to a high of 17%, without detectable changes in the underlying patient demographics. Retrospective statistical analysis indicated that lot-to-lot comparison protocols were underpowered and that validation studies for this assay required testing >100 samples to achieve 90% power to detect reagent lots that would significantly alter the distributions of patient results.

CONCLUSIONS

The number of test samples required for adequate lot-to-lot validation protocols is high and may be prohibitively large, especially for low-volume or complex assays. Monitoring of the distributions of patient results has the potential to detect lot-to-lot inconsistencies relatively quickly. We recommend that manufacturers implement remote monitoring of patient results from analyzers in multiple institutions to allow rapid identification of between-lot result inconsistency.

摘要

背景

在医学实验室中,保持结果的一致性是一个挑战。试剂批间变化是导致结果一致性受到威胁的主要因素。

方法

在 2007 年 10 月至 2012 年 7 月期间,我们回顾了明尼苏达州罗彻斯特市 Mayo 诊所和弗吉尼亚大学 Charlottesville 分校每批新胰岛素样生长因子 1(IGF-1)试剂(西门子医疗诊断公司)的批验证数据。使用废弃的患者样本进行分析以比较批间差异。同期,我们还确定了这两个机构在每个试剂批的报告患者结果的分布情况。

结果

批间验证研究未发现任何试剂批与前一批有显著差异。相比之下,在这一时期,105668 例报告的患者 IGF-I 结果中,有 105668 例报告的患者结果的平均值和中位数发生了显著的批间变化。结果升高的频率增加了近两倍,达到了 17%,而患者的基本人口统计学特征没有变化。回顾性统计分析表明,批间比较方案的效能不足,该检测的验证研究需要测试>100 个样本,才能达到 90%的效能,以检测到会显著改变患者结果分布的试剂批。

结论

充分的批间验证方案所需的测试样本数量很高,而且可能大到无法承受,特别是对于低容量或复杂的检测。监测患者结果的分布情况有可能相对较快地发现批间不一致。我们建议制造商从多个机构的分析仪中实施患者结果的远程监测,以便快速识别批间结果的不一致。

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