Biogroup, Technical Direction, Lyon, France.
Bioesterel-Biogroup - Technical Platform, Sanary sur Mer, France.
Clin Chem Lab Med. 2023 Nov 24;62(5):853-860. doi: 10.1515/cclm-2023-0965. Print 2024 Apr 25.
Monitoring quality control for a laboratory or network with multiple instruments measuring the same analyte is challenging. We present a retrospective assessment of a method to detect medically significant out-of-control error conditions across a group of instruments measuring the same analyte. The purpose of the model was to ensure that results from any of several instruments measuring the same analytes in a laboratory or a network of laboratories provide comparable results and reduce patient risk. Limited literature has described how to manage QC in these very common situations.
Single Levey-Jennings control charts were designed using peer group target mean and control limits for five common clinical chemistry analytes in a network of eight analyzers in two different geographical sites. The QC rules used were 1/2/R, with the mean being a peer group mean derived from a large population of the same instrument and the same QC batch mean and a group CV. The peer group data used to set the target means and limits were from a quality assurance program supplied by the instrument supplier. Both statistical and clinical assessments of significance were used to evaluate QC failure. Instrument bias was continually monitored.
It was demonstrated that the biases of each instrument were not statistically or clinically different compared to the peer group's average over six months from February 2023 until July 2023. Over this period, the error rate determined by the QC model was consistent with statistical expectations for the 1/2/R rule. There were no external quality assurance failures, and no detected error exceeded the TEa (medical impact). Thus, the combined statistical/clinical assessment reduced unnecessary recalibrations and the need to amend results.
This paper describes the successful implementation of a quality control model for monitoring a network of instruments, measuring the same analytes and using externally provided quality control targets. The model continually assesses individual instrument bias and imprecision while ensuring all instruments in the network meet clinical goals for quality. The focus of this approach is on detecting medically significant out-of-control error conditions.
监测使用相同分析物测量的多台仪器的实验室或网络的质量控制具有挑战性。我们提出了一种方法的回顾性评估，该方法旨在检测一组测量相同分析物的仪器中的具有医学意义的失控误差情况。该模型的目的是确保实验室或实验室网络中测量相同分析物的任何几台仪器提供可比的结果并降低患者风险。很少有文献描述如何在这些非常常见的情况下管理 QC。
使用网络中 8 台分析仪的两个不同地理位置中的 5 种常见临床化学分析物的同行组靶均值和控制限，设计单 Levey-Jennings 控制图。使用的 QC 规则为 1/2/R，均值为从同一仪器和同一 QC 批均值和组 CV 的大量人群中得出的同行组均值。用于设置目标均值和限值的同行组数据来自仪器供应商提供的质量保证计划。使用统计和临床评估来评估 QC 失败的显著性。持续监测仪器偏差。
结果表明，与同行组平均水平相比，在 2023 年 2 月至 2023 年 7 月的六个月期间，每台仪器的偏差在统计学或临床上均无差异。在此期间，QC 模型确定的错误率与 1/2/R 规则的统计预期一致。没有外部质量保证失败，也没有检测到超过 TEa（医学影响）的错误。因此，综合统计/临床评估减少了不必要的重新校准和结果修正的需要。
本文描述了成功实施了一种用于监测测量相同分析物并使用外部提供的质量控制目标的仪器网络的质量控制模型。该模型持续评估单个仪器的偏差和不精密度，同时确保网络中的所有仪器都满足质量的临床目标。该方法的重点是检测具有医学意义的失控误差情况。
