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规划连续生产分析仪加框操作的基于风险的 SQC 计划。

Planning Risk-Based SQC Schedules for Bracketed Operation of Continuous Production Analyzers.

机构信息

Department of Pathology and Laboratory Medicine, University of Wisconsin School of Public Health, Madison WI;

Westgard QC, Inc., Madison WI.

出版信息

Clin Chem. 2018 Feb;64(2):289-296. doi: 10.1373/clinchem.2017.278291. Epub 2017 Nov 2.

Abstract

BACKGROUND

To minimize patient risk, "bracketed" statistical quality control (SQC) is recommended in the new CLSI guidelines for SQC (C24-Ed4). Bracketed SQC requires that a QC event both precedes and follows (brackets) a group of patient samples. In optimizing a QC schedule, the frequency of QC or run size becomes an important planning consideration to maintain quality and also facilitate responsive reporting of results from continuous operation of high production analytic systems.

METHODS

Different plans for optimizing a bracketed SQC schedule were investigated on the basis of Parvin's model for patient risk and CLSI C24-Ed4's recommendations for establishing QC schedules. A Sigma-metric run size nomogram was used to evaluate different QC schedules for processes of different sigma performance.

RESULTS

For high Sigma performance, an effective SQC approach is to employ a multistage QC procedure utilizing a "startup" design at the beginning of production and a "monitor" design periodically throughout production. Example QC schedules are illustrated for applications with measurement procedures having 6-σ, 5-σ, and 4-σ performance.

CONCLUSIONS

Continuous production analyzers that demonstrate high σ performance can be effectively controlled with multistage SQC designs that employ a startup QC event followed by periodic monitoring or bracketing QC events. Such designs can be optimized to minimize the risk of harm to patients.

摘要

背景

为了将患者风险最小化,CLSI 新的 SQC 指南(C24-Ed4)建议采用“括号式”统计质量控制(SQC)。括号式 SQC 要求 QC 事件既在一组患者样本之前又在之后(用括号括起来)。在优化 QC 计划时,QC 频率或运行规模成为维持质量和促进高产量分析系统连续运行结果快速报告的重要规划考虑因素。

方法

根据 Parvin 的患者风险模型和 CLSI C24-Ed4 建立 QC 计划的建议,研究了优化括号式 SQC 计划的不同方案。西格玛度量运行规模指标图用于评估不同西格玛性能过程的不同 QC 计划。

结果

对于高西格玛性能,有效的 SQC 方法是采用多阶段 QC 程序,在生产开始时使用“启动”设计,在生产过程中定期使用“监测”设计。针对具有 6-σ、5-σ 和 4-σ 性能的测量程序,举例说明了 QC 计划。

结论

具有高 σ 性能的连续生产分析仪可以通过采用启动 QC 事件后定期监测或括号式 QC 事件的多阶段 SQC 设计进行有效控制。此类设计可以进行优化,以将对患者的伤害风险最小化。

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