Çubukçu Hikmet Can
General Directorate of Health Services, Rare Diseases Department, Turkish Ministry of Health, Ankara, Türkiye.
Clin Chem Lab Med. 2024 May 31;62(11):2185-2197. doi: 10.1515/cclm-2024-0156. Print 2024 Oct 28.
Clinical laboratories face limitations in implementing advanced quality control (QC) methods with existing systems. This study aimed to develop a web-based application to addresses this gap, and improve QC practices.
QC Constellation, a web application built using Python 3.11, integrates various statistical QC modules. These include Levey-Jennings charts with Westgard rules, sigma-metric calculations, exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) charts, and method decision charts. Additionally, it offers a risk-based QC section and a patient-based QC module aligning with modern QC practices. The codes and the web application links for QC Constellation were shared at https://github.com/hikmetc/QC_Constellation, and http://qcconstellation.com, respectively.
Using synthetic data, QC Constellation demonstrated effective implementation of Levey-Jennings charts with user-friendly features like checkboxes for Westgard rules and customizable moving averages graphs. Sigma-metric calculations for hypothetical performance values of serum total cholesterol were successfully performed using allowable total error and maximum allowable measurement uncertainty goals, and displayed on method decision charts. The utility of the risk-based QC module was exemplified by assessing QC plans for serum total cholesterol, showcasing the application's capability in calculating risk-based QC parameters including maximum unreliable final patient results, risk management index, and maximum run size and offering risk-based QC recommendations. Similarly, the patient-based QC and optimization modules were demonstrated using simulated sodium results.
In conclusion, QC Constellation emerges as a pivotal tool for laboratory professionals, streamlining the management of quality control and analytical performance monitoring, while enhancing patient safety through optimized QC processes.
临床实验室在利用现有系统实施先进的质量控制（QC）方法时面临局限性。本研究旨在开发一个基于网络的应用程序来填补这一空白，并改进质量控制实践。
QC Constellation是一个使用Python 3.11构建的网络应用程序，集成了各种统计质量控制模块。这些模块包括带有韦斯特加德规则的利维-詹宁斯图、西格玛指标计算、指数加权移动平均（EWMA）和累积和（CUSUM）图，以及方法决策图。此外，它还提供了一个基于风险的质量控制部分和一个与现代质量控制实践相一致的基于患者的质量控制模块。QC Constellation的代码和网络应用程序链接分别在https://github.com/hikmetc/QC_Constellation和http://qcconstellation.com上共享。
使用合成数据，QC Constellation展示了利维-詹宁斯图的有效实施，具有用户友好的功能，如韦斯特加德规则的复选框和可定制的移动平均线图。利用允许总误差和最大允许测量不确定度目标，成功地对血清总胆固醇的假设性能值进行了西格玛指标计算，并显示在方法决策图上。通过评估血清总胆固醇的质量控制计划，例证了基于风险的质量控制模块的实用性，展示了该应用程序在计算基于风险的质量控制参数（包括最大不可靠最终患者结果、风险管理指数和最大运行规模）方面的能力，并提供基于风险的质量控制建议。同样，使用模拟钠结果演示了基于患者的质量控制和优化模块。
总之，QC Constellation成为实验室专业人员的关键工具，简化了质量控制管理和分析性能监测，同时通过优化的质量控制流程提高了患者安全性。
