The Application of Six Sigma to Assess the Analytical Performance of Plasma Proteins and Design a Risk-Based Statistical Quality Control Strategy: A Multicenter Study

Background

This study applied the six sigma model to evaluate plasma protein testing performance in six laboratories, with customized quality control programs and targeted improvements introduced where necessary.

Methods

Internal quality control (IQC) and external quality assessment (EQA) data for plasma proteins were gathered from six laboratories. Sigma values for each analyte were determined based on the coefficient of variation (CV), bias, and total allowable error (TEa). Using six sigma performance verification charts, we calibrated analyte performance and, guided by Westgard sigma rules, batch length, and quality goal index (QGI), developed laboratory-specific quality control schemes and improvement plans.

Results

Despite standardized platforms and reagents, sigma values showed significant inter-laboratory variation, with some differences also observed within labs at varying analyte concentrations. For projects with sigma < 6, tailored quality control measures were implemented, leading to marked performance improvements.

Conclusion

The six sigma model provides an objective framework for evaluating plasma protein test performance and enhancing quality. It enables quantitative assessment of laboratory management and supports the development and implementation of customized, risk-based statistical quality control (SQC) strategies and improvement measures across multiple laboratory systems.