A longitudinal EQA-based process sigma framework for risk-oriented quality management of infectious disease...

Traditional Six Sigma evaluation mainly relies on internal quality control (IQC) data and reflects analytical precision under controlled conditions. However, it may not adequately capture long-term process variation in multicenter laboratory networks. External quality assessment (EQA) provides information on inter-laboratory performance, but single-round EQA results are insufficient for evaluating longitudinal process stability.

Six consecutive EQA rounds from eight clinical laboratories between 2023 and 2025 were analyzed, covering eight infectious disease immunoassays. Each round included five samples, yielding 30 standardized ratios, defined as the measured result divided by the corresponding EQA target value, for each laboratory-assay combination. The dispersion of these longitudinal standardized ratios was summarized as the CV proxy, an empirical indicator of long-term process variation. EQA-derived process sigma was subsequently calculated using long-term systematic bias, study-defined total allowable error, and the CV proxy. Linear mixed-effects modeling was used to estimate the contributions of assay type and laboratory center to process sigma variation. Bootstrap and leave-one-round-out analyses were performed to assess the empirical stability of the CV proxy estimates. Exploratory analyses were conducted to examine associations between operational factors and increased CV proxy.

Across 64 laboratory-assay combinations, the median process sigma was 4.85, ranging from 1.20 to 12.50. HBeAg showed the lowest median process sigma, whereas anti-HIV, anti-HCV, and HBsAg showed higher values. Assay type explained 58% of process sigma variation, and center-level differences accounted for approximately 20%. Higher CV proxy values showed exploratory associations with calibration delay, reagent lot changes, and insufficient training.

CV proxy and EQA-derived process sigma provide complementary tools for long-term process capability assessment and risk-oriented quality management using routinely available EQA data.