Strengthening the interpretation of longitudinal EQA data through the sporadic inclusion of native human sera in general clinical chemistry.

INTRODUCTION

Comparative external quality assessment (EQA) surveys using native human sera alongside routinely distributed processed EQA materials (EQAMs) provide valuable information on measurement procedure (MP) trueness and the status of standardization. They also allow EQA providers to retrospectively identify critical or less critical analyte/MP pairings regarding the commutability of processed EQAMs. This study evaluates the benefit of interpreting such commutability-related indications in the context of longitudinal EQA data, for cholesterol, urea, creatinine (enzymatic), creatinine (kinetic), γ-glutamyl transferase (GGT), and sodium.

METHODS

In the comparative EQA, nearly 400 results were obtained for each of four native human sera and two processed EQAMs. Mixed-effects models were used to estimate analyte/MP-specific mean natural logarithm (ln) bias to reference measurement values for native and processed EQAMs, mean differences in ln bias between sample types, and based on longitudinal EQA data between 08/2023 and 10/2024, MP/batch-specific mean ln bias.

RESULTS

In the comparative EQA study, MP-specific mean differences in bias between sample types were generally small, with most meeting the analyte-specific commutability criteria. An exception among native EQAMs was observed for GGT, showing differences in bias of more than -7% and pronounced variabilities in MP-specific mean bias (standard deviation (SD) in percentage points: 3.9-10.2), likely associated with low enzyme activities. In contrast, most analyte/MP-specific bias in native EQAMs was highly consistent (SD: enzymatic assays 0.3-3.1, creatinine (kinetic) 1.7-6.6, sodium 0.21-0.42). Longitudinal data showed higher variabilities for processed EQAMs for multiple analyte/MP pairings (SD: enzymatic assays 1.0-10.0, creatinine (kinetic) 2.1-6.5, sodium 0.38-1.00), indicating higher MP susceptibilities to synthetic EQAM matrices. Substantial shifts in bias ranges between sample types were observed for cholesterol and urea across most MPs, and for individual MPs for the remaining analytes.

CONCLUSION

In some processed EQAM batches, bias was markedly greater than observed for the batches in the comparative EQA survey, indicating that the limited number of batches in such studies do not allow generalized interpretation on commutability, and requires batch-specific assessment. However, when combined with longitudinal monitoring of MP-specific bias, a comprehensive picture emerges of MP trueness, MP susceptibility to interferences and commutability of EQAMs, thereby supporting MP and control material manufacturers in quality improvement.