Are frequent calibrations enough? A patient-based quality control perspective on blood gas and laboratory analyzers

Quality control (QC) plays a fundamental role in clinical laboratories by safeguarding the accuracy and reliability of test outcomes. Conventional QC materials often fail to fully reflect patient sample behavior due to matrix differences, leading to non-commutability. Patient-Based Quality Control (PBQC), which evaluates real patient data, offers greater sensitivity for detecting analytical errors. Blood gas analyzers in point-of-care settings rely on frequent automatic calibrations, whereas central laboratory analyzers use operator-driven calibrations. This study compared their analytical performance using PBQC metrics.

In this retrospective study, patient results for glucose, sodium (Na), and hemoglobin (Hgb) were collected over a three-month period (May-July) at Ümraniye Training and Research Hospital. Data were obtained from central laboratory analyzers (Roche COBAS 8000, Mindray BC6000) and a point-of-care blood gas analyzer (Siemens Rapidpoint 500). A total of 112,898 results were included following the application of three truncation methods based on Reference Interval (RI), RCVG, and the Outlier Q test. PBQC analysis was conducted using Exponentially Weighted Moving Average (EWMA) charts (λ = 0.05) with evaluation of False Positive Flag Rate and average number of patient results before error detection (ANPed), in accordance with IFCC recommendations. Probability of error detection (Ped) values were also calculated from IQC data.

PBQC analysis demonstrated that the False Positive Flag Rate was consistently lower in central laboratory analyzers compared to the blood gas analyzer. ANPed values were low in both device types, indicating effective error detection capability. Ped values reached 1.00 for glucose in the Rapidpoint analyzer and sodium in the COBAS 8000, whereas lower Ped values were observed for Rapidpoint sodium and hemoglobin.

PBQC provides an effective complement to conventional QC, enabling early detection of analytical deviations in both central laboratory and point-of-care analyzers. Our findings show that blood gas analyzers, despite frequent automatic calibrations, generate more false positive alerts, whereas central laboratory analyzers demonstrate more stable performance. We propose that PBQC-based algorithms be integrated into blood gas analyzer software, allowing calibrations to be triggered by observed deviations rather than fixed time intervals. Such an approach would enhance analytical reliability and improve quality assurance in point-of-care testing.