Assessment of patient based real-time quality control on comparative assays for common clinical analytes

It is critical for laboratories to conduct multianalyzer comparisons as a part of daily routine work to strengthen the quality management of test systems. Here, we explored the application of patient-based real-time quality controls (PBRTQCs) on comparative assays to monitor the consistency among clinical laboratories.

The present study included 11 commonly tested analytes that were detected using three analyzers. PBRTQC procedures were set up with exponentially weighted moving average (EWMA) algorithms and evaluated using the AI-MA artificial intelligence platform. Comparative assays were carried out on serum samples, and patient data were collected. Patients were divided into total patient (TP), inpatient (IP), and outpatient (OP) groups.

Optimal PBRTQC protocols were evaluated and selected with appropriate truncation limits and smoothing factors. Generally, similar comparative assay performance was achieved using both the EWMA and median methods. Good consistency between the results from patients' data and serum samples was obtained, and unacceptable bias was detected for alkaline phosphatase (ALP) and gamma-glutamyl transferase (GGT) when using analyzer C. Categorizing patients' data and applying specific groups for comparative assays could significantly improve the performance of PBRTQCs. When monitoring the inter- and intraanalyzer stability on a daily basis, EWMA was superior in detecting very small quality-related changes with lower false-positive alarms.

We found that PBRTQCs have the potential to efficiently assess multianalyzer comparability. Laboratories should be aware of population variations concerning both analytes and analyzers to build more suitable PBRTQC protocols.