B-137 Normalization of Bead-Based Multiplex Immunoassay Biomarker Data in a High-Throughput Research Laboratory Setting

Abstract Background Laboratory test results guide clinical decisions, yet laboratories struggle to generate consistent measurements. While clinical laboratories employ lot verification testing and error detection techniques, these methods inadequately address two sources of variation: reagent lot changes and manual testing errors. Current approaches fail to detect subtle analytical drift across reagent lots and cannot identify all manual testing deviations. This study investigated a comprehensive statistical framework to normalize biomarker measurements from clinical samples, targeting improved measurement reproducibility across extended testing. Methods The study included 25,970 plasma samples collected from nine hospitals between Dec. 2018 and Feb. 2025. Using the Luminex MAGPIX we measured 43 biomarkers across 6 multiplex panels, using 115 reagent kit lots. All samples originated from patients meeting a blood culture order criteria, with their remnant plasma samples frozen and stored in our centralized biobank. The study’s endpoint was an improvement in the incurred sample reanalysis (ISR) acceptance rate, defined as the percentage of reanalyzed samples measured within 30% of the average of its original and reanalyzed value. Missing or invalid measurements were identified according to FDA bioanalytical method validation guidance. We implemented two correction strategies. First, we standardized calibration curve fitting within reagent lots using the nCal R package, which applies a Bayesian hierarchical five-parameter logistic model. Standard curve errors were identified when calibration sample median fluorescence intensity (MFI) deviated significantly from lot patterns, while quality control samples remained stable. Second, we corrected lot-to-lot variation by mapping all measurements to a reference lot, defined as the lot with highest sample measurement count, using linear regression models derived from ISR data. Results The normalization method improved ISR acceptance rates in 38 of 42 biomarkers. Acceptance rates increased by an average of 4.9% across all biomarkers. Figure 1 illustrates the post-normalization improvements in ISR acceptance rates for each biomarker. Conclusion Our statistical framework improved biomarker measurement reproducibility by addressing two major sources of laboratory variation. This normalization process improved ISR acceptance rates across 90% of tested biomarkers, enhancing measurement reproducibility. By leveraging historical ISR data to correct lot-to-lot variability and implementing unified standard curves within lots, our approach can reduce measurement variability in a high-throughput clinical testing environment. These improvements in data quality support more reliable biomarker studies and strengthen clinical laboratory quality control processes.