Application of mathematical models of system uncertainty to evaluate the utility of assay calibration protocols

Abstract

Laboratory protocols used to calibrate commercial clinical chemistry systems affect test result quality. Mathematical models of system uncertainty can be developed using performance parameters provided by the manufacturer for various subsystems. These models can be used to evaluate protocols for specific laboratory operations. A mathematical model was developed to estimate the uncertainty inherent in the Roche Diagnostics P-Modular system, and included uncertainties associated with the sample and reagent pipettes, spectrometer and the calibration process. The model was then used to evaluate various alternate calibration protocols: calibration based on mean of replicate measurements (n=1-6) and calibration based on conditional acceptance when the following quality control specimen was within one standard deviation of target. The effect of calibrator concentrations on assay measurement uncertainty was also studied, and calibrator concentrations that minimize uncertainty at a specific concentration were identified. The simulation model produced uncertainty estimates of 3.5% for the serum cholesterol assay and identified sample pipette (40%) and spectrometer (21%) as the largest contributors to measurement uncertainty. Each additional replicate calibrator measurements result in diminishing reductions in measurement uncertainty, with maximum reductions (19%) achieved with five replicate measurements. The conditional acceptance of calibration only when the control was within 1s resulted in an 18% reduction. The model can be used to evaluate the utility of laboratory protocols and establish realistic assay performance targets. The model also can help instrument manufacturers and laboratorians identify major contributors to assay measurement uncertainty, which helps improve performance in future assay systems.