A simulation-based approach to modeling the uncertainty of two-substrate clinical enzyme measurement processes

Results of clinical laboratory tests inform every stage of the medical decision-making process, and measurement of enzymes such as alanine aminotransferase provide vital information regarding the function of organ systems such as the liver and gastrointestinal tract. Estimates of measurement uncertainty quantify the quality of the measurement process, and therefore, methods to improve the quality of the measurement process require minimizing assay uncertainty. To accomplish this, we develop a physics-based mathematical model of the alanine aminotransferase assay, with uncertainty introduced into its parameters that represent variation in the measurement process, and then use the Monte Carlo method to quantify the uncertainty associated with the model of the measurement process. Furthermore, the simulation model is used to estimate the contribution of individual sources of uncertainty as well as that of uncertainty in the calibration process to the net measurement uncertainty.