Modeling and Analysis of the Uncertainty of Enzyme Measurement Processes in Clinical Laboratories

Clinical laboratory measurement processes inform every stage of the medical decision-making process, and measurement of the activity levels of enzymes such as alkaline phosphatase, alanine aminotransferase, etc. provide vital information regarding various body functions such as the liver and gastrointestinal tract. The uncertainty associated with the result of these enzyme assays describes the quality of the measurement process, and therefore methods to improve the quality of the measurement process require minimizing the measurement uncertainty of the enzyme assay. In this paper, we develop a physics-based mathematical model of the alkaline phosphatase (ALP) measurement process, with uncertainty introduced into its components to represent the sources of variation in the measurement process. The Monte Carlo method is then utilized to describe the long-term behavior of the model and estimate the uncertainty associated with the result of the measurement. An uncertainty profile used to generate estimates of uncertainty at different patient sample activity levels is constructed. We then utilize the model to quantify the contributions of the individual sources of uncertainty to the net measurement uncertainty, and also quantify the effect of uncertainty within the calibration process on the net measurement uncertainty.