Use of anion gap for the quality control of electrolyte analyzers

A simple model for the simulation of patient Na, CO2, Cl, and anion gap was formulated from patient electrolyte data. Analytical error, either random or systematic, was incorporated into the simulation of the electrolyte data and allowed study of the response of anion gap to error. Power functions, plots of probability of error detection vs. size of analytical error, were constructed and indicated a low probability of error detection when single patient specimens with abnormal anion gaps were reanalyzed. These power functions showed that pooling of the anion gap data by averaging consecutive anion gaps resulted in a high probability for detecting systematic error. We recommend, as a useful quality control procedure, averaging at least eight consecutive anion gaps and testing for a significant difference between the average and the established mean gap.