Recommendation to treat continuous variable errors like attribute errors

Abstract

Clinical laboratory errors can be considered as either belonging to attribute or continuous variables. Attribute errors are usually considered to be pre- or post-analytical errors, whereas continuous variable errors are analytical. Goals for each error type are different. Error goals for continuous variables are often specified as limits that contain 95% of the results, whereas attribute error goals are specified as allowed error rates for serious events. This leads to a discrepancy, because for a million results, there can be up to 50,000 medically unacceptable analytical errors, but allowable pre- and post-analytical error rates are much lower than 5%. Steps to remedy this are to classify analytical error rates into severity categories, exemplified by existing glucose error grids. The results in each error grid zone are then counted, as has been recommended by the Food and Drug Administration (FDA). This in effect transforms the continuous variable errors into attribute errors. This is an improvement over current practices for analytical errors, whereby the use of uncertainty intervals is recommended that include only 95% of the results (i.e., leaves out the worst 5%), and it is precisely this 5% of results that are likely to be in the most severe zones of an error grid.