Regression Using 20 Samples to Harmonize a Point‐of‐Care Analyzer With a Commercial Laboratory Analyzer for Feline Plasma Biochemistry Testing

BACKGROUND: Harmonization enables direct comparison of patient results from different analyzers and/or measurement techniques and the use of common reference intervals and decision thresholds. Point-of-care analyzers (POC) cannot be calibrated by the end user, so regression offers an opportunity for calculated harmonization. This approach has been used to harmonize (POC) with commercial laboratory analyzers (CL) with > 90 paired feline plasma samples. OBJECTIVE: Our aims were to use 20 pairs of results to derive regression equations to harmonize the POC results with those of the CL. Harmonized results were assessed by recognized quality goals. METHODS: Systematic sampling was used to choose the 20 samples used to calculate regression parameters (with three techniques). These were applied to the remainder of the original dataset to harmonize the POC results to those from the CL. POC results were assessed for bias and the number of results reaching quality goals before and after adjustment. RESULTS: Regression from 20 paired samples was able to harmonize POC analyzer results to a commercial laboratory analyzer for 16 feline plasma biochemistry measurands. CONCLUSIONS: Most analytes could be harmonized by this approach. Reasons for harmonization failure were inadequate concentration range of patient results (AST, lipase, total bilirubin, GGT), potential non-linear relationship (lipase), and no clinical performance goal (globulins). Additional studies using samples with clinically important values may overcome these limitations, and similar studies of other species and other types of point-of-care analyzers would be of interest.