Estimation of Reference Intervals from Routine Data Using the refineR Algorithm—A Practical Guide

Abstract

Abstract Background Accurate reference intervals are essential for the interpretation of laboratory test results. Typically, they are determined by the central 95% range of test results from a predefined reference population. As these direct studies can face practical and ethical challenges, indirect methods using routine measurements offer an alternative approach. Methods We provide step-by-step guidance on how to apply an indirect method in practice using refineR, the most recently published indirect method, and showcase the application by evaluating real-world data of 12 prespecified analytes. Measurements were retrieved from ARUP Laboratories’ data warehouse, and were obtained from routine patient testing on cobas c502 or e602 analyzers. Test results were prefiltered and cleaned and, if necessary, physiologically partitioned prior to estimating reference intervals using refineR. Estimated reference intervals were then compared to established intervals provided by the manufacturer. Results For most analytes, the reference intervals estimated by refineR were comparable to those provided by the manufacturer, shown by overlapping confidence intervals at both reference limits, or only the upper or lower limit. For thyroid-stimulating hormone, refineR estimated higher reference limits, while estimates for prealbumin were lower compared to the established reference interval. Conclusions We applied the refineR algorithm to a variety of real-world data sets resulting in reference intervals similar to intervals previously established by direct methods. We further provide practical guidance and a code example on how to apply an indirect method in a real-world scenario facilitating their access and thus their use in laboratory settings.