Transference and Validation of Reference Intervals

Reference intervals are the most widely used decision-making tool in laboratory medicine and serve as the basis for many of the interpretations of pathology results. Although laboratories are well versed in method verification and validation to assess if methods are fit-for-purpose, less importance is commonly put on selecting the most appropriate reference interval to determine whether an individual is healthy.Sources of reference intervals can vary from the lowest level of current publications on methodology, e.g., manufacturers' package inserts, to published professional recommendations by national or international expert bodies, expert local groups, or individuals, to the more robust reference interval derived from apparently healthy populations. Health-associated reference intervals including shared (i.e., common or harmonized) reference intervals can be determined by formal (direct) studies using a local population and specified preanalytical conditions or by (indirect) data mining drawn from a local population in which specified preanalytical conditions are used.The advantage of large, well-conducted direct studies is the well-defined reference population with exclusion of diseased subjects, optimum control of preanalytical variables, robust statistical analysis to remove outlier values, and narrow confidence limits around the obtained reference limits. Such is the case with the joint collaboration of the Canadian Laboratory Initiative on Pediatric Reference Intervals (CALIPER)4 and the Canadian Health Measures Survey (CHMS), which collected 11 999 biospecimens representative of 96% of Canada's household population covering the ages 3–79 years and supported by other physical measures and general health information (1). Before the calculation of intervals, subjects were first excluded according to various parameters including increased body mass index, history of chronic or metabolic disease, acute illness, or use of prescribed medication a month before sample collection. Next there was statistical removal of outlier values followed by nonparametric ranking analysis to calculate pediatric (3–18 years), adult (19–59 years), and geriatric (60–79 …