Big data and reference intervals

Although reference intervals (RIs) play an important role in clinical diagnosis, there remain significant differ-ences with respect to race, gender, age and geographic location. Accordingly, the Clinical Laboratory Standards Institute (CLSI) EP28-A3c has recommended that clinical laboratories establish RIs appropriate to their subject population. Unfortunately, the traditional and direct approach to establish RIs relies on the recruitment of a sufficient number of healthy individuals of various age groups, collection and testing of large numbers of specimens and accurate data interpretation. The advent of the big data era has, however, created a unique opportunity to mine laboratory information. Unfortunately, this indirect method lacks standardization, consensus support and CLSI guidance. In this review we provide a historical perspective, comprehensively assess data processing and statistical methods, and post-verification analysis to validate this big data approach in establishing laboratory specific RIs.

Automated summary

Although reference intervals are important in clinical diagnosis, there are significant differences in various factors. Traditional methods of establishing reference intervals have limitations, but the advent of big data offers a unique opportunity to establish specific reference intervals for clinical laboratories.