An automated and objective method for age partitioning of reference intervals based on continuous centile curves.

Reference intervals are the most commonly used decision support tool when interpreting quantitative laboratory results. They may require partitioning to better describe subpopulations that display significantly different reference values. Partitioning by age is particularly important for the paediatric population since there are marked physiological changes associated with growth and maturation. However, most partitioning methods are either technically complex or require prior knowledge of the underlying physiology/biological variation of the population. There is growing interest in the use of continuous centile curves, which provides seamless laboratory reference values as a child grows, as an alternative to rigidly described fixed reference intervals. However, the mathematical functions that describe these curves can be complex and may not be easily implemented in laboratory information systems. Hence, the use of fixed reference intervals is expected to continue for a foreseeable time. We developed a method that objectively proposes optimised age partitions and reference intervals for quantitative laboratory data (https://research.sph.nus.edu.sg/pp/ppResult.aspx), based on the sum of gradient that best describes the underlying distribution of the continuous centile curves. It is hoped that this method may improve the selection of age intervals for partitioning, which is receiving increasing attention in paediatric laboratory medicine.