Laboratory data can be meaningful only when compared with reliable reference data; therefore, the estimation of reliable reference data is just as important as the accurate measurement of measurands in patient samples. Since analyte concentrations in the human body are influenced by both random variations (such as biological fluctuations) and systematic variations (such as physiological rhythms and age-related changes), the conventional model for estimating reference data - based solely on the statistical distribution of single-sample measurements from reference individuals - may not provide sufficiently reliable information for interpreting patient results. Therefore, a paradigm shift from relying solely on single-sample measurement distributions to incorporating metabolic changes observed in the human body when estimating reference intervals may enhance the clinical value of laboratory data for an effective clinical decision making and patient care. This opinion paper aims to summarize how to facilitate this transition and to identify the most suitable model for estimating reference intervals that reflect underlying metabolic dynamics.
Coşkun et al. (Sat,) studied this question.
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