Use of routine clinical laboratory data to define reference intervals

Abstract

Background Reference intervals are used to distinguish between healthy and diseased state. Ideally, they are defined using specimens only from ‘healthy’ individuals, but this is often difficult or impossible. In order to use routine clinical laboratory data, outliers must be removed before the underlying distribution and changes related to age and sex can be modelled. This paper illustrates the process for plasma alkaline phosphatase (ALP). ALP levels are high in infancy and childhood, peak in adolescence, are stable from the early 20s and rise after the fourth decade. Three types of normalizing transformations (Logarithmic, Box-Cox and Cole's LMS) are compared. Methods Single ALP results from 75,328 individuals aged 0–80 years were binned by sex and age. The normalizing transformations were applied to each bin, outliers were removed and the normalizing transformations were reapplied to the remaining data. The normality of the transformed data was assessed by normal score plots and the Kolmogorov-Smirnov test. Fractional polynomials were used to model the underlying parameters of the transformations and the derived parametric reference intervals (mean ± 1.96 standard deviations), separately for each sex as a whole and partitioned into two or three age ranges, with overlapping to give smooth transitions. Results All transformations yielded acceptably normal data, but the LMS method gave the closest approximation to normal. Outlier rates were similar for each method. The derived reference ranges were similar for all the three methods. Splitting the data-set into several segments resulted in a better fit with the peak seen in adolescence. Conclusion Routine clinical laboratory specimens can be used to derive reference intervals.