Reference intervals: an update

Reference intervals serve as the basis of laboratory testing and aid the physician in differentiating between the healthy and diseased patient. Standard methods for determining the reference interval are to define and obtain a healthy population of at least 120 individuals and use nonparametric estimates of the 95% reference interval. This method is less accurate if the group size is significantly less and does not allow for exclusion of outliers. In order to overcome these limitations many authors in the current literature report reference intervals after arbitrary truncation of the data or use inappropriate parametric calculations. We argue that the use of outlier removal and robust estimators, with or without transformation to normality, address the shortcomings of the standard method and eliminate the need for employing less valid methods. To test these methods of analysis well-defined test groups are required. In a few studies physician-determined health status is provided for each subject along with commonly measured analytes. The NHANES and Fernald studies provide such groups. With such data it is possible to show the range of effects on the reference interval width by including a known non-healthy subgroup. With the NHANES data the effect ranged from negligible to a 30% increase in reference interval width. We found that use of outlier detection with the robust estimator yielded reference intervals that were closer to those of the true healthy group. Another issue is one of demographics. That is, whether or not one should derive separate reference intervals for different demographic groups, e.g., males and females. The standard mathematical test for deriving separate reference intervals is due to Harris and Boyd. Using the NHANES data we examined 33 analytes for each of three ethnic groups (separated by genders). We used the Harris and Boyd procedure and observed that it was necessary to derive separate reference intervals for approximately 30% of the comparisons. The most notable analytes were glucose and gamma GT. The methods used by most laboratories have similar precision, identical units, are linearly related (often on a 1:1 basis) and correlate well with each other. As a result the only difference is the method bias. By using the reference interval width, this bias is eliminated. We argue that the log ratio of the reference interval widths is a good estimate of the variability between groups.