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Preface
The need for pediatric reference intervals is quite evident, as children and adolescents undergo a series of remarkable changes in growth, organ development, and sexual maturity from the time they are born to the time they become adults. For example, neonates and premature infants start off with immature hepatic, renal, and pulmonary function which can affect the way a wide variety of medications are metabolized. The rapid endocrine changes evident during puberty are a testament to the need for well-defined reference intervals in this age group. Thus, without appropriate reference intervals, laboratory testing becomes an inexact endeavor in futility.
How Do We Approach the Definition of Normal Reference Intervals?
In the ideal situation, samples would be collected directly from a large number of healthy children of all age groups, the laboratory would test each sample, and those results would be used to determine statistically relevant reference intervals for the appropriate age partitions. However, we know this is not an easy task. Children are considered vulnerable subjects from a research perspective and, therefore, have limitations on the total volume of blood that can be safely collected from them. Strict protocols for consent/assent to participate in research studies may be challenging to administer to children and require parent or guardian involvement. Additionally, participants need to be carefully screened for disorders that might bias any measured results (1). The difficult nature of phlebotomy, particularly in younger children, can also cause emotional stress on parents who might not want their child to participate in a potentially painful procedure with no immediate benefit. Because of these and other practical challenges, it is often difficult to obtain adequate sample volumes from at least 120 healthy individuals to achieve a statistically accurate normative reference interval for an age partition. That challenge is compounded by the need for multiple age and sex partitions in growing and maturing children. Thus, relatively large studies with “healthy” volunteers are particularly difficult to perform but provide reassurance in the statistical determination of reference intervals that typically encompass the 2.5th to 97.5th percentiles of the reference population data (1).
Statistical Approaches Used to Determine Reference Intervals
In lieu of the preferred approach of prospective collection of large sample numbers described above, several investigators have derived or used statistical methods on large databases of existing laboratory results to retrospectively determine appropriate reference intervals. One notable approach that has been included in previous editions of this book is the Hoffmann approximation (2). This approach typically uses either Chauvenet’s or Dixon’s criteria for removing outliers and involves plotting % cumulative frequency versus the laboratory value (or log of the value, if non-Gaussian distribution). Using this approach, one obtains a straight line typically within the central portion of the plotted curve. This straight line can be extrapolated to provide the 2.5th and 97.5th percentiles for the population being studied. An example for total iron-binding capacity is shown in Fig. 1(3). This approach is simplistic overall and has particular appeal in populations with limited data available from fresh sample collections from volunteers, such as pediatrics. Disadvantages include the need to be very selective in the population being considered since patients typically have testing performed due to a clinical concern and may not represent a “healthy” population. Other disadvantages include the need to carefully select appropriate age interval(s) and the need for a very large number of data points in order to perform these statistical calculations for reference interval derivation. There has been a recent validation of the Hoffmann approximation using a large database from a nationwide chain of clinical reference laboratories in the United States, without exclusions or filtering (4).
A major criticism of the Hoffman approximation is the observation of narrower than expected reference intervals (5). There have, therefore, been a number of statistical ap- proaches that have attempted to improve upon the Hoffman approximation. These include the Bhattacharya graphical method which involves the identification of Gaussian distribu- tions within a distribution (6). Another is a computational (nongraphical) strategy using maximum likelihood through the expectation-maximization algorithm (7). As reviewed by Holmes and Buhr (5), however, all of these methods should be considered to be “indirect” estimates of reference intervals in healthy children and should be verified using data from such individuals.
Another method that has recently been developed is the use of continuous pediatric reference curves for chemistry analytes (8). The advantages of this approach are that it provides a continuum of upper and lower reference intervals and avoids the use of reference
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