Laboratory Testing in Infants and Children

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Chapter 707 Laboratory Testing in Infants and Children

Reference intervals, more commonly known as normal values, are difficult to establish within the pediatric population. Differences in genetic composition, physiologic development, environmental influences, and subclinical disease are variables that need to be considered when developing reference intervals. Other considerations for further defining reference intervals include partitioning based on sex and age. The most commonly used reference range is generally given as the mean of the reference population ±2 standard deviations (SD). This is acceptable when the distribution of results for the tested population is essentially gaussian. The serum sodium concentration in children, which is tightly controlled physiologically, has a distribution that is essentially gaussian; the mean value ±2 SD gives a range very close to that actually observed in 95% of children (see Table 707-1 on the Nelson Textbook of Pediatrics website at www.expertconsult.com image). However, not all analytes have a gaussian distribution. The serum creatine kinase level, which is subject to diverse influences and is not actively controlled, does not show a gaussian distribution, as evidenced by the lack of agreement between the range actually observed and that predicted by the mean value ±2 SD. In these cases, a reference interval defining the 2.5-97.5th percentiles are typically used.

Table 707-1 GAUSSIAN AND NONGAUSSIAN LABORATORY VALUES IN 458 NORMAL SCHOOL CHILDREN 7-14 YR OF AGE

  SERUM SODIUM (mmol/L) SERUM CREATINE KINASE (U/L)
Mean 141 68
SD 1.7 34
Mean ± 2 SD 138-144 0-136
Actual 95% range 137-144 24-162

SD, standard deviation.

Reference cutoffs are typically established from large studies with a large reference population. Examples of these cutoffs are illustrated by reference cutoffs established for cholesterol, lipoproteins, and neonatal bilirubin. Patient results exceeding these cutoffs have a future risk of acquiring disease. A final modification needed for reporting reference intervals is referencing the Tanner stage of sexual maturation, which is most useful in assessing pituitary and gonadal function.

The establishment of common reference intervals remains an elusive target. While some patient results are directly comparable between laboratories and methods, most are not. Careful interpretation of patient results must consider when testing was performed and what method was used. Higher order methods, methods that are more accurate and precise, continue to be slowly developed. These will be critical to the standardization of tests and the establishment of common reference intervals.

Accuracy and Precision of Laboratory Tests

Technical accuracy, or trueness, is an important consideration in interpreting the results of a laboratory test. Because of improvements in methods of analysis and elimination of analytic interference, the accuracy of most tests is limited primarily by their precision. Accuracy is a measure of the nearness of a test result to the actual value, whereas precision is a measure of the reproducibility of a result. No test can be more accurate than it is precise. Analysis of precision by repetitive measurements of a single sample gives rise to a gaussian distribution with a mean and an SD. The estimate of precision is the coefficient of variation (CV):

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The CV is not likely to be constant over the full range of values obtained in clinical testing, but it is approximately 5% in the normal range. The CV is generally not reported, but is always known by the laboratory. It is particularly important in assessing the significance of changes in laboratory results. For example, a common situation is the need to assess hepatotoxicity incurred as a result of the administration of a therapeutic drug and reflected in the serum alanine aminotransferase (ALT) value. If serum ALT increases from 25 U/L to 40 U/L, is the change significant? The CV for ALT is 7%. Using the value obtained ±2 × CV to express the extremes of imprecision, it can be seen that a value of 25 U/L is unlikely to reflect an actual concentration of >29 U/L, and a value of 40 U/L is unlikely to reflect an actual concentration of <34 U/L. Therefore, the change in the value as obtained by testing is likely to reflect a real change in circulating ALT levels. Continued monitoring of serum ALT is indicated, even though both values for ALT are within normal limits. Likely in this case is only a probability. Inherent biologic variability is such that the results of 2 successive tests may suggest a trend that will disappear on further testing.

The precision of a test may also be indicated by providing confidence limits for a given result. Ordinarily, 95% confidence limits are used, indicating that it is 95% certain that the value obtained lies between the 2 limits reported. Confidence limits are calculated using the mean and SD of replicate determinations:

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where t is a constant derived from the number of replications. In most cases, t = 2.

Accuracy is expressed by determining the difference, or bias, between results from a comparative method and a definitive or reference method. A definitive or reference method provides results with increased precision and accuracy compared to the clinical laboratory. When these methods are used, along with highly purified materials (i.e., Standard Reference Materials from the National Institute of Standards and Technology) to establish values for assay calibrators used in the clinical laboratory, the accuracy of patient results is improved. Creatinine, hemoglobin A1c, and neonatal bilirubin are examples in which the accuracy of these tests has been improved.

Predictive Value of Laboratory Tests

Predictive value (PV) theory deals with the usefulness of tests as defined by their clinical sensitivity (ability to detect a disease) and specificity (ability to define the absence of a disease).

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The problems addressed by PV theory are false-negative and false-positive test results. Both are major considerations in interpreting the results of screening tests in general and neonatal screening tests in particular.

Testing for HIV seroreactivity illustrates some of these considerations. If it is assumed that approximately 1,100,000 of 284,000,000 residents of the USA are infected with HIV (prevalence = 0.39%) and that 90% of those infected demonstrate antibodies to HIV, then we can consider the usefulness of a simple test with 99% sensitivity and 99.5% specificity. If the entire population of the USA were screened, it would be possible to identify most of those infected with HIV.

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However, there will be 119,900 false-negative test results. Even with 99.5% specificity, the number of false-positive test results would be larger than the number of true-positive results:

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In addition, there will be 281,480,000 true-negative results.

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Given the high cost associated with follow-up and the anguish produced by a false-positive result, it is easy to see why universal screening for HIV seropositivity received a low priority immediately after the introduction of testing for HIV infection.

By contrast, we can consider the screening of 100,000 individuals from groups at increased risk for HIV in whom the overall prevalence of disease is 10%, with all other considerations being unchanged.

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