Chemistry & Hematology Reference Intervals | Current Diagnosis & Treatment: Pediatrics, 25e | AccessPediatrics

INTRODUCTION

Laboratory tests provide valuable information necessary to evaluate a patient’s condition and to monitor recommended treatment. Chemistry and hematology test results are compared with those of healthy individuals or those undergoing similar therapeutic treatment to determine clinical status and progress. In the past, the term normal ranges relayed some ambiguity because statistically; the term normal also implied a specific (Gaussian or normal) distribution, and epidemiologically it implied the state of the majority, which is not necessarily the desirable or targeted population. This is most apparent in cholesterol levels, where values greater than 200 mg/dL are common, but not desirable. Use of the term reference range or reference interval is therefore recommended by the International Federation of Clinical Chemistry (IFCC) and the Clinical and Laboratory Standards Institute (CLSI) to indicate that the values relate to a reference population and clinical condition.

Reference ranges are established for a specific age, sex, and sexual maturity; they are also defined for a specific pharmacologic status, dietary restrictions, and stimulation protocol. Similarly, diurnal variation is a factor as is degree of obesity. Some reference ranges are particularly meaningful when combined with other results (eg, parathyroid hormone and calcium), or when an entire set of analytes is evaluated.

Laboratory tests are becoming more specific and measure much lower concentrations than ever before. Therefore, reference ranges should reflect the analytical procedure as well as reagents and instrumentation used for a specific analysis. As test methodology continues to evolve, reference ranges are modified and updated.

CHALLENGES IN DETERMINING & INTERPRETING PEDIATRIC REFERENCE INTERVALS

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The pediatric environment is particularly challenging for the determination of reference intervals since growth and developmental stages do not have a distinct and finite boundary by which test results can be tabulated. Furthermore, adult reference ranges are not always appropriate for pediatric patients. Reference ranges may overlap and, in many cases, complicate diagnosis and treatment. Collection and allocation of test results by age for the purpose of establishing a reference range is a convenient and manageable way to report them, but caution is needed in their interpretation and clinical correlation. Additionally, ethical concerns may exist related to blood draws in infants and young children to establish these reference ranges. Despite these challenges, there have been multicenter studies to improve the reference ranges for laboratory testing in pediatrics.

A particular difficulty lies in establishing reference ranges for analytes whose levels are changed under scheduled stimulation conditions. The common glucose tolerance test is such an example, but more complex endocrinology tests require skill and extensive experience to interpret. Reference ranges for these serial tests are established over a long period of time and are not easily transferable between test methodologies.

Adeli K: Special issue on laboratory reference intervals. eJIFCC. September 2008. http://www.ifcc.org/PDF/190201200801.pdf.

C28A3: Defining, Establishing, and Verifying Reference Intervals in the Clinical Laboratory: Approved Guideline. 3rd ed. http://www.clsi.org/source/orders.

Ozarda Y: Reference intervals: current status, recent developments and future considerations. Biochem Med 2016;26(1):5–16
[PubMed: 26981015] .

GUIDELINES FOR USE OF DATA IN A REFERENCE RANGE STUDY

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The College of American Pathologists provides guidelines for the adoption of reference ranges used in hospitals and commercial clinical laboratories. It recognizes the enormous task of establishing a laboratory’s own reference ranges and recommends alternatives to the process. A laboratory may acquire reference ranges by:

Conducting its own study to evaluate a statistically significant number of “healthy” volunteers. It is a monumental task for a laboratory to develop its own pediatric reference ranges due to the need for parental consent, approval by review boards, and the numerous age categories that need to be evaluated.
Adopting ranges established by the manufacturer of a particular analytical instrument. The laboratory must validate the data by analyzing a sample of 20 subjects representing that specific population to confirm that the adopted range is truly representative of that group.
Using reference data in the general medical literature and conferring with physicians to make sure the data agree with their clinical experience. A validation study is also recommended.
Analyzing hospital patient data. Laboratory test results from hospital patients have been used to compute reference ranges provided they fulfill stated clinical criteria. Patient records need to indicate that the patient’s specific medical condition does not influence the analyte whose reference range is being determined. For example, a child undergoing surgery for bone fracture repair is expected to have normal electrolytes and thyroid function, whereas a child examined for precocious puberty should not be included in a reference range study for luteinizing hormone.

Statistically, the sample size of a hospital patient study should be considerably larger than that of a healthy group. A study from a healthy population may require 20 subjects to be statistically significant, whereas a hospital population should evaluate a minimum of 120 patients.

Biological Variation Database Reference List: http://www.westgard.com/biological-variation-database-reference-list.htm.

College of American Pathologists publication: http://www.cap.org/apps/docs/laboratory_accreditation/sample_checklist.pdf.

Schnabl K, Chan MK, Gong Y, Adeli K: Closing the gap on paediatric reference intervals: the CALIPER initiative. Clin Biochem Rev 2008 Aug;29(3):89–96
[PubMed: 19107221] .

STATISTICAL COMPUTATION OF REFERENCE INTERVALS

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The establishment of reference intervals is based on a statistical distribution of test results obtained from a representative population. The CLSI recommendation for data collection and statistical analysis provides guidelines for managing the data. For clinicians, it is not important that they can reproduce the calculation. It is far more critical to understand the benefits and restrictions provided by the described statistical approaches and to evaluate patient results with these limitations in mind.

The reference range includes 95% of all results obtained from a representative population. Note that 5% of that population will have “abnormal” results, when in fact they are “healthy” and an integral part of the reference group study. Similarly, an equivalent 5% of the “ill” population will have laboratory results within the reference range. These are inherent features of the statistical computation. Taking that analysis one step further, the probability of a healthy patient having a test result within a calculated reference range is

P = .95

When multiple tests or panels of tests are used, the combined probability of all the test results falling in their respective reference ranges drops dramatically. For example, the probability of all results from 10 tests in the complete metabolic panel being in the reference range is

P = (.95)¹⁰ = .60

Therefore, about one-third of healthy patients will have one test result in the panel that is outside the reference range. Clinical judgment is needed to determine the significance of test results falling outside the reference range.

A. Parametric Method of Computation

The parametric method of establishing reference intervals is simple, though not always representative, since it is based on the assumption that the data have a Gaussian distribution. A mean (x) and standard deviation (SD) are calculated; test results of 95% of that specific population will fall within the mean ±1.96 SD, as shown in Figure 46–1.

Figure 46–1.

Gaussian distribution and parametric calculation using x ± 1.96 SD to define the range.

A graph shows a distribution curve, marking acceptance and rejection areas.

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Where the distribution is not Gaussian, a mathematical manipulation of the values (eg, plotting the log of the value, instead of the value itself) may give a Gaussian distribution. The mean and SD are then converted back to give a usable reference range.

B. Nonparametric Method of Computation

The nonparametric method of establishing reference ranges is currently recommended by CLSI, since it defines outliers as those in the extreme 2.5 percentile of the upper and lower limits of data, respectively. The number of data points excluded at the limits depends on the skew of the curve, and so the computation accommodates a non-Gaussian distribution. A histogram depicting the non-Gaussian distribution of data from a free thyroxine reference range study conducted at Children’s Hospital in Denver is shown in Figure 46–2.

Figure 46–2.

Histogram of free thyroxine (FT₄) using clinic and hospital patients at Children’s Hospital Colorado.

A bar graph measures change in frequency of free thyroxine dosages.

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Ichihara K, Boyd JC; IFCC Committee on Reference Intervals and Decision Limits (C-RIDL): An appraisal of statistical procedures used in derivation of reference intervals. Clin Chem Lab Med 2010 Nov;48(11):1537–1551
[PubMed: 21062226] .

WHY REFERENCE INTERVALS VARY

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Recent modifications to reference ranges are due to the introduction of new and improved analytical procedures, advanced automated instrumentation, and standardization of reagents and reference materials. Reference ranges are also affected by preanalytical variations that can occur during sample collection, processing, and storage.

Preanalytical variations of biological origin can occur when specimens are drawn in the morning versus in the evening, or from hospitalized recumbent patients versus ambulatory outpatients. Variations also may be caused by metabolic and hemodynamic factors. Preanalytical factors may be a product of the socioeconomic environment or ethnic background (eg, genetic or dietary).

Analytical variations are caused by differences in analytical measurements and depend on the analytical tools as well as an inherent variability in obtaining a quantitative value. Furthermore, new reagents, instruments, and improved testing procedures added to the clinical laboratory can result in an element of variability between tests.

Antigen-antibody reactions have revolutionized clinical chemistry but have also added a degree of variability because biologically derived reagents have different specificity and sensitivity. In addition to the targeted analyte, some of its metabolites are also measured, and these may or may not be biologically active.
Reference materials continue to be reviewed and evaluated by organizations such as the World Health Organization and the National Institute for Standards and Technology.
Analytical instrumentation with advanced electronics and robotics has improved the accuracy of results and increased throughput. However, they have added an element of variability between instruments from different manufacturers.
Analytical detection methods have also made big strides as they have expanded from simple ultraviolet-visible spectrophotometry to fluorescence, nephelometry, radioimmunoassay, and chemiluminescence.

Jung B, Adeli K: Clinical laboratory reference intervals in pediatrics: the CALIPER initiative. Clin Biochem 2009 Nov;42 (16–17):1589–1595. Epub 2009 Jul 7
[PubMed: 19591815] .

SENSITIVITY, SPECIFICITY, & PREDICITIVE VALUES

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Despite its statistical derivation, a reference interval does not necessarily provide a finite and clear-cut guideline as to whether a patient has a condition. There will always be a segment of the population with test values that fall within the reference interval, but clinical manifestations that indicate disease is present. Similarly, a segment of the population will have test values outside the reference interval, but no clinical signs of disease. The ability of a test and corresponding reference interval to detect individuals with disease is defined by the diagnostic sensitivity of the test. Similarly, the ability of a test to detect individuals without disease is described by the diagnostic specificity. These characteristics are governed by the analytical quality of the test as well as the numerical parameters (reference interval) that define the presence of disease. The tolerance level for the desired sensitivity and specificity of a test requires significant input from clinicians.

Generally, specificity increases as sensitivity decreases. A typical distribution of test results, shown in Figure 46–3, provides information on individuals without disease (solid line) and individuals with the disease (dashed line). As with most tests, there is an overlap area. A patient with a test result of 1 is likely healthy, and the result indicates a true negative (TN) for the presence of disease. A patient with a test result of 9 is likely to have the disease, and the test result is a true positive (TP). There is a small, but significant, population with a test result of 2–5 in whom the test is not 100% conclusive. A statistical analysis may determine the most likely cutoff for healthy individuals, but the clinically acceptable cutoff depends on the test as well as clinical correlation.

Figure 46–3.

Frequency distribution of test results for patients with and without disease. FN, false negative; FP, false positive; TN, true negative; TP, true positive.

A graph shows the frequency distribution curves of test results for patients with and without disease.

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If cutoff values for the reference interval are such that a test result indicates that a healthy patient has the disease, the result is a false positive (FP). Conversely if a test result indicates that a patient is well when in fact he or she has the disease, the result is a false negative (FN). To define the ability of the test and reference interval to identify a disease state, the diagnostic sensitivity and specificity are measured.

In the example shown in Figure 46–3, a reference interval of 0.5–3 will provide more TN results and minimize FP results. Alternatively, a reference interval of 0.5–4 will increase the rate of FN. Thus, an increase in sensitivity leads to a decrease in specificity. A medical condition that requires aggressive treatment may necessitate a test and corresponding reference interval with a high sensitivity, which is a measure of the TP rate. This is accomplished at the expense of lowering specificity.

One must also consider that both diagnostic sensitivity and specificity as derived do not take into account disease prevalence. As shown in Figure 46–4, diagnostic sensitivity is calculated exclusively within a diseased population and the converse is true for diagnostic specificity. In a clinical setting, one is screening a population of individuals with and without disease. Therefore, positive predicative value (PPV) and negative predictive value (NPV) must also be used to better understand test screening performance and are defined as follows:

A reference interval is a statistical representation of test results from a finite population, but it is by no means inclusive of every member of the group. It is merely one component in the measure of a patient’s status to be viewed in relation to a number of other testing factors.

Figure 46–4.

Sensitivity, specificity, and predictive values of a test. FN, false negative; FP, false positive; NPV, negative predictive value; PPV, positive predictive value; TN, true negative; TP, true positive.

A table depicts the diagnostic sensitivity and specificity values of a test.

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PEDIATRIC REFERENCE INTERVALS

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The establishment of reference ranges is a complex process. Assumptions are made in the management of data processes, regardless of the population used for the accumulation of test results. Analytical instrument manufacturers conduct large studies to identify reference intervals for each specific analyte, and pediatric values have always been the most challenging. Some of the manufacturers’ recommended reference intervals are listed in Table 46–1 for general chemistry, Table 46–2 for endocrinology, and Table 46–3 for hematology. The interpretation of chemistry and hematology laboratory results is equally complex and forms a continuous challenge for physicians and the medical community at large.

Table 46–1.General chemistry.

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Table 46–1. General chemistry.

Analyte, Units Specimen Type

Age

Instrument

Male Range

Female Range

A_1C hemoglobin (%) B

0 day–Adult

DCA Vantage

Normal: < 5.7%

Prediabetes: 5.7%–6.5%

Diabetes: > 6.5%

Normal: < 5.7%

Prediabetes: 5.7%–6.5%

Diabetes: > 6.5%

α-Fetoprotein (IU/mL)

S, P

0–30 days

1–3 mo

4 mo–18 y

> 18 y

Vitros 5600

50–100,000

40–1000

0–12

< 7.5

50–100,000

40–1000

0–12

< 7.5

α₁-Antitrypsin (mg/dL)

S, P

0–1 mo

1–6 mo

6 mo–2 y

2–19 y

> 19 y

Vitros 5600

79–223

71–190

60–161

70–179

88–183

79–223

71–190

60–161

70–179

88–183

Anti-streptolysin O (IU/mL)

S, P

0–19 y

> 19 y

Vitros 5600

< 241

< 200

< 241

< 200

Albumin (g/dL)

S, P

0–7 days

8–30 days

1–2 mo

3–5 mo

6–12 mo

1–3 y

4–6 y

7–18 y

≥ 19 y

Vitros 5600

2.3–3.8

2.0–4.5

2.0–4.8

2.1–4.9

2.1–4.7

3.4–4.2

3.5–5.2

3.7–5.6

3.5–5.0

1.8–3.9

1.8–4.4

1.9–4.2

2.2–4.4

2.2–4.7

3.4–4.2

3.5–5.2

3.7–5.6

3.5–5.0

Allergens (KUA/L) S, P

0 day–Adult

Phadia ImmunoCAP

0–0.34

ALP (U/L)

S, P

0–7 days

8–30 days

1–3 mo

4–6 mo

7–12 mo

1–3 y

4–6 y

7–9 y

10–11 y

12–13 y

14–15 y

16–18 y

≥ 19 y

Vitros 5600

77–265

91–375

60–360

55–325

60–300

129–291

134–346

156–386

120–488

178–455

116–483

58–237

38–126

65–270

65–365

80–425

80–345

60–330

129–291

134–346

156–386

116–515

93–386

62–209

45–116

38–126

ALT (U/L)

S, P

0–3 y

4–13 y

14–18 y

≥ 19 y

Vitros 5600

12–45

10–41

11–26

< 50

14–45

11–28

10–35

5–34

Ammonia (µmol/L)

P

0–1 days

1–13 y

14 d–17 y

≥ 18 y

Vitros 5600

64–107

56–92

21–50

9–33

64–107

56–92

21–50

9–33

Amylase (U/L)

S, P

0–2 mo

3–5 mo

6–11 mo

1–18 y

≥ 19 y

Vitros 5600

0–30

0–50

0–80

30–100

30–110

0–30

0–50

0–80

30–100

30–110

Anticardiolipin IGA (APL)

S

0 day–Adult

Inova Bio-Flash

< 20

Anticardiolipin IGG (GPL)

S

0 day–Adult

Inova Bio-Flash

< 20

Anticardiolipin IGM (MPL)

S

0 day–Adult

Inova Bio-Flash

< 20

ASCA IGA (units)

S

0 day–Adult

Inova DSX

< 20

ASCA IGG (units)

S

0 day–Adult

Inova DSX

< 20

AST (U/L)

S, P

0–7 days

8–30 days

1–3 mo

4–6 mo

7–12 mo

1–3 y

4–6 y

7–9 y

10–11 y

12–15 y

16–18 y

≥ 19 y

Vitros 5600

30–100

20–70

22–63

13–65

25–55

20–60

15–50

15–40

10–60

15–40

15–45

17–59

24–95

24–72

20–64

20–63

22–63

20–60

15–50

15–40

10–40

10–30

5–30

14–36

Bilirubin direct (mg/dL)

S, P

0–30 days

> 1 mo

Vitros 5600

0–0.6

0–0.3

0–0.6

0–0.3

Bilirubin total (mg/dL)

S, P

0–1 days

1–2 days

3–5 days

6–30 days

> 1 mo

Vitros 5600

0.1–5.8

0.1–8.5

0.1–11.5

< 11.5

0.2–1.2

0.1–5.8

0.1–8.5

0.1–11.5

< 11.5

0.2–1.2

Bilirubin indirect (mg/dL)

S, P

0–30 days

> 1 mo

Vitros 5600

0–0.6

0–0.3

0–0.6

0–0.3

BNP (ng/L)

Whole blood (purple)

0 day–Adult

I–STAT

0–99

Pro-BNP (pg/mL)

S, P

0 day–Adult

Vitros 5600

0–125

BUN (mg/dL)

S, P

0–7 days

8–30 days

1–3 mo

4–6 mo

7–12 mo

1–3 y

4–13 y

14–18 y

≥ 19 y

Vitros 5600

2–13

2–16

2–12

1–14

2–14

5–17

7–17

8–21

9–20

2–13

2–15

2–14

1–13

5–17

7–17

8–21

7–17

C3 (mg/dL)

S, P

0–1 mo

1–2 mo

2–3 mo

3–4 mo

4–5 mo

5–6 mo

6–9 mo

9–11 mo

11–12 mo

1–2 y

2–3 y

3–5 y

5–8 y

8–10 y

10–19 y

> 19 y

Vitros 5600

55–129

61–155

67–136

64–182

67–174

77–178

78–173

76–187

87–181

84–177

80–178

89–173

92–161

93–203

86–184

88–165

55–129

61–155

67–136

64–182

67–174

77–178

78–173

76–187

87–181

84–177

80–178

89–173

92–161

93–203

86–184

88–165

C4 (mg/dL)

S, P

0–1 mo

1–2 mo

2–3 mo

3–4 mo

4–5 mo

5–6 mo

6–9 mo

9–11 mo

11–12 mo

1–2 y

2–3 y

3–5 y

5–8 y

8–10 y

10–19 y

> 19 y

Vitros 5600

9.2–33

9.7–37

11–35

11–50

9.3–47

11–55

12–48

16–51

16–52

12–45

13–47

17–42

16–42

13–52

10–40

14–44

9.2–33

9.7–37

11–35

11–50

9.3–47

11–55

12–48

16–51

16–52

12–45

13–47

17–42

16–42

13–52

10–40

14–44

Ca (mg/dL)

S, P

0–8 days

8–30 days

1–3 mo

3–6 mo

6–12 mo

1–4 y

4–10 y

10–12 y

12–14 y

14–16 y

16–19 y

> 19 y

Vitros 5600

7.3–11.4

8.6–11.7

8.5–11.3

8.3–11.4

7.7–11.0

8.7–9.8

8.8–10.1

8.9–10.1

8.8–10.6

9.2–10.7

8.9–10.7

8.4–10.2

7.5–11.3

8.4–11.9

8.0–11.1

7.7–11.5

7.8–11.1

8.7–9.8

8.8–10.1

8.9–10.1

8.8–10.6

9.2–10.7

8.9–10.7

8.4–10.2

iCa (mmol/L)

B

0–1 day

1–3 days

4–7 days

8 days–1 mo

1 mo–18 y

> 18 y

Radiometer ABL 90 flex

1.1–1.4

1.1–1.5

1.2–1.5

1.3–1.6

1.2–1.4

1.2–1.3

1.1–1.4

1.1–1.5

1.2–1.5

1.3–1.6

1.2–1.4

1.2–1.3

Chloride (mmol/L)

S, P

0–8 days

8 days–6 mo

6–12 mo

1–19 y

> 19 y

Vitros 5600

96–111

96–110

96–108

96–109

98–107

96–111

96–110

96–108

96–109

98–107

Cholesterol (mg/dL)

S, P

0 day–2 mo

2–7 mo

7–12 mo

1–2 y

2–18 y

> 18 y

Vitros 5600

45–177

60–197

89–208

44–181

< 170

< 190

63–198

66–218

74–218

44–181

< 170

< 190

Creatine kinase (U/L)

S, P

0–3 mo

3–12 mo

1–2 y

2–11 y

11–15 y

15–19 y

Vitros 5600

28–300

24–170

27–160

30–150

33–145

42–470

26–240

24–175

30–170

27–140

Creatinine (mg/dL)

S, P

0–3 days

3–10 days

10–17 days

17 days–1 y

1–11 y

11–18 y

> 18 y

Vitros 5600

0.33–1.08

0.14–0.90

0.14–0.61

0.14–0.52

0.23–0.61

0.42–0.90

0.71–1.18

0.33–1.08

0.14–0.90

0.14–0.61

0.14–0.52

0.23–0.61

0.42–0.90

0.52–0.99

Cystatin C (mg/L)

S, P

0–3 mo

4–12 mo

> 1 y

Vitros 5600

0.8–2.3

0.7–1.5

0.5–1.3

0.8–2.3

0.7–1.5

0.5–1.3

Ferritin (ng/mL)

S,P

0–6 wk

7 wk–1 y

1–10 y

10–19 y

19–50 y

> 18 y

> 50 y

Vitros 5600

< 400

10–95

10–60

10–300

n/a

18–444

n/a

< 400

10–95

10–60

10–70

6–137

n/a

11–264

Anti-deamidated gliadin peptide IgA

0 day–Adult

Bio-Flash

< 20

Anti-deamidated gliadin peptide IgG

0 day–Adult

Bio-Flash

< 20

GGT (U/L)

S, P

0–8 days

8–30 days

1–4 mo

4–7 mo

7–12 mo

1–4 y

4–7 y

7–10 y

10–12 y

12–14 y

14–16 y

16–19 y

> 19 y

Vitros 5600

25–148

23–153

17–130

8–83

10–35

5–16

8–18

11–21

14–25

14–37

10–28

9–29

15–73

19–131

17–124

15–109

10–54

5–16

8–18

11–21

14–23

12–21

12–22

9–23

12–43

Glucose (mg/dL)

S, P

0–30 days

> 1 mo

Vitros 5600

40–80

60–105

40–80

60–105

Glucose (2-h tolerance test) (mg/dL)

S, P

0 day–Adult

Vitros 5600

< 200

Glucose–CSF (mg/dL)

CSF

0 day–Adult

Vitros 5600

40–75

HDL (mg/dL)

S, P

0 day–2 y

2 y–Adult

Vitros 5600

8–61

> 45

8–61

> 45

β₂-Glycoprotein 1-antibody, IgA (SAU)

S

0 day–Adult

Inova Bio-Flash

0–20

β₂-Glycoprotein 1-antibody, IgG (SGU)

S

0 day–Adult

Inova Bio-Flash

0–20

β₂-Glycoprotein 1-antibody, IgM (SMU)

S

0 day–Adult

Inova Bio-Flash

0–20

IgA (mg/dL)

S, P

0–30 days

1–6 mo

6–12 mo

1–4 y

4–7 y

7–10 y

10–13 y

13–16 y

16–19 y

> 19 y

Vitros 5600

0–11

0–40

1–82

9–137

44–187

58–204

46–218

29–251

68–259

70–400

0–10

0–42

6–68

15–111

33–146

28–180

55–193

62–241

69–262

70–400

IgE (kU/L)

S

0–12 mo

1–2 y

2–3 y

3–10 y

10 y–Adult

Phadia ImmunoCAP

0–29

0–49

0–45

0–52

0–87

0–29

0–49

0–45

0–52

0–87

IgG (mg/dL)

S, P

0–30 days

1–6 mo

6–12 mo

1–4 y

4–7 y

7–10 y

10–13 y

13–16 y

16–19 y

> 19 y

Vitros 5600

197–833

140–533

130–823

413–1112

468–1328

582–1441

685–1620

590–1600

522–1703

700–1600

136–872

311–664

325–647

421–1202

560–1319

485–1473

586–1609

749–1640

804–1817

700–1600

IgM (mg/dL)

S, P

0–30 days

1–6 mo

6–12 mo

1–4 y

4–7 y

7–10 y

10–13 y

13–16 y

16–19 y

> 19 y

Vitros 5600

0–65

0–84

15–117

30–146

31–151

21–140

27–151

26–184

28–179

40–230

0–57

0–127

0–130

35–184

42–184

30–165

42–211

34–225

42–224

40–230

Iron (μg/dL)

S, P

0–7 days

7 d–1 y

1–10 y

> 10 y

Vitros 5600

100–250

40–100

50–120

49–181

100–250

40–100

50–120

37–170

Iron-binding capacity (mcg/dL)

S

0 day–Adult

Vitros 5600

261–462

265–497

LDH (U/L)

S, P

0–1 mo

1–4 mo

4–7 mo

7–12 mo

1–4 y

4–7 y

7–10 y

10–12 y

12–14 y

14–16 y

16–18 y

> 18 y

Vitros 5600

550–2100

480–1220

400–1230

380–1200

500–920

470–900

420–750

432–700

470–750

360–730

340–670

313–618

580–2000

460–1150

460–1060

500–920

470–900

420–750

380–700

380–640

390–580

340–670

313–618

LDL measured (mg/dL)

S, P

0 day–Adult

Vitros 5600

< 100

Lead screen (mcg/dL)

Whole blood-purple top

0 day–Adult

LeadCare

< 10

Magnesium (mg/dL)

S, P

0–7 days

7–30 days

1 mo–2 y

2–6 y

6–10 y

10–14 y

> 14 y

Vitros 5600

1.2–1.6

1.6–2.4

1.6–2.6

1.5–2.4

1.6–2.3

1.6–2.2

1.5–2.3

1.2–1.6

1.6–2.4

1.6–2.6

1.5–2.4

1.6–2.3

1.6–2.2

1.5–2.3

iMg (mmol/L)

B

0 day–Adult

Nova pHOx Ultra

0.45–0.60

Non-HDL cholesterol (mg/dL)

S, P

2–18 y

> 18 y

Vitros 5600 (calculated)

< 120

< 150

< 120

< 150

Potassium (mmol/L)

S, P

0–7 days

7 days–3 mo

3 mo–18 y

> 18 y

Vitros 5600

3.7–5.9

4.1–5.3

3.4–4.7

3.5–5.0

3.7–5.9

4.1–5.3

3.4–4.7

3.5–5.0

Prealbumin (mg/dL)

S, P

0–1 mo

1–6 mo

6 mo–4 y

4–6 y

6–14 y

14–19 y

> 19 y

Vitros 5600

7–22

8–34

7–32

12–30

12–42

22–45

17–42

7–22

8–34

7–32

12–30

12–42

22–45

17–42

Phosphorus (mg/dL)

S, P

0–15 days

15 days–1 y

1–5 y

5–13 y

13–16 y

16–19 y

Vitros 5600

5.85–10.9

5.05–8.76

4.52–7.09

4.37–6.25

3.41–5.82

3.19–5.29

5.85–10.9

5.05–8.76

4.52–7.09

4.37–6.25

3.78–6.47

3.19–5.29

Procalcitonin (ng/mL)

P

0 day–Adult

Abbott Architect i1000

0–0.5

Prolactin (ng/mL)

S, P

0 day–Adult

Vitros 5600

3.7–17.9

3.0–18.6

Sodium (mmol/L)

S, P

0–8 days

8–30 days

1–6 mo

6–12 mo

1–19 y

> 19 y

Vitros 5600

133–146

134–144

134–142

133–142

134–143

137–145

133–146

134–144

134–142

133–142

134–143

137–145

Anti–human tissue transglutaminase IgA

0 day–Adult

Bio-Flash

< 20

Anti–human tissue transglutaminase IgG

0 day–Adult

Bio-Flash

< 20

Troponin I (ng/mL)

S, P

0 day–Adult

Vitros 5600

< 0.12

Total protein (g/dL)

S, P

0–2 mo

2–6 mo

6–12 mo

1–4 y

4–7 y

7–10 y

10–20 y

> 20 y

Vitros 5600

3.9–7.6

4.1–7.9

3.9–7.9

5.9–7.0

5.9–7.8

6.2–8.1

6.3–8.6

6.2–8.2

3.4–7.0

3.9–7.6

4.5–7.8

5.9–7.0

5.9–7.8

6.2–8.1

6.3–8.6

6.2–8.2

Triglycerides (mg/dL)

S, P

0–9 y

10–18 y

> 18 y

Vitros 5600

< 75

< 90

< 115

< 75

< 90

< 115

TCO₂-bicarb (mmol/L)

S, P

0–7 days

7–30 days

1–6 mo

6–12 mo

1–19 y

> 19

Vitros 5600

17–26

17–27

17–29

18–29

20–31

22–30

17–26

17–27

17–29

18–29

20–31

22–30

Uric acid (mg/dL)

S, P

0–30 days

1–12 mo

1–10 y

10–12 y

12–14 y

14–16 y

16–18 y

> 18 y

Vitros 5600

2.0–5.2

2.5–9.0

1.8–5.0

2.3–5.4

2.7–6.7

2.4–7.8

4.0–8.6

3.5–8.5

2.0–5.2

2.5–9.0

1.8–5.0

3.0–4.7

3.0–5.9

2.5–7.5

Vitamin B₁₂ (pg/mL)

S, P

0 day–Adult

Vitros 5600

163–949

ALP, alkaline phosphatase; ALT, alanine transaminase; ASCA, anti-Saccharomyces cerevisiae antibodies; AST, aspartate aminotransferase; B, whole blood; BNP, brain natriuretic peptide; BUN, blood urea nitrogen; Ca, calcium; CSF, cerebrospinal fluid; GGT, γ-glutamyl transpeptidase; HDL, high-density lipoprotein; iCa, ionized calcium; IgA, immunoglobulin A; IgE, immunoglobulin E; IgG, immunoglobulin G; IgM, immunoglobulin M; iMg, ionized magnesium; LDH, lactic dehydrogenase; P, plasma; S, serum; U, urine.

Data from Children’s Hospital, Colorado, Chemistry Laboratory Procedure Manuals.

Table 46–2.Endocrine chemistry.

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Table 46–2. Endocrine chemistry.

Analyte, Units, Specimen Type, Source

Age

Methodology

Male Range

Female Range

Cortisol (μg/dL)

S, P

0 day–Adult

(AM values)

(PM values)

Vitros 5600

4.5–22.7

1.7–14.1

4.5–22.7

1.7–14.1

Estradiol (ng/mL)

S

Prepuberty

Tanner 1

Tanner 2

Tanner 3

Tanner 4

Tanner 5

HPLC/MS

< 1.5

0.5–1.1

0.5–1.6

0.5–2.5

1.0–3.6

< 1.5

0.5–2.0

1.0–2.4

0.7–6.0

2.1–8.5

3.4–17

FSH (mIU/mL)

S, P

4 wk–11 mo

12 mo–8 y

Tanner 1

Tanner 2

Tanner 3

Tanner 4

Tanner 5

Adult

Follicular

Midcycle

Luteal phase

Siemens Immulite

0.16–4.1

0.26–3.0

1.8–3.2

1.2–5.8

2.0–9.2

2.6–11.0

2.0–9.2

0.24–14.2

1.0–4.2

1.0–10.8

1.5–12.8

1.5–11.7

1.0–9.2

1.8–11.2

6–35

1.8–11.2

Growth hormone (ng/mL)

S

0 day–Adult

Siemens Immulite

0–8

0–3

IGF-BP3 (μg/mL)

S, P

0–7 days

8–15 days

16 days–1 y

2 y

3 y

4 y

5 y

6 y

7 y

8 y

9 y

10 y

11 y

12 y

13 y

14 y

15 y

16 y

17 y

18 y

19 y

20 y

Siemens Immulite

0.1–0.7

0.5–1.4

0.7–3.6

0.8–3.9

0.9–4.3

1.0–4.7

1.1–5.2

1.3–5.6

1.4–6.1

1.6–6.5

1.8–7.1

2.1–7.7

2.4–8.4

2.7–8.9

3.1–9.5

3.3–10.0

3.5–10.0

3.4–9.5

3.2–8.7

3.1–7.9

2.9–7.3

2.9–7.2

0.1–0.7

0.5–1.4

0.7–3.6

0.8–3.9

0.9–4.3

1.0–4.7

1.1–5.2

1.3–5.6

1.4–6.1

1.6–6.5

1.8–7.1

2.1–7.7

2.4–8.4

2.7–8.9

3.1–9.5

3.3–10.0

3.5–10.0

3.4–9.5

3.2–8.7

3.1–7.9

2.9–7.3

2.9–7.2

IGF-1 (ng/mL)

S, P

0–3 y

4–6 y

7–9 y

10–11 y

12–13 y

14–15 y

16–18 y

Siemens Immulite

< 15.0–129.0

22.0–208.0

40.1–255.0

68.7–316.0

143.0–506.0

177.0–507.0

173.0–414.0

18.2–172.0

35.4–232.0

56.9–277.0

118.0–448.0

170.0–527.0

191.0–496.0

190.0–429.0

LH (mIU/mL)

S, P

14 days–11 mo

12 mo–8 y

Tanner 1

Tanner 2

Tanner 3

Tanner 4

Tanner 5

Adult

Follicular

Midcycle

Luteal

Siemens Immulite

0.02–7.0

0.02–0.3

0.2–4.9

0.2–5.0

0.4–7.0

1.5–9.0

0.02–7.0

0.02–0.3

0.02–0.18

0.02–4.7

0.10–12.0

0.4–11.7

2.0–9.0

18.0–49.0

2.0–11.0

T₃, total (ng/dL) S, P

0–3 days

4 days–1 y

1–6 y

7–11 y

12–18 y

Siemens Immulite

60–300

90–260

90–240

90–230

100–210

60–300

90–260

90–240

90–230

100–210

T₄ (μg/dL) S, P

0–3 days

3–30 days

30 days–1 y

1–6 y

6–19 y

> 19 y

Siemens Immulite

8–20

5–15

6–14

4.5–12.5

4.5–11.5

8–20

5–15

6–14

4.5–12.5

4.5–11.5

5.5–11.5

T₄ free (ng/dL)

S

0–2 days

3–30 days

1 mo–19 y

> 19 y

Siemens Immulite

2.0–5.0

0.9–2.2

0.8–2.0

0.78–2.19

2.0–5.0

0.9–2.2

0.8–2.0

0.78–2.19

TSH (μIU/mL)

S, P

0–3 days

3–30 days

1–6 mo

6 mo–5 y

5–13 y

> 13 y

Vitros 5600

1.0–20.0

0.5–8.0

0.5–6.0

0.5–5.5

0.5–5.0

1.0–20.0

0.5–8.0

0.5–6.0

0.5–5.5

0.5–5.0

Testosterone, total (ng/dL)

S, P

Premature

Newborn

Prepubertal

Tanner 1

Tanner 2

Tanner 3

Tanner 4

Tanner 5

Vitros 5600

37–198

75–400

1–10

18–150

100–320

200–620

350–970

5–22

20–64

1–10

7–28

15–35

13–32

20–38

Anti-thyroglobulin (IU/mL)

S, P

0–Adult

Siemens Immulite

< 20

Thyroglobulin (ng/mL)

S, P

0–Adult

Siemens Immulite

< 33

FSH, follicle-stimulating hormone; IGF-1, insulin-like growth factor 1; IGF-BP3, insulin-like growth factor–binding protein 3; LH, luteinizing hormone; T₃, triiodothyronine; T₄, thyroxine; TSH, thyroid-stimulating hormone.

Data from Children’s Hospital, Colorado, Chemistry Laboratory Procedure Manuals.

Table 46–3.Hematology.

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Table 46–3. Hematology.

Analyte, Units, Specimen Type, Source

Age

Methodology

Male Range

Female Range

WBC (×10³/µL)

EDTA whole blood

0–1 mo

1–24 mo

2–12 y

12–18 y

> 18 y

Sysmex XN-Series

6.5–16.7

7.7–13.7

5.7–10.5

5.2–9.7

5.8–10.3

6.5–16.7

7.7–13.7

5.7–10.5

5.2–9.7

5.8–10.3

RBC (×10⁶/µL)

EDTA whole blood

0–14 days

15–30 days

1–2 mo

2–6 mo

6 mo–6 y

6–12 y

12–18 y

> 18 y

Sysmex XN-Series

3.7–5.1

3.25–4.62

3.0–4.3

3.3–4.7

3.75–4.9

3.9–5.0

4.1–5.4

4.1–5.45

3.7–5.1

3.25–4.62

3.0–4.3

3.3–4.7

3.75–4.9

3.9–5.0

3.7–4.8

Hemoglobin (g/dL)

EDTA whole blood

0–3 days

4–7 days

8–14 days

15–30 days

1–6 mo

6 mo–6 y

6–12 y

12–18 y

> 18 y

Sysmex XN-Series

12.8–18.1

12.5–17.0

11.9–16.3

10.5–14.8

9.5–13.3

10.3–13.8

11.1–14.5

11.8–15.8

11.8–16.4

12.8–18.1

2.5–17.0

11.9–16.3

10.5–14.8

9.5–13.3

10.3–13.8

11.1–14.5

11.3–14.7

11.2–14.3

HCT (%)

EDTA whole blood

0–3 days

4–7 days

8–14 days

15–30 days

1–6 mo

6 mo–6 y

6–12 y

12–18 y

> 18 y

Sysmex XN-Series

36.5–51.4

35.0–47.5

33.6–45.0

30.0–40.9

27.0–38.5

30.5–39.7

32.9–41.5

34.0–46.0

34.0–48.0

36.5–51.4

35.0–47.5

33.6–45.0

30.0–40.9

27.0–38.5

30.5–39.7

32.9–41.5

33.0–42.6

MCV (fL)

EDTA whole blood

0–3 days

4–7 days

8–30 days

1–2 mo

2–6 mo

6 mo–12 y

12–18 y

> 18 y

Sysmex XN-Series

97.0–106.0

90.0–101.0

87.0–96.5

86.5–92.1

82.0–87.0

75.6–85.2

80.8–87.7

83.5–90.2

97.0–106.0

90.0–101.0

87.0–96.5

86.5–92.1

82.0–87.0

75.6–85.2

80.8–87.7

83.5–90.2

Polys (×10³/µL) (absolute)

EDTA whole blood

0–3 days

4–30 days

1 mo–2 y

2–10 y

10–18 y

> 18 y

Sysmex XN-Series

4.33–9.11

3.33–9.42

1.5–6.0

1.8–5.4

2.0–5.8

2.5–6.0

4.43–11.4

3.18–9.43

1.5–6.0

1.8–5.4

2.0–5.8

2.5–6.0

Bands (×10³/µL) (absolute)

EDTA whole blood

0–1 mo

1 mo–Adult

Sysmex XN-Series

0–3.5

0–1.0

0–3.5

0–1.0

Lymphs (×10³/µL) (absolute)

EDTA whole blood

0–15 days

15–30 days

1 mo–2 y

2–6 y

6–12 y

> 12 y

Sysmex XN-Series

1.35–4.09

1.68–5.25

2.22–5.63

1.33–3.47

1.23–2.69

1.03–2.18

1.35–4.09

1.68–5.25

2.22–5.63

1.33–3.47

1.23–2.69

1.03–2.18

Monos (×10³/µL) (absolute)

EDTA whole blood

0–15 days

16 days–6 mo

6 mo–2 y

2 y–Adult

Sysmex XN-Series

0.52–1.77

0.28–1.38

0.25–1.15

0.18–0.94

0.52–1.77

0.28–1.38

0.25–1.15

0.18–0.94

EOS (×10³/µL) (absolute)

EDTA whole blood

0–1 mo

1 mo–Adult

Sysmex XN-Series

0.03–0.51

0.01–0.42

0.03–0.51

0.01–0.42

Basos (×10³/µL) (absolute)

EDTA whole blood

0–15 days

16 days–Adult

Sysmex XN-Series

0.02–0.11

0.01–0.07

0.02–0.11

0.01–0.07

Platelet (×10³/µL)

EDTA whole blood

0 days–Adult

Sysmex XN-Series

150–500

MCH (pg)

EDTA whole blood

0–3 days

4–60 days

2 mo–18 y

> 18 y

Sysmex XN-Series

31.7–36.4

29.8–33.4

26.0–30.7

28.3–31.4

31.7–36.4

29.8–33.4

26.0–30.7

28.3–31.4

MCHC (g/dL)

EDTA whole blood

0 day–Adult

Sysmex XN-Series

33.5–36.0

Data from Children’s Hospital, Colorado, Hematology Laboratory Procedure Manual.

Chapter 46: Chemistry & Hematology Reference Intervals

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INTRODUCTION

CHALLENGES IN DETERMINING & INTERPRETING PEDIATRIC REFERENCE INTERVALS

GUIDELINES FOR USE OF DATA IN A REFERENCE RANGE STUDY

STATISTICAL COMPUTATION OF REFERENCE INTERVALS

A. Parametric Method of Computation

Figure 46–1.

B. Nonparametric Method of Computation

Figure 46–2.

WHY REFERENCE INTERVALS VARY

SENSITIVITY, SPECIFICITY, & PREDICITIVE VALUES

Figure 46–3.

Figure 46–4.

PEDIATRIC REFERENCE INTERVALS

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