# Establishing Reference Ranges

```Module 6
“Normal Values”:
How are Normal Reference
Ranges Established?
Doctor, was my test normal?
Reference Ranges
O Comparison of a patient’s laboratory test
result versus a reference or “normal” range
is an important aspect of medical decision
making
O Reference Ranges are required by
professional accreditation and regulatory
standards
Reference Ranges
O Laboratory directors determine and
evaluate reference ranges reported
with all test results
O In most cases, a “normal range” is
used as the test’s “reference range”.
O For some analytes, the reference
range is defined as “less than” or
“greater than” a certain value
O Example: total cholesterol: <200mg
is desirable
Establishing Reference Ranges
O When selecting the decision threshold or
cutoff value (a limit above or below which a
patient is considered affected by a particular
disease, i.e., normal or abnormal), a variety
of methods can be used.
Gaussian Distribution
O If test results from a normal healthy patient
population fall into a bell-shaped, Gaussian,
normal distribution, the central 95% is usually
used as the test’s normal range.
O For many (but not all) tests, this is how the range
of tests results for normal healthy individuals is
determined.
Some Normals are Abnormal
…… and Vice Versa
O The normal range encompasses the mean plus or
minus two standard deviations or, again, about 95%
of normal, healthy individuals’ test results.
O However, 5% (roughly 1 out of 20) normal healthy
patients may be outside the cutoff value.
O Roughly 2.5% of normal people can be expected to have a
result below and roughly 2.5% of normal people can be
expected to have a result above the reported normal
range.
O This situation is encountered with almost all tests.
O This is because the distribution of tests results from
normal, healthy individuals overlaps with the
distribution of test results from sick patients with the
relevant disease.
Two Populations of Results
O Theoretically, the better tests minimize this
overlap between the distribution of normal
and abnormal test results.
O An ideal test would have no overlap at all
and could perfectly discriminate between a
normal and abnormal test result.
O Lab experts continue to look for at least one
test like this …….
Non-Gaussian Distributions
O For non-Gaussian distributions, lab directors
can use other nonparametric techniques to
establish reference range limits
O Example: set upper and lower limits of normal
to include 95% of the population after all of
the test results have been transformed into
logarithms taking the central 95% of the
transformed data.
Where’s the Threshold or
Cut-off?
O Ultimately, where the lab places the limits
(threshold or cut-off) on a normal or reference
range determines what level of result is
considered “normal” or “abnormal”.
O In the next slide, the values for the
concentration of a hypothetical analyte were
determined in a group of 200 healthy
persons and in a group of 50 diseased
persons. The raw data for the group were
fitted to Gaussian distributions.
O A through D represent possible cutoff values
that could be used to classify subjects
based on the analyte values.
Distribution of a Test Result in
Healthy (n=200) and Diseased (n=50) Persons
Frequency
Healthy Persons
Cutoffs
A
B C
D
Diseased Persons
10 20 30 40 50 60 70 80 90 100 110 120 130
Analyte Value (units)
O What would be the advantage(s) of selecting
A as the cut off value for “normal”?
O All patients with the disease would have a
positive test result
O What would be the advantage(s) of selecting
D as the cut-off value?
O All healthy patients would have a negative
test result
O What would be the disadvantage(s) of
selecting D as the cut off?
O Persons with the disease may not be
diagnosed
O What would be the disadvantage(s) of
selecting A as the cut-off?
O Patients who do not have the disease would
be classified as having an “abnormality”
Consider the implications if this were a
screening test for cancer….
Image by Theresa Kristopaitis, MD
Recall the Definitions of
Sensitivity and Specificity
O Sensitivity is the ability of a test to detect
disease
O Proportion of persons with disease in whom
the test is positive
O Specificity is the ability to detect the
absence of disease
O Proportion of persons without disease in
whom the test is negative
Predictive Value Grid
Test Result
Disease or Sick
No Disease
(Normal, Healthy)
Positive
Result*
True Positives
False Positives
Negative Result*
False Negatives
True Negatives
TOTAL
*“positive” usually refers to a test being abnormal, “negative” usually refers to normal
Does this Grid Reflect Cut-off Value A or D?
Test Result
Disease or Sick
No Disease
(Normal, Healthy)
Positive
Result*
True Positives
False Positives
50
Negative Result*
TOTAL
False Negatives
25
True Negatives
0
175
50
200
The sensitivity of the test would be 100%
HOWEVER
The specificity of the test would be 87%
O Sensitivity and specificity therefore are not
fixed characteristics of a test and must be
calculated for each cutoff chosen
O When a test cutoff is altered, an inverse
relationship between sensitivity and
specificity is noted
Where’s the Threshold or
Cut-off?
O To reiterate, where the lab places the threshold
or cut-off on a normal or reference range
determines what level of result is considered
“normal” or “abnormal”.
O It also affects the distribution of values and
how they are tallied in the predictive value grid
and resultant diagnostic value of a test
O It affects the care of patients and may have
serious implications
Congratulations!
You have completed the
“Introduction to Laboratory Medicine”
modules!
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