Module 6

1
Module 6

• Normal ValuesHow are Normal Reference Ranges
  Established?

2
Doctor, was my test normal?
3
Reference Ranges

• Comparison of a patients laboratory test result
  versus a reference or normal range is an
  important aspect of medical decision making
• Reference Ranges are required by professional
  accreditation and regulatory standards

4
Reference Ranges

• Laboratory directors determine and evaluate
  reference ranges reported with all test results
• In most cases, a normal range is used as the
  tests reference range.
• For some analytes, the reference range is defined
  as less than or greater than a certain value
• Example total cholesterol lt200mg is
  desirable

5
Establishing Reference Ranges

• 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.

6
Gaussian Distribution

• 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 tests normal range.
• For many (but not all) tests, this is how the
  range of tests results for normal healthy
  individuals is determined.

7
Some Normals are Abnormal and Vice Versa

• The normal range encompasses the mean plus or
  minus two standard deviations or, again, about
  95 of normal, healthy individuals test results.
• However, 5 (roughly 1 out of 20) normal healthy
  patients may be outside the cutoff value.
• 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.
• This situation is encountered with almost all
  tests.
• 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.

8
Two Populations of Results

• Theoretically, the better tests minimize this
  overlap between the distribution of normal and
  abnormal test results.
• An ideal test would have no overlap at all and
  could perfectly discriminate between a normal and
  abnormal test result.
• Lab experts continue to look for at least one
  test like this .

9
Non-Gaussian Distributions

• For non-Gaussian distributions, lab directors can
  use other nonparametric techniques to establish
  reference range limits
• 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.

10
Wheres the Threshold or Cut-off?

• 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.

11

• 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.
• A through D represent possible cutoff values that
  could be used to classify subjects based on the
  analyte values.

12
Distribution of a Test Result in Healthy (n200)
and Diseased (n50) Persons
Frequency
10
20
30
40
50
60
70
130
80
90
100
110
120
Analyte Value (units)
13

• What would be the advantage(s) of selecting A as
  the cut off value for normal?
• All patients with the disease would have a
  positive test result
• What would be the advantage(s) of selecting D as
  the cut-off value?
• All healthy patients would have a negative test
  result

14

• What would be the disadvantage(s) of selecting D
  as the cut off?
• Persons with the disease may not be diagnosed
• What would be the disadvantage(s) of selecting A
  as the cut-off?
• Patients who do not have the disease would be
  classified as having an abnormality

15

• Consider the implications if this were a
  screening test for cancer.

Image by Theresa Kristopaitis, MD
16
Recall the Definitions of Sensitivity and
Specificity

• Sensitivity is the ability of a test to detect
  disease
• Proportion of persons with disease in whom the
  test is positive
• Specificity is the ability to detect the absence
  of disease
• Proportion of persons without disease in whom the
  test is negative

17
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
18
Does this Grid Reflect Cut-off Value A or D?
Test Result Disease or Sick No Disease (Normal, Healthy)
Positive Result True Positives 50 False Positives 25
Negative Result False Negatives 0 True Negatives 175
TOTAL 50 200
19
Answer Cutoff Value A

• The sensitivity of the test would be 100
• HOWEVER
• The specificity of the test would be 87

20

• Sensitivity and specificity therefore are not
  fixed characteristics of a test and must be
  calculated for each cutoff chosen
• When a test cutoff is altered, an inverse
  relationship between sensitivity and specificity
  is noted

21
Wheres the Threshold or Cut-off?

• 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.
• It also affects the distribution of values and
  how they are tallied in the predictive value grid
  and resultant diagnostic value of a test
• It affects the care of patients and may have
  serious implications

22

• Congratulations!
• You have completed the
• Introduction to Laboratory Medicine modules!