Diagnostic, screening tests, differences and applications and their characteristics, four pillars of screening tests, sensitivity, specificity, predictive values and accuracy

1. Screening
Professor Tarek Tawfik
Amin
Public Health
Cairo University
amin55@myway.com

2. Objectives
By the end of the session 4th medical students
should be able to:
- Identify the difference between diagnostic
and screening test.
- Identify and recall the pillars of screening
test accuracy.
- Interpret the output of accuracy parameters
of screening test.

3. The patient’s scenario
Mrs. Zi has discovered a lump
in her left breast.
So what?

4. Diagnostic tests: ordered to answer a specific question
Diagnostic tests help physicians revise
disease probability for their patients.
1. Establish a diagnosis in symptomatic patients. ECG
to diagnose ST-elevation myocardial infarction in patients with chest pain.
2. Screen for disease in asymptomatic patients.
Prostate-specific antigen (PSA) test in men older than 50 years.
3. Provide prognostic information in patients with
established disease. CD4 count in patients with HIV.
4. Monitor therapy by either benefits or side
effects. Measuring the international normalized ratio (INR) in patients taking
warfarin.
5. Confirm that a person is free from a disease.
Pregnancy test to exclude the diagnosis of ectopic pregnancy.

5. Criterion (reference test)
• The criterion (reference) standard test definitively
decides either presence or absence of a disease.
Pathological specimens for malignancies and pulmonary angiography for pulmonary embolism.
• Criterion standard tests routinely come with
drawbacks; expensive, less widely available, and more invasive. Compel
physicians to choose other diagnostic tests as surrogates (alternative test).
Venography, the criterion standard for vein thrombosis, is an invasive procedure with
significant complications [renal failure, allergic reaction, and clot formation]. Venography less
desirable than the alternative diagnostic test—venous duplex ultrasonography.
• The price most diagnostic tests (surrogates) pay for their
ease of use compared with their criterion standard is a
decrease in accuracy.

6. Screening Vs. Diagnostic
tests
• Screening tests are not diagnostic tests
• The primary purpose of screening tests is to
detect early disease or risk factors for disease in
large numbers of apparently healthy
individuals.
• The purpose of a diagnostic test is to establish
the presence (or absence) of disease as a basis for
treatment decisions in symptomatic or screen
positive individuals (confirmatory test).

7. Screening tests Diagnostic tests
1-To detect potential disease indicators 1- To establish presence/absence of disease
2-Large numbers of asymptomatic, but
potentially at risk individuals
2-Symptomatic individuals to establish
diagnosis, or asymptomatic individuals with
a positive screening test
3-Simple, acceptable to patients and staff 3-Maybe invasive, expensive but justifiable
as necessary to establish diagnosis
4-Generally chosen towards high
sensitivity not to miss potential disease
4-Chosen towards high specificity (true
negatives). More weight given to accuracy
and precision than to patient acceptability
5-Essentially indicates suspicion of disease 5- Result provides a definite diagnosis
6-Cheap, benefits should justify the costs
since large numbers of people will need to
be screened to identify a small number of
potential cases
6-Higher costs associated with diagnostic
test maybe justified to establish diagnosis.

8. The validity of a screening test:
sensitivity and specificity
o The measures of sensitivity and
specificity describe how well the
proposed screening test performs
against an agreed 'Gold Standard'
test.
o In medicine, a gold standard test or criterion
standard test is a diagnostic test or benchmark
that is regarded as definitive.
o The actual gold standard test may be too
unpleasant for the patient, too impractical or too
expensive to be used widely as a screening test

9. Sensitivity and Specificity
• The outcomes of any screening test
• The calculations

10. Screening
Disease status as determined by 'Gold Standard'
Disease No Disease
Test positive True positives
(a)
False positives
(b)
Total test
positives (a+b) → Positive
predictive value
Test negative False negatives
(c)
True negatives
(d)
Total test
negatives (c+d) → Negative
predictive value
Total with
disease (a+c)
Total without
disease (b+d)
Total screened
(a+b+c+d)
↓
Sensitivity
↓
Specificity
True positives = number of individuals with disease and a positive screening test (a)
False positives = number of individuals without disease but have a positive screening test (b)
False negatives = number of individuals with disease but have a negative screening test (c)
True negatives = number of individuals without disease and a negative screening test (d)
Missed
cases Labeling
effect

11. 1-Sensitivity
• Sensitivity is defined as the ability of the test to
detect all those with disease in the screened
population. This is expressed as the proportion
of those with disease correctly identified by a
positive screening test result
• Sensitivity = Number of true positives
Total with disease
= a/ (a+c)

12. 2-Specificity
• Specificity is defined as the ability of the test to
identify correctly those free of disease in the
screened population. This is expressed as the
proportion of those without disease correctly
identified by a negative screening test result
• Specificity = Number of true negatives
Total without disease
= d/ (b+d)

13. 3-Positive Predictive Value
• The positive predictive value (PPV) describes the
probability of having the disease given a positive
screening test result in the screened population.
• How many of +ve (s) at screening are actually having the
disease?
This is expressed as the proportion of those with disease
among all screening test positives.
• PPV = Number of true positives
total test positives
PPV = a / (a+b)

14. 4-Negative Predictive Value
• The negative predictive value (NPV) describes the
probability of not having the disease given a negative
screening test result in the screened population.
• How many of –ve (S) are not diseased?
This is expressed as the proportion of those without
disease among all screening test negatives.
• NPV = Number of true negatives
total test negatives
NPV= d / (c+d)

15. Disease Prevalence Effect
• Sensitivity and specificity are
independent of prevalence of
disease, i.e. test specific (they describe
how well the screening test performs against the gold standard).
• PPV and NPV however are disease
prevalence dependant, i.e.
population specific. PPV and NPV give
information on how well a test screening test will perform in a
given population with known prevalence.
• Generally a higher prevalence
will increase the PPV and

16. Example
A new ELISA (antibody test) is
developed to diagnose HIV infections.
Serum from 10,000 patients that were
positive by Western Blot (the Gold
Standard assay) was tested, and
9,990 were found to be positive by the
new ELISA screening test.
The manufacturers then used the ELISA
to test serum from 10,000 nuns who
denied risk factors for HIV infection.
9,990 were negative and the 10

17. HIV [Nuns and HIV patients]
Infected Not infected
ELISA test
+ 9,990 (a) 10 (b)
- 10 (c) 9,990 (d)
10,000 (a+c) 10,000 (b+d)
Sensitivity = a/(a+c)
= 9990/(9990+10)
= 99.9%
Specificity= b/(b+d)
= 9990/(9990+10)
= 99.9%
Excellent test

18. On population level
The test is applied to a million people where 1%
are infected with HIV (assuming the sensitivity
and specificity remain the same). Of the million
people, 10,000 would be infected with HIV. Since
the new ELISA is 99.9% sensitive, the test will
detect 9,990 (true positives) people who are
actually infected and miss 10 (false negative).
Looking at those numbers the test appears
very good because it detected 9,990 out of
10,000 HIV infected people.
But there is another side to the test. Of the 1
million people in this population, 990,000 are
not infected. Looking at the test results of the
HIV negative population (the specificity of the
assay is 99.9%), 989,010 are found to be not

19. 1% Prevalence
HIV
Infected Not infected
Test
+ 9990 (a) 990 (b)
Test positives
a+b
PPV= a/(a+b)
=
9990/(9990+990)
=91%
- 10 (c) 989,010 (d)
Test negatives
c+d
NPV=
d/(c+d) =989,010/
(10+989,010)
= 99.9%
HIV positive
10,000
HIV negative
999,000
Total screened=
a+b+c+d
Sensitivity =
99,9%
Specificity
= 99,9%
Sensitivity and specificity are not the only performance features because they do not
address the problems of the prevalence of disease in different populations.
For that, the understanding of the positive and negative predictive value is crucial.

20. Blood donors have already been
screened for HIV risk factors before
they are allowed to donate blood, so
that the HIV sero-prevalence in this
population is closer to 0.1% instead
of 1%. For every 1,000,000 blood
donors, 1,000 are HIV positive. With a
sensitivity of 99.9%, the ELISA would
pick up 999 of those thousand, but
would fail to pick up one HIV sero-
positive individual.
Of the 999,000 uninfected
individuals, the test would label
998,001 individuals assero-negative
(true negatives).

21. Blood donors 0.1%
Prevalence
HIV
+ -
Test
+ 999 (a) 999 (b)
Test
positives
1,998
PPV=
a/(a+b)
=50%
- 1 (c) 998,001 (d)
Test
negatives
998,002
NPV=
d/(c+d)
=99.999%
HIV
positive
1000
HIV
negative
999,000
Total
a+b+c+d
Sensitivity
99.9%
Specificity
99.9%

22. • The second population consists
of former IV drug users
attending drug rehabilitation
units, with a prevalence of 10%.
For a million of these
individuals, 100,000 would be
HIV-infected and 900,000 would
be HIV negative.

23. 10% Prevalence
HIV
+ -
Test
+
99,900
(a)
900
(b)
Test
positives
100,800
PPV=
a/(a+b)
=99%
-
100
(c)
899,100
(d)
Test
negatives
899,200
NPV=
d/(c+d)
=99.999%
HIV
negative
100,000
HIV
negative
900,000
Total screened=
a+b+c+d
Sensitivity
99.9%
Specificity
99.9%

24. • The sensitivity and specificity of the
test has not changed. It is just that
the predictive value of the test has
changed depending on the population
being tested.
• The positive predictive value is how
many of the test-positives truly
have the disease. In the first
example with a 1% sero-positive
rate, the ELISA has a positive
predictive value of 0.91 (91%). When
Remarks

25. Rema
rks
Although the sensitivity of the ELISA
does not change between
populations, the positive predictive
value changes drastically from
only half the people that tested
positive being truly positive in a
low- incidence population to 99% of
the people testing positive being
truly positive in the high-
prevalence population. The
negative predictive value of the

26. Derivatives of screening

27. Term Calculation Plain English
True positive (TP) Counts in 2 X 2 table # Patients with the disease who have a positive
test result
True negative (TN) Counts in 2 X 2 table # Patients without the disease who have a
negative test result
False positive (FP) Counts in 2 X 2 table # Patients without the disease who have a
positive test result
False negative (FN) Counts in 2 X 2 table # Patients with the disease who have a negative
test result
Sensitivity = True positive rate (TPR) TP / (TP + FN) The probability that a patient with the disease
will have a positive test result
1 - Sensitivity = False-negative rate (FPR) FN / (TP + FN) The probability that a patient with the disease
will have a negative test result
Specificity = True negative rate (TNR) TN / (TN + FP) The probability that a patient without the disease
will have a negative test result
1 - Specificity = False-positive rate (FPR) FP / (TN + FP) The probability that a patient without the disease
will have a positive test result
Positive predictive value TP / (TP + FP) The probability that a patient with a positive test
result will have the disease
Negative predictive value TN / (TN + FN) The probability that a patient with a negative test
result will not have the disease.
Accuracy (TP + TN) / (TP + TN + FP + FN) The probability that the results of a test will
accurately predict presence or absence of disease
Bayes’ theorem Posttest Odds = Pretest Odds X Likelihood Ratio The odds of having or not having the disease after
testing
Likelihood ratio of a positive test result (LR+) Sensitivity / (1 - Specificity) The increase in the odds of having the disease
after a positive test result
Likelihood ratio of a negative test result (LR-) (1 - Sensitivity) / Specificity The decrease in the odds of having the disease
after a negative test result

28. Thank you