Clinical Effectiveness:
Interpreting test results
Nick Price 17th October 2006
Aims
 to
reflect on the implications of a
study of health professional's
interpretation of a test result
 to develop skills in interpreting test
results
Objectives
By the end of the session you should be able to:
 Define sensitivity in ordinary language
 Define specificity in ordinary language
 Understand how the prevalence of a condition in
your test population influences the significance of
a positive test result in a particular patient.
 Understand how 'testing more patients, just in
case' will influence the likelihood of a patient with
a positive result having the condition.
 Understand to term 'positive predictive value'.
 Have an opportunity to try explaining the result
of a test to your peers.
Sensitivity
How many true positives in comparison to
the ‘gold standard’.
Or (most accurately)
 The chance of having a positive test,
assuming that you do have the condition.
Or
 So with a very Sensitive Test a Negative
will rule Out the condition – SnNOut
Or
 So a sensitive test is likely to pick up the
condition.

Sensitivity 2
Can you think of some tests with
very high sensitivity in
comparison to a gold standard?
e.g. D-dimer (99%), Leucocytes on
Multistix (87%), random blood sugar
Specificity
(most accurately)
 The chance of having a negative test given
that you do not have the disease.
Or
 How many false negatives.
Or
With a very Specific test a Positive result
rules the condition IN -SpPin
 So with a specific test a positive test is
likely to mean you have the condition.
Specificity 2
Can you think of some very specific
tests?
3+ of glucose and ketones on multistix?
A hard craggy breast lump?
A yes score of 3+ on CAGE (99.8%)
Some not very specific ones:
Moderately raised random blood sugar in general
population
The Truth Table
TRUTH
T
E
S
T
POSITIVE
NEGATIVE
POSITIVE
a
b
NEGATIVE
c
d
Sensitivity is the probability [a / (a + c) in the table] that a true
positive has been correctly classified as positive by the test.
Specificity is the probability [d / (b + d)] that a true negative is
correctly classified negative by the test
Example
With leukocyte esterase dipstix (LED)
for chlamydia vs ‘gold standard’
In a GUM clinic 500 patients were
tested, 100 tested positive with gold
standard, 90 tested positive with
LED. Of these 90, 5 were in fact
negative with the gold standard.
What is the sensitivity and specificity
of LED
Example 2
Sensitivity = 85/100 = 85%
Specificity = 395/400 = 98%
Truth
Test
+
-
Total
+
85
5
90
-
15
395
410
Total
100
400
500
So what is the chance that a
positive LED test means you have
chalmydia?
Aka what is the ‘positive predictive value’
(PPV).
 This is the true positives / true positives
and the false positives
 PPV = a/a+c = 85/90 = 94%.

Excellent, so this is a good test to use in GP
e.g. routinely when taking smears!
PPV 1
So the incidence of chlamydia in the general
population of all women having smears in
GP is say 5%.
We do 500 smears a year
We have a test that has sensitivity of 85%
and a marvellous specificity of 98%.
What chance the patient with a positive test
actually has chlamydia in this context?
Example 3
Sensitivity = 85%
Specificity = 98%
PPV = 21/31 = 67%
NPV = 465/469 = 99%
Truth
Test
+
-
Total
+
21
10
31
-
4
465
469
Total
500x5%
= 25
475
500
So the incidence of the disease
greatly effects the PPV or how
many patients you will see with
false positive test result
So what about the case in the
experimental study?
 1%
of babies have Down’s
 If the baby has Down’s 90% will
have +ve test.
 If the baby does not have Down’s
1% chance the result will be positive
 With a +ve result what is the chance
baby has Down’s?
So what about the case in the
experimental study? 2
 1%
of babies have Down’s
(incidence)
 If the baby has Down’s 90% will
have +ve test. (90% sensitivity)
 If the baby does not have Down’s
1% chance the result will be positive
(99% specificity)
 With a +ve result what is the chance
baby has Down’s? (PPV)
Example 4 – Maths solution
Sensitivity = 90%
Specificity = 99%
PPV = 90/190 = 47%
NPV = 9800/9810 = 99.9%
Truth
Test
+
-
Total
+
90
100
190
-
10
9800
9810
Total
100
9900
10000
Example 4 – narrative solution
 Read
the paper!
Now practice explaining one of these
example in trios, then rotate.
Objectives
By the end of the session you should be able to:
 Define sensitivity in ordinary language
 Define specificity in ordinary language
 Understand how the prevalence of a condition in
your test population influences the significance of
a positive test result in a particular patient.
 Understand how 'testing more patients, just in
case' will influence the likelihood of a patient with
a positive result having the condition.
 Understand to term 'positive predictive value'.
 Have an opportunity to try explaining the result
of a test to your peers.