1. Clinical Effectiveness: sensitivity, specificity and PPV
Dr Nick Price

2. 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

3. 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.

4. 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
So a sensitive test is likely to pick up the condition.

5. 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

6. Specificity (most accurately)
The chance of having a negative test given that you do not have the disease. Or
How many false negatives.
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.

7. 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

8. The Truth Table
         TRUTH
POSITIVE
NEGATIVE
TEST a b 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

9. 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

10. Example 2 Sensitivity = 85/100 = 85% Specificity = 395/400 = 98%
Truth
Test + -
Total 85 5 90 15
395
410
100
400
500

11. 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!

12. 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?

13. Example 3 Sensitivity = 85% Specificity = 98% PPV = 21/31 = 67% NPV = 465/469 = 99%
Truth
Test + -
Total 21 10 31 4
465
469
500x5% = 25
475
500

14. So the incidence of the disease greatly effects the PPV or how many patients you will see with false positive test result

15. 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?

16. 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)

17. 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
9900
10000

18. Example 4 – narrative solution
Read the paper!
Now practice explaining one of these example in trios, then rotate.

19. 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.