**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** 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 2Sensitivity = 85/100 = 85%Specificity = 395/400 =** 98%

**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 3Sensitivity = 85%Specificity = 98%PPV = 21/31 =** 67%NPV = 465/469 = 99%

**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 solutionSensitivity = 90%Specificity =** 99%PPV = 90/190 = 47%NPV = 9800/9810 = 99.9%

**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.