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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.