Interpreting Diagnostic Tests

1. Interpreting Diagnostic Tests Ian McDowell Department of Epidemiology & Community Medicine January 2010 Note to users: you may find the additional notes & explanations in the ppt notes panel helpful.

2. Objectives • To understand sources of error in typical measurements • To understand sensitivity, specificity • To explain the implications of false positives and false negatives • To understand predictive values, • And Likelihood ratios

3. Road map to date This session considers the interpretation of diagnostic tests, a daily issue in clinical practice. It builds on some of the ideasintroduced last term: Measurements: validity, bias determinants of bias Applying conclusions from a study sample to an individual patient Contrasts between researchon hospital patientsand community practice Evidence-based practice

4. The Challenge of Clinical Measurement • Diagnoses are based on information, from formal measurements and/or from your clinical judgment. • This information is seldom perfectly accurate: • Random errors can occur (machine not working?) • Biases in judgment or measurement can occur (“this kid doesn’t look sick”) • Due to biological variability, this patient may not fit the general rule • Diagnosis (e.g., hypertension) involves a categorical judgment; this often requires dividing a continuous score (blood pressure) into categories. Choosing the cutting-point is challenging.

5. Therefore… • You need to be aware … • Diagnostic judgments are based on probabilities; • That using a quantitative approach is better than just guessing! • That you will gradually become familiar with the typical accuracy of measurements in your chosen clinical field; • That the principles apply to both diagnostic and screening tests; • Of some of the ways to describe the accuracy of a measurement.

6. Why choose one test and not another? • Reliability: consistency or reproducibility; this considers chance or random errors (which sometimes increase, sometimes decrease, scores). “Is it measuring something?” • Validity: “Is it measuring what it is supposed to measure?” By extension, “what diagnostic conclusion can I draw from a particular score on this test?” Validity may be affected by bias, which refers to systematic errors (these fall in a certain direction) • Safety, Acceptability, Cost, etc. 6

7. Reliability and Validity Reliability LowHigh Biasedresult! • • • • • • • • • Validity Low • • • • • • • ☺ High • • • • • • • Average of these inaccurate results is not bad. This is probably how screening questionnaires (e.g., for depression) work •

8. Ways of Assessing Validity • Content or “Face” validity: does it make clinical or biological sense? Does it include the relevant symptoms? • Criterion: comparison to a “gold standard” definitive measure (e.g., biopsy, autopsy) • Expressed as sensitivity and specificity • Construct validity (this is used with abstract themes, such as “quality of life” for which there is no definitive standard) 8

9. Criterion validation: “Gold Standard” The criterion that your clinical observation or simple test is judged against: • more definitive (but expensive or invasive) tests, such as a complete work-up, or • the clinical outcome (for screening tests, when workup of well patients is unethical). Sensitivity and specificity are calculatedfrom a research study comparing the test to a gold standard. 9

10. “2 x 2” table for validating a test Gold standard Disease DiseasePresent Absent Test score: Test positive Test negative a (TP) b (FP) c (FN) d (TN) • Validity: Sensitivity Specificity • = a/(a+c) = d/(b+d) • =TP/Diseased = TN/Healthy TP = true positive; FP = false positive… Golden Rule: always calculate based on the gold standard

11. A Bit More on Sensitivity = Test’s ability to detect disease when it is present a/(a+c) = TP/(TP+FN) = TP/disease Mnemonics: - a sensitive person is one who is aware of your feelings- (1 – seNsitivity) = false Negative rate = how many cases are missed by the screening test? 11

12. …and More on Specificity Precision of the test • a specific test would identify only that type of disease. “Nothing else looks like this” • a highly specific test generates few false positives. So, • If the result is positive, you can be confident the patient has this diagnosis. • Mnemonics: (1- sPecificity) = false Positive rate (How many are falsely classified as having the disease?) 12

13. Problems Resulting from Test Errors • False Positives can arise due to other factors (such as taking other medications, diet, etc.) They entail the cost and danger of further investigations, labeling, worry for the patient. • This is similar to Type I or alpha error in a test of statistical significance (the possibility of falsely concluding that there is an effect of an intervention). • False Negatives imply missed cases, so potentially bad outcomes if untreated • Cf. Type II or beta error: the chance of missing a true difference 13

14. Most Tests Provide a Continuous Score. Selecting a Cutting Point Test scores for a healthy population Sick population Healthyscores Pathologicalscores Possible cut-point Move this way to increase sensitivity(include more ofsick group) Move this way toincrease specificity(exclude healthy people) Crucial issue: changing cut-point can improve sensitivity or specificity, but never both

15. D + D - a b T + T - c d Clinical applications • A specific test can be useful to rule in a disease. Why? • Very specific tests give few false positives.So, if the result is positive, you can be sure the patient has the condition (‘nothing else would give this result’): “SpPin” • A sensitive test can be useful for ruling a disease out: • A negative result on a very sensitive test (which detects all true cases) reassures you thatthe patient does not have the disease: “SnNout”

16. Your Patient’s Question:“Doctor, how likely am I to have this disease?”This introduces Predictive Values • Sensitivity & specificity don’t answer this, because they work from the gold standard. • Now you need to work from the test result, but you won’t know whether this person is a true positive or a false positive (or a true or false negative). Hmmm… How accurately does a positive (or negative) result predict disease (or health)?

17. Start from Prevalence • Before you do any test, the best guide you have to a diagnosis is based on prevalence: • Common conditions (in this population) are the more likely diagnosis • Prevalence indicates the ‘pre-test probability of disease’

18. 2 x 2 table: Prevalence

19. D + D – a b T + T – c d Positive and Negative Predictive Values • Based on rows, not columns • Positive Predictive Value (PPV) = a/(a+b) = Probability that a positive score is a true positive • NPV = d/(c+d); same for a negative test result • BUT… there’s a big catch: • We are now working across the columns, so PPV & NPV depend on how many cases of disease there are (prevalence). • As prevalence goes down, PPV goes down (it’s harder to find the smaller number of cases) and NPV rises. • So, PPV and NPV must be determined for each clinical setting, • But they are immediately useful to clinician: they reflect this population, so tell us about thispatient

20. Prevalence and Predictive Values B. Primary care A. Specialist referral hospital D + D - D + D - 50 100 50 10 T + T - T + T - 5 1000 5 100 Sensitivity = 50/55 = 91% Specificity = 100/110 = 91% Prevalence = 55/165 = 33% Sensitivity = 50/55 = 91% Specificity = 1000/1100 = 91% Prevalence = 55/1155 = 3% PPV = 50/60 = 83% NPV = 100/105 = 95% PPV = 50/150 = 33% NPV = 1000/1005 = 99.5%

21. Predictive Values • High specificity = few FPs: Sp = TN/(TN+FP);FPs also drive PPV: PPV = TP/(TP + FP);So, the clinician is more certain that a patient with a positive test has the disease (it rules in the disease) • The higher the sensitivity, the higher the NPV:Sn = TP/(TP+FN); NPV = TN/(TN+FN); the clinician can be more confident that a patient with a negative score does not have the diagnosis (because there are few false negatives). So, high NPV can rule out a disease.

22. From the literature you can getSensitivity & Specificity. To work out PPV and NPV for your practice, you need to guess prevalence, then work backwards: Fill cells in following order: “Truth” Disease Disease Total Predictive Present Absent Values Test Pos Test Neg Total 4th 5th 7th 6th 8th 9th 10th 11th 2nd 3rd 1st (from estimated prevalence) (from sensitivity) (from specificity)

23. a b c d N Gasp…! Isn’t there an easier way to do all this…? Yes (good!) But first, you need a couple more concepts (less good…) • We said that before you apply a test, prevalence gives your best guess about the chances that this patient has the disease. • This is known as “Pretest Probability of Disease”: (a+c) / N in the 2 x 2 table: • It can also be expressed as odds of disease: (a+c) / (b+d), as long as the disease is rare

24. This Leads to … Likelihood Ratios • Defined as the odds that a given level of a diagnostic test result would be expected in a patient with the disease, as opposed to a patient without: true positive rate / false positive rate [TP / FP] • Advantages: • Combines sensitivity and specificity into one number • Can be calculated for many levels of the test • Can be turned into predictive values • LR for positive test = Sensitivity / (1-Specificity) • LR for negative test = (1-Sensitivity) / Specificity

25. Practical application: a Nomogram • You need the LR for this test • Plot the likelihood ratio on center axis (e.g., LR+ = 20) 3) Select pretest probability(prevalence) on left axis (e.g. Prevalence = 30%) ▪ ▪ 4) Draw line through these points to right axis to indicate post-test probability of disease Example: Post-test probability = 91%

26. There is another way to combine sensitivity and specificity:Meet Receiver Operating Characteristic (ROC) curves 1 0.8 0.6 Sensitivity 0.4 0.2 0 0 0.2 0.4 0.6 0.8 1 1-Specificity ( = false positives) Work out Sen and Spec for every possible cut-point, then plot these. Area under the curve indicates the information provided by the test In an ideal test, theblue line would reach the top leftcorner.For a useless test it would lie along the diagonal: nobetter than guessing

27. Chaining LRs Together (1) • Example: 45 year-old woman presents with “chest pain” • Based on her age, pretest probability that a vague chest pain indicates CAD is about 1% • Take a fuller history. She reports a 1-month history of intermittent chest pain, suggesting angina (substernal pain; radiating down arm; induced by effort; relieved by rest…) • LR of this history for angina is about 100

28. The previous example: 1. From the History: She’s young;pretest probabilityabout 1% Pretest probabilityrises to 50%based on history LR 100

29. Chaining LRs Together (2) 45 year-old woman with 1-month history of intermittent chest pain… After the history, post test probability is now about 50%. What will you do?A more precise (but also more costly) test: • Record an ECG • Results = 2.2 mm ST-segment depression. LR for ECG 2.2 mm result = 10. • This raises post test probability to > 90% for coronary artery disease (see next slide)

30. The previous example: ECG Results Post-test probabilitynow rises to 90% Now start pretest probability (i.e. 50%, prior to ECG, based onhistory)