Interpreting Diagnostic Tests

1. Interpreting Diagnostic Tests Ian McDowell Department of Epidemiology & Community Medicine January 2012 Note to readers: you may find the additional notes & explanations in the ppt notes panel helpful.

2. The Challenge of Clinical Measurement • Diagnoses are based on information - from formal measurements and/or from clinical judgment. • This information is seldom perfectly accurate: • Random errors can occur (machine needs calibrating?) • Biases in judgment or measurement can occur (“this patient seems anxious: is he exaggerating?”) • 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. How to choose a cutting-point?

3. Therefore… • You need to be aware … • That we express these complexities in terms of 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 how we describe the accuracy of a measurement.

4. Test characteristics • 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. 4

5. 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 •

6. 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) 6

7. 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 that compares the test to a gold standard. 7

8. “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

9. Sensitivity = test’s ability to detect disease when it is present a/(a+c) = TP/(TP+FN) = TP/disease A sensitive person is one who can perceive your feelings (1 – seNsitivity) = false Negative rate: how many cases are missed by the test? • Specificity = precision of the test: identifies only that type of disease. “Nothing else looks like this” • A specific test generates few false positives. So, if the result is positive, the patient has this diagnosis. • (1- sPecificity) = false Positive rate: how many are falsely classified as having the disease?) 9

10. Test Errors • False Positives can arise due to other factors (diet; taking other medications, 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: an adverse event. • Cf. Type II or beta error: the chance of missing a true difference 10

11. 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

12. Improving the test: Healthy population Sick population Healthyscores Pathologicalscores Improved testreduces overlap,increasing sen & spec.

13. D + D - a b T + T - c d Clinical applications • A specific test can be useful to rule in a disease. Why? • 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”

14. 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. • The clinician sees the test result, but does not 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) test result predict disease (or health)?

15. Start from Prevalence • Before you apply 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. You will then refine this informed guess in a series of stages: • First, consider the patient’s age and sex; use the prevalence for a similar person. • Then, based on the patient’s history you may modify the estimate.

16. 2 x 2 table: Prevalence

17. D + D - a b T + T - c d Predictive Values • Based on rows, not columns • PPV = a/(a+b); interprets positive test: false positive rate • NPV = d/(c+d); interprets negative test: false negative rate • Immediately useful to clinician: they tell us about the test in this population and thus this patient • Vary with the prevalence of disease, so must be determined for each clinical setting • As prevalence goes down, PPV goes down and NPV rises

18. 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%

19. Exercise ECG (aka "treadmill test") • A 22 year old male with chest pain has a pretest probability of obstructive CAD of roughly 1%. • With a "positive" exercise ECG, his posttest probability is still less than 5%, in other words, there's a greater than 95% chance that he doesn’t have important CAD, despite a "positive" test. • The same applies in the opposite direction for a 72 year old male with typical anginal chest pain.  Pretest probability is 95%; if the exercise ECG is negative, the posttest probability is still probably greater than 80%. • The overarching guideline is to treat the patient, not the test. To display the effects of changing cut-points and prevalence on predictive values, click here. (scroll down to the middle of the page)

20. 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)

21. D + D - TP FP T + T - FN TN Predictive Values • High specificity = few FPs: Sp = TN/(TN+FP).FPs also drive PPV: PPV = TP/(TP + FP);So, with a high PPV 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. 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

23. 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 cut-points on the test • Can be turned into predictive values • LR for positive test = Sensitivity / (1-Specificity) • LR for negative test = (1-Sensitivity) / Specificity

24. 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%

25. 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

26. 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

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

28. 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)

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