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Diagnostic test

Diagnostic test. Wenjie Yang ywjie@zzu.edu.cn 2009.12. Questions. A patient presents to us with a chief complaint Why do we order tests? What tests to order? Based on what? What do we hope to achieve as we get the result of the test?

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Diagnostic test

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  1. Diagnostic test Wenjie Yang ywjie@zzu.edu.cn 2009.12

  2. Questions • A patient presents to us with a chief complaint • Why do we order tests? • What tests to order? Based on what? • What do we hope to achieve as we get the result of the test? • What if there are multiple tests that are related to this complaint? • What if we are considering 6 or 7 possible diagnoses that might explain this chief complaint?

  3. Test-Treatment Threshold Post-test probability

  4. Assessing the validity and reliability of diagnostic tests? • Choose diagnostic tests wisely • Interpret the result of diagnostic tests

  5. 1. Diagnostic Index Subjective Index headache, dizzy, disgusting ... Semi-subjective (or semi-objective) hardness of liver, rale in lung... Objective index blood pressure value, blood sugar value, blood cells counting...

  6. “ Gold Standard” the most accurate and reliable diagnostic method(s). chest X-ray and sputum smear --- pneumonia electrocardiogram (ECG) and serum enzyme ---acute myocardial infarction tissue biopsy --- cancer

  7. Diabetes OGTT (oral glucose tolerance test) blood sugar test urine sugar test

  8. Gold standard True Positive(a) ＋ patient False Negative(c) Clients － Diagnostic test False Positive(b) ＋ Non-patient － True Negative(d) Assessing the Validity of Diagnostic Tests

  9. The 2x2 Table describes test outcomes: Disease present Disease absent Group (a) True Positive Group (b) False Positive Positive result Group (c) False Negative Group (d) True Negative Negative result

  10. Sensitivity: proportion of those with disease • who test positive • (a) • (a) + (c) Disease present Disease absent Group (a) True Positive Group (b) False Positive Positive result Group (c) False Negative Group (d) True Negative Negative result

  11. 2) Specificity: proportion of those without disease who test negative (d) (b) + (d) Disease absent Disease present Group (a) True Positive Group (b) False Positive Positive result Group (c) False Negative Group (d) True Negative Negative result

  12. The Ideal Situation--100% Agreement Disease present Disease absent n = 200 n = 800 200 True positive 0 False positive Positive result 0 False negative 800 True negative Negative result

  13. A More Likely Outcome Disease present Disease absent n = 200 n = 800 170 True Positive 30 False Positive Positive result 30 False Negative 770 True Negative Negative result

  14. Consequences of a False Positive • Even 3-5% will be large on a population level • Follow-up tests, cost, potential harm, anxiety • Consequences of a False Negative • Even one person can have tragic implications • At best, a false sense of security • Might neglect future tests

  15. Changing a Diagnostic Cut Point

  16. Uses of sensitive tests: when there is an important penalty for missing a disease ; when a great many possibilities are being considered, in order to reduce the number of possibilities; when the probability of disease is relatively low and the purpose of the test is to discover disease.

  17. Uses of specific tests When to confirm a diagnosis that has been suggested by other tests. When false positive results bring severe harm to the client physically, emotionally, or financially.

  18. If a test result is positive, how likely is it that this individual has the disease?

  19. 3. Predictive value • Definition: The probability of disease, given the results of a test. Characteristics of Screening Tests

  20. Positive Predictive Value (PPV): The likelihood that a positive test result indicates the existence of the disease (a) (a) + (b) Disease present Disease absent Group (a) True Positive Group (b) False Positive Positive result Group (c) False Negative Group (d) True Negative Negative result

  21. Negative Predictive Value (NPV): The likelihood that a negative test result indicates the absence of the disease (d) (c) + (d) Disease present Disease absent Group (a) True Positive Group (b) False Positive Positive result Group (c) False Negative Group (d) True Negative Negative result

  22. The relationship between predictive value and Se, Sp, P (prevalence) Se × P +PV= (Se × P)+(1-Sp) × (1-P) (1-P) × Sp - PV= (1-P) × Sp + P × (1-Se)

  23. Bayes’ theorem: As the prevalence of a disease increases, the positive predictive value of the test increases (PPV) and its negative predictive value (NPV) decreases.

  24. Predictive Values and Prevalence Sensitivity = 99%; Specificity = 95%

  25. 4. Multiple Test

  26. Parallel testing Test A or test B or test C is positive Test A and test B and test C are negative A + _ Sensitivity B + _ Specificity C + _

  27. Test A and test B and test C is positive Test A or test B or test C are negative • Serial testing A + B + C + - - - Sensitivity Specificity

  28. Effect of parallel and serial testing on sensitivity, specificity,and predictive value of test combinations test Se (%) Sp (%) PPV(%) NPV(%) A 80 60 33 92 B 90 90 69 97 A or B (parallel) 98 54 35 99 A and B (serial) 72 96 82 93 for 20%prevalence

  29. Test A Test B Patient + + 70 + - 15 - + 10 - - 5 Non-patient + + 10 + - 15 - + 20 - - 55

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