Screening test (basic concepts)

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Diagnostic, screening tests, differences and applications and their characteristics, four pillars of screening tests, sensitivity, specificity, predictive values and accuracy

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Screening test (basic concepts)

  1. 1. Screening Professor Tarek Tawfik Amin Public Health Cairo University amin55@myway.com
  2. 2. Objectives By the end of the session 4th medical students should be able to: - Identify the difference between diagnostic and screening test. - Identify and recall the pillars of screening test accuracy. - Interpret the output of accuracy parameters of screening test.
  3. 3. The patient’s scenario Mrs. Zi has discovered a lump in her left breast. So what?
  4. 4. Diagnostic tests: ordered to answer a specific question Diagnostic tests help physicians revise disease probability for their patients. 1. Establish a diagnosis in symptomatic patients. ECG to diagnose ST-elevation myocardial infarction in patients with chest pain. 2. Screen for disease in asymptomatic patients. Prostate-specific antigen (PSA) test in men older than 50 years. 3. Provide prognostic information in patients with established disease. CD4 count in patients with HIV. 4. Monitor therapy by either benefits or side effects. Measuring the international normalized ratio (INR) in patients taking warfarin. 5. Confirm that a person is free from a disease. Pregnancy test to exclude the diagnosis of ectopic pregnancy.
  5. 5. Criterion (reference test) • The criterion (reference) standard test definitively decides either presence or absence of a disease. Pathological specimens for malignancies and pulmonary angiography for pulmonary embolism. • Criterion standard tests routinely come with drawbacks; expensive, less widely available, and more invasive. Compel physicians to choose other diagnostic tests as surrogates (alternative test). Venography, the criterion standard for vein thrombosis, is an invasive procedure with significant complications [renal failure, allergic reaction, and clot formation]. Venography less desirable than the alternative diagnostic test—venous duplex ultrasonography. • The price most diagnostic tests (surrogates) pay for their ease of use compared with their criterion standard is a decrease in accuracy.
  6. 6. Screening Vs. Diagnostic tests • Screening tests are not diagnostic tests • The primary purpose of screening tests is to detect early disease or risk factors for disease in large numbers of apparently healthy individuals. • The purpose of a diagnostic test is to establish the presence (or absence) of disease as a basis for treatment decisions in symptomatic or screen positive individuals (confirmatory test).
  7. 7. Screening tests Diagnostic tests 1-To detect potential disease indicators 1- To establish presence/absence of disease 2-Large numbers of asymptomatic, but potentially at risk individuals 2-Symptomatic individuals to establish diagnosis, or asymptomatic individuals with a positive screening test 3-Simple, acceptable to patients and staff 3-Maybe invasive, expensive but justifiable as necessary to establish diagnosis 4-Generally chosen towards high sensitivity not to miss potential disease 4-Chosen towards high specificity (true negatives). More weight given to accuracy and precision than to patient acceptability 5-Essentially indicates suspicion of disease 5- Result provides a definite diagnosis 6-Cheap, benefits should justify the costs since large numbers of people will need to be screened to identify a small number of potential cases 6-Higher costs associated with diagnostic test maybe justified to establish diagnosis.
  8. 8. The validity of a screening test: sensitivity and specificity o The measures of sensitivity and specificity describe how well the proposed screening test performs against an agreed 'Gold Standard' test. o In medicine, a gold standard test or criterion standard test is a diagnostic test or benchmark that is regarded as definitive. o The actual gold standard test may be too unpleasant for the patient, too impractical or too expensive to be used widely as a screening test
  9. 9. Sensitivity and Specificity • The outcomes of any screening test • The calculations
  10. 10. Screening Disease status as determined by 'Gold Standard' Disease No Disease Test positive True positives (a) False positives (b) Total test positives (a+b) → Positive predictive value Test negative False negatives (c) True negatives (d) Total test negatives (c+d) → Negative predictive value Total with disease (a+c) Total without disease (b+d) Total screened (a+b+c+d) ↓ Sensitivity ↓ Specificity True positives = number of individuals with disease and a positive screening test (a) False positives = number of individuals without disease but have a positive screening test (b) False negatives = number of individuals with disease but have a negative screening test (c) True negatives = number of individuals without disease and a negative screening test (d) Missed cases Labeling effect
  11. 11. 1-Sensitivity • Sensitivity is defined as the ability of the test to detect all those with disease in the screened population. This is expressed as the proportion of those with disease correctly identified by a positive screening test result • Sensitivity = Number of true positives Total with disease = a/ (a+c)
  12. 12. 2-Specificity • Specificity is defined as the ability of the test to identify correctly those free of disease in the screened population. This is expressed as the proportion of those without disease correctly identified by a negative screening test result • Specificity = Number of true negatives Total without disease = d/ (b+d)
  13. 13. 3-Positive Predictive Value • The positive predictive value (PPV) describes the probability of having the disease given a positive screening test result in the screened population. • How many of +ve (s) at screening are actually having the disease? This is expressed as the proportion of those with disease among all screening test positives. • PPV = Number of true positives total test positives PPV = a / (a+b)
  14. 14. 4-Negative Predictive Value • The negative predictive value (NPV) describes the probability of not having the disease given a negative screening test result in the screened population. • How many of –ve (S) are not diseased? This is expressed as the proportion of those without disease among all screening test negatives. • NPV = Number of true negatives total test negatives NPV= d / (c+d)
  15. 15. Disease Prevalence Effect • Sensitivity and specificity are independent of prevalence of disease, i.e. test specific (they describe how well the screening test performs against the gold standard). • PPV and NPV however are disease prevalence dependant, i.e. population specific. PPV and NPV give information on how well a test screening test will perform in a given population with known prevalence. • Generally a higher prevalence will increase the PPV and
  16. 16. Example A new ELISA (antibody test) is developed to diagnose HIV infections. Serum from 10,000 patients that were positive by Western Blot (the Gold Standard assay) was tested, and 9,990 were found to be positive by the new ELISA screening test. The manufacturers then used the ELISA to test serum from 10,000 nuns who denied risk factors for HIV infection. 9,990 were negative and the 10
  17. 17. HIV [Nuns and HIV patients] Infected Not infected ELISA test + 9,990 (a) 10 (b) - 10 (c) 9,990 (d) 10,000 (a+c) 10,000 (b+d) Sensitivity = a/(a+c) = 9990/(9990+10) = 99.9% Specificity= b/(b+d) = 9990/(9990+10) = 99.9% Excellent test
  18. 18. On population level The test is applied to a million people where 1% are infected with HIV (assuming the sensitivity and specificity remain the same). Of the million people, 10,000 would be infected with HIV. Since the new ELISA is 99.9% sensitive, the test will detect 9,990 (true positives) people who are actually infected and miss 10 (false negative). Looking at those numbers the test appears very good because it detected 9,990 out of 10,000 HIV infected people. But there is another side to the test. Of the 1 million people in this population, 990,000 are not infected. Looking at the test results of the HIV negative population (the specificity of the assay is 99.9%), 989,010 are found to be not
  19. 19. 1% Prevalence HIV Infected Not infected Test + 9990 (a) 990 (b) Test positives a+b PPV= a/(a+b) = 9990/(9990+990) =91% - 10 (c) 989,010 (d) Test negatives c+d NPV= d/(c+d) =989,010/ (10+989,010) = 99.9% HIV positive 10,000 HIV negative 999,000 Total screened= a+b+c+d Sensitivity = 99,9% Specificity = 99,9% Sensitivity and specificity are not the only performance features because they do not address the problems of the prevalence of disease in different populations. For that, the understanding of the positive and negative predictive value is crucial.
  20. 20. Blood donors have already been screened for HIV risk factors before they are allowed to donate blood, so that the HIV sero-prevalence in this population is closer to 0.1% instead of 1%. For every 1,000,000 blood donors, 1,000 are HIV positive. With a sensitivity of 99.9%, the ELISA would pick up 999 of those thousand, but would fail to pick up one HIV sero- positive individual. Of the 999,000 uninfected individuals, the test would label 998,001 individuals assero-negative (true negatives).
  21. 21. Blood donors 0.1% Prevalence HIV + - Test + 999 (a) 999 (b) Test positives 1,998 PPV= a/(a+b) =50% - 1 (c) 998,001 (d) Test negatives 998,002 NPV= d/(c+d) =99.999% HIV positive 1000 HIV negative 999,000 Total a+b+c+d Sensitivity 99.9% Specificity 99.9%
  22. 22. • The second population consists of former IV drug users attending drug rehabilitation units, with a prevalence of 10%. For a million of these individuals, 100,000 would be HIV-infected and 900,000 would be HIV negative.
  23. 23. 10% Prevalence HIV + - Test + 99,900 (a) 900 (b) Test positives 100,800 PPV= a/(a+b) =99% - 100 (c) 899,100 (d) Test negatives 899,200 NPV= d/(c+d) =99.999% HIV negative 100,000 HIV negative 900,000 Total screened= a+b+c+d Sensitivity 99.9% Specificity 99.9%
  24. 24. • The sensitivity and specificity of the test has not changed. It is just that the predictive value of the test has changed depending on the population being tested. • The positive predictive value is how many of the test-positives truly have the disease. In the first example with a 1% sero-positive rate, the ELISA has a positive predictive value of 0.91 (91%). When Remarks
  25. 25. Rema rks Although the sensitivity of the ELISA does not change between populations, the positive predictive value changes drastically from only half the people that tested positive being truly positive in a low- incidence population to 99% of the people testing positive being truly positive in the high- prevalence population. The negative predictive value of the
  26. 26. Derivatives of screening
  27. 27. Term Calculation Plain English True positive (TP) Counts in 2 X 2 table # Patients with the disease who have a positive test result True negative (TN) Counts in 2 X 2 table # Patients without the disease who have a negative test result False positive (FP) Counts in 2 X 2 table # Patients without the disease who have a positive test result False negative (FN) Counts in 2 X 2 table # Patients with the disease who have a negative test result Sensitivity = True positive rate (TPR) TP / (TP + FN) The probability that a patient with the disease will have a positive test result 1 - Sensitivity = False-negative rate (FPR) FN / (TP + FN) The probability that a patient with the disease will have a negative test result Specificity = True negative rate (TNR) TN / (TN + FP) The probability that a patient without the disease will have a negative test result 1 - Specificity = False-positive rate (FPR) FP / (TN + FP) The probability that a patient without the disease will have a positive test result Positive predictive value TP / (TP + FP) The probability that a patient with a positive test result will have the disease Negative predictive value TN / (TN + FN) The probability that a patient with a negative test result will not have the disease. Accuracy (TP + TN) / (TP + TN + FP + FN) The probability that the results of a test will accurately predict presence or absence of disease Bayes’ theorem Posttest Odds = Pretest Odds X Likelihood Ratio The odds of having or not having the disease after testing Likelihood ratio of a positive test result (LR+) Sensitivity / (1 - Specificity) The increase in the odds of having the disease after a positive test result Likelihood ratio of a negative test result (LR-) (1 - Sensitivity) / Specificity The decrease in the odds of having the disease after a negative test result
  28. 28. Thank you

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