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Diagnostic Tests

Diagnostic Tests. How to crunch the numbers for clinical practice David Nguyen PGY-1 Anaesthesiology University of Calgary. This requires some math, but it’s not too bad. Overview. Diagnostic process Basics Epistats 2 x 2 table

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Diagnostic Tests

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  1. Diagnostic Tests How to crunch the numbers for clinical practice David Nguyen PGY-1 Anaesthesiology University of Calgary

  2. This requires some math, but it’s not too bad

  3. Overview • Diagnostic process • Basics Epistats • 2 x 2 table • Definitions - Sensitivity, specificity, PPV, NPV, prevalence • Interpreting Diagnostic Tests • SPIN SNOUT • Likelihood ratios • Going beyond sensitivity and specificity

  4. Cases • Strep throat • Iron deficiency Anemia

  5. BASICS

  6. 2 x 2 tables • Let’s say we are performing a diagnostic test for a disease on a given population. • The test has dichotomous results (positive or negative) • A proportion of this population has the disease

  7. Where (a + b + c + d) = total population of interest

  8. Definitions • Stating it mathematically: • Sensitivity = the probability of obtaining a positive test, GIVEN that a person is diseased • Specificity = the probability of obtaining a negative test, GIVEN that a person is not diseased

  9. The Validity of Screening Tests • The validity of a test refers to how well it can distinguish diseased people from non-diseased people. • Measures of validity are expressed quantitatively: sensitivity (SN) and specificity (SP). • As SN and SP approaches 100%  the validity of the test improves  the better the test performs on population level studies  which probably means the test will be more useful clinically

  10. Other very important terminology • Positive predictive value (PPV) = the probability of being diseased GIVEN that a test is positive • Negative predictive value (NPV) = the probability of being non-diseased GIVEN that a test is negative

  11. What’s the difference between SN/SP and PPV/NPV • Sensitivity and specificity are inherent characteristics of a diagnostic test • PPV and NPV are not inherent characteristics of a diagnostic test. • PPV and NPV depends on sensitivity, specificity and prevalence of disease (aka pre-test probability).

  12. Interpreting Diagnostic Tests

  13. Why do we order diagnostic tests? • To help rule in a disease • To help rule out a disease *Others will argue that the point of ordering diagnostic test is to improve patient health. Ideally, your test changes your management plan.

  14. SPIN SNOUT

  15. SPINSpecific, Positive, rule IN SNOUT Sensitive, Negative, Rule OUT

  16. Probabilities • Pre-test probability = defined as the probability a person has a disease prior to performing the diagnostic test (often synonymous to prevalence of disease) • Post-test probability = defined as the probability a person has a disease after performing the diagnostic test

  17. The Steps to Calculating Post-test Probability

  18. Step 1: Estimating Pre-test probability • Estimated through • History • Physical examination • Investigations • Clinical prediction tools • Others – knowing the prevalence of disease, clinical experience

  19. Step 1: Estimate pre-test probability Step 2: Convert pre-test probability to pre-test odds: Step 3: Calculate likelihood ratios from SN and SP Step 4: Convert pre-test odds to post-test odds: Step 5: Convert post-test odds to post-test probability:

  20. General rule of thumb Are strong! These can often lead to conclusive changes from pretest to posttest probability. LR = 1 is useless.

  21. Likelihood ratio nomogram

  22. Practice

  23. Practice Case #1 • 15 year old male presents with a 3 day history of sudden onset sore throat and fever. • No cough, no rhinorrhea, no conjunctivitis. • Physical examination = 37.9C, tender anterior cervical lymphadenopathy, swollen tonsils with exudate, no hepatosplenomegaly, no rashes

  24. Group A Strep Pharyngitis is high on your differential. • You performed a swab throat and culture. Several days later, the test comes back positive. • A literature search shows one study that found throat swabs have an 85% sensitivity and 95% specificity. • How do you interpret this?

  25. Step 1 - 5 Step 1 – pre-test probability Step 2 – pre-test odds Step 3– calculate LR Step 4 – post-test odds Step 5 – post-test probability

  26. Practice case #1: Step 1 – estimating the pre-test probability • You decide to use the Strep Pharyngitis Score (a clinical prediction tool!) to estimate the pre-test probability • Pre-test probability = 35% McIsaac, Warren J., et al. "A clinical score to reduce unnecessary antibiotic use in patients with sore throat." Canadian Medical Association Journal 158.1 (1998): 75-83.

  27. Step 1 - 5 Step 1 – pre-test probability Step 2 – pre-test odds Step 3– calculate LR Step 4 – post-test odds Step 5 – post-test probability 35%

  28. Step 1 - 5 Step 1 – pre-test probability Step 2 – pre-test odds Step 3– calculate LR Step 4 – post-test odds Step 5 – post-test probability 35%

  29. Step 1 - 5 Step 1 – pre-test probability Step 2 – pre-test odds Step 3– calculate LR Step 4 – post-test odds Step 5 – post-test probability 35%

  30. Step 1 - 5 Step 1 – pre-test probability Step 2 – pre-test odds Step 3– calculate LR Step 4 – post-test odds Step 5 – post-test probability 35%

  31. Step 1 - 5 Step 1 – pre-test probability Step 2 – pre-test odds Step 3– calculate LR Step 4 – post-test odds Step 5 – post-test probability 35%

  32. Likelihood ratio nomogram

  33. Practice #2 • Let’s suppose another patient comes in complaining of pharyngitis • You determine their pre-test probability is 1% based on a pharyngitis score of 1 • The guideline does not recommend throat swabs for this prevalence. • What if you decide to swab anyways? What would be their post-test probability?

  34. Likelihood ratio nomogram Post-test probability if positive = 14.7%

  35. Practice #3 • 2 year old boy presents to GP office for pallor • Active, no fatigue, no change in activity or sleep. No blood, black, tar in stool. • Picky eater – little meats and vegetables. Drinks mostly milk (8 bottles per day). • Physical: T 37, BP 90/50, P 145, RR 16, Height 85.5 cm (50th %ile), Weight 13.2 kg (75th %ile). Appears pale, active toddler and tearing. HEENT: dental caries. Resp: normal. CVS: Mild tachycardia, II/VI syst ejection murmur heard best over the upper left sternal border. Abdomen normal. Rectal: Dark brown, soft stool, negative for occult blood. http://www.hawaii.edu/medicine/pediatrics/pedtext/s11c01.html

  36. CBC = 6.2 g/dl (anemic) • Serum ferritin was ordered. Knowing that the cutoff for a positive test is < 12, how will you interpret the results if the serum ferritin comes back: • Serum ferritin = 5 • Serum ferritin = 13 • Serum ferritin = 101

  37. You estimate the pre-test probability of this child’s anemia is due to iron deficiency anemia at 50%. Baker, Robert D., and Frank R. Greer. "Diagnosis and prevention of iron deficiency and iron-deficiency anemia in infants and young children (0–3 years of age)." Pediatrics 126.5 (2010): 1040-1050.

  38. Diagnostic Tools for Iron-Deficiency Anemia Data adapted and modified for purposes of this presentation: Guyatt, Gordon H., et al. "Laboratory diagnosis of iron-deficiency anemia." Journal of general internal medicine 7.2 (1992): 145-153.

  39. Diagnostic Tools for Iron-Deficiency Anemia Guyatt, Gordon H., et al. "Laboratory diagnosis of iron-deficiency anemia." Journal of general internal medicine 7.2 (1992): 145-153.

  40. Back to our question • CBC = 6.2 g/dl (anemic) • Serum ferritin was ordered. Knowing that the sensitivity = 59% and specificity = 99% and the cutoff for a positive test is < 12, how will you interpret the results if the serum ferritin comes back: • Serum ferritin = 5 • Serum ferritin = 13 • Serum ferritin = 101

  41. Step 1 - 5 Step 1 – pre-test probability Step 2 – pre-test odds Step 3– calculate LR Step 4 – post-test odds Step 5 – post-test probability 40% 0.67 LR(+) = 59 LR( - ) = 0.41 Post-test odds (< 12) = 39.3 Post-test odds ( 12) = 0.41 Post-test probability (pos, <12) = 98% Post-test probability (neg,  12) = 22%

  42. Calculating Post-test probability from stratified likelihood ratios 98% for positive test (<12) or 22% for negative test (12)

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