1. Diagnosis:Testing the Test Verma Walker Kathy Davies

2. Journal of Pediatric Gastroenterology & Nutrition. 35(1):39-43, 2002 Jul. BACKGROUND AND OBJECTIVE: Studies support the accuracy of 13C-urea breath test for diagnosing and confirming cure of Helicobacter pylori infection in children. Three methods are used to assess 13CO2 increment in expired air: mass spectrometry, infrared spectroscopy, and laser-assisted ratio analysis. In this study, the 13C-urea breath test performed with infrared spectroscopy in children and adolescents was evaluated 13 C-urea breath test with infrared spectroscopy for diagnosing helicobacter pylori infection in children and adolescents.

3. METHODS: Seventy-five patients (6 months to 18 years old) were included. The gold standard for diagnosis was a positive culture or positive histology and a positive rapid urease test. Tests were performed with 50 mg of 13C-urea diluted in 100 mL orange juice in subjects weighing up to 30 kg, or with 75 mg of 13C-urea diluted in 200 mL commercial orange juice for subjects weighing more than 30 kg. Breath samples were collected just before and at 30 minutes after tracer ingestion. The 13C-urea breath test was considered positive when delta over baseline (DOB) was greater than 4.0% RESULTS: Tests were positive for H. pylori in 31 of 75 patients. Sensitivity was 96.8%, specificity was 93.2%, positive predictive value was 90.9%, negative predictive value was 97.6%, and accuracy was 94.7%. CONCLUSIONS: 13C-urea breath test performed with infrared spectroscopy is a reliable, accurate, and noninvasive diagnostic tool for detecting H. pylori infection.

4. Gold Standard Investigation Positive nNegative n HistologyPositive 280 Negative 344 RUTPositive 300 Negative 144 CulturePositive 220 Negative 944 13C-UBTPositive 303 Negative 141

5. Gold Standard Positive (condition present) Gold Standard Negative (condition absent) Test Result PositiveTrue Positive 30 a False Positive 3 b Test Result Negative 1 c False Negative d 41 True Negative

6. Sensitivity the proportion of truly diseased persons, as measured by the gold standard, who are identified as diseased by the test under study. True Positives/(True Positives + False Negatives) a/(a+c) Sensitivity = Snout = Rules Out

7. Specificity The proportion of truly non-diseased persons, as measured by the gold standard, who are so identified by the diagnostic test under study. True Negatives/(False Positive + True Negative) d/(b+d) Specificity = Spin = Rules In

9. Predictive Values In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., does have the disease), or that a person with a negative test truly does not have the disease. The predictive value of a screening test is determined by the sensitivity and specificity of the test, and by the prevalence of the condition for which the test is used.

10. Positive Predictive Value True Positive/(True Positive + False Positive) a/(a+b) Probability that a person with positive test is a true positive (does have the disease) Negative Predictive Value True Negative/(True Negative + False Negative) d/(d+c) Probability that a person with a negative test truly does not have the disease

11. Using Predictive Values Keep clinical significance in mind – Terminal or rare disease – Impact of false negative on patient outcome –Benefit of testing to patient Population tested is high or low risk? Alternative Tests for screening

12. Likelihood Ratios The likelihood ratio for a test result compares the likelihood of that result in patients with disease to the likelihood of that result in patients without disease: Positive LR = (a/a+c)/(b/b+d) –sensitivity / (1-specificity) Negative LR = (c/a+c)/(d/b+d) –(1-sensitivity) / specificity

13. Impact on Disease Likelihood LR >10 or <0.1 cause large changes in likelihood LR 5-10 or 0.1-0.2 cause moderate changes LR 2-5 or 0.2-0.5 cause small changes LR between <2 and 0.5 cause little or no change

14. Ruling In & Out Does patient have disease ? Higher Positive LR means disease is likely to be present if test is positive Does patient not have disease? Lower Negative LR means that disease is not likely present or cause of patient current condition

15. Prevalence Proportion of persons with a particular disease within a given population at a given time. Probability that a person selected at random will have disease. (a+c) / (a+b+c+d) Pre-test odds Odds that a person will have the disease; calculated before test is complete. prevalence / (1-prevalence) Post-test odds Measures impact of test result on odds of disease being present pre-test odds * LR Post-test probability Chances of disease after factoring in test results post-test odds / (post test odds+1)

16. Nomogram

18. Clinical Implications One test is not a diagnosis Implications of false positive Further testing may be needed Numbers may be significant but not clinically relevant

19. Number Meanings 100,000 men studied for coronary artery disease Uric Acid Factor in prediction Developed CA disease uric acid=7.8 mg/L Did not develop CA disease uric acid= 7.7 mg/L P Value = 0.05– significant Problems?

20. Number Meanings Large study found significant difference for very small difference in values Unlikely that uric acid will be useful as clinical predictor When test is performed, difference is less than any l ab error

21. Purposes of Statistics Estimate relationships between variables, cause & effect and differences in magnitude Measure the significance of the results; do the numbers have any clinical meaning? Adjust for the impact of confounding variables on results

22. Bibliography Center for Evidence Based Medicine. Ed. Douglas Badenoch, Olive Goddard, Bridget Burchell, Sept. 2002. NHS Research and Development. 1 Oct. 2002 http://www.minervation.com/cebm/docs/likerats.html Evidence Based Medicine Tool Kit. Ed. Jeanette Buckingham, Bruce Fisher, Duncan Saunders. Nov. 2000. University of Alberta. 5 Sept. 2002 Kawakami, Elisabete. 13C-Urea Breath Test with infrared spectroscopoy for diagnosing Helicobacter pylori infection in children and adolescents. Journal of Pediatric Gastroenterology and Nutrition 2002; 35(1): 39-43. Riegelman, Richard. Studying a Study and Testing a Test: How to read the Medical evidence. 4 th Edition: Lippincott, Williams & Wilkins, 2000 Schwartz, Alan. EBM and Decision Tools: Diagnostic Test Cutoffs