Diagnosis:Testing the Test

Verma Walker

Kathy Davies

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.

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.

Gold Standard Investigation Positive n Negative nHistology Positive 28 0

Negative 3 44

RUT Positive 30 0Negative 1 44

Culture Positive 22 0Negative 9 44

13C-UBT Positive 30 3Negative 1 41

Gold Standard Positive

(condition present)

Gold Standard Negative

(condition absent)

Test Result Positive True Positive 30

a

False Positive

3

b

Test Result Negative 1 c

False Negative

d 41

True Negative

Sensitivity

• the proportion of truly diseased persons, as measured by the gold standard, who areidentified as diseased by the test under study.

• True Positives/(True Positives + False Negatives)

• a/(a+c) • Sensitivity = Snout = Rules Out

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

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 theprevalence of the condition for whichthe test is used.

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

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

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

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

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

•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)

Nomogram

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

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?

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 lab error

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

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<http://www.med.ualberta.ca/ebm/ebm.htm>

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. 4th Edition: Lippincott, Williams & Wilkins, 2000

Schwartz, Alan. EBM and Decision Tools: Diagnostic Test Cutoffs <http://araw.mede.uic.edu/cgi-bin/cutoff.cgi>