diagnosis of helicobacter pylori infection.ppt

Evidence-Based Medicine:Diagnosis of Helicobacter pylori infection

Clinical ScenarioThe patient is a middle aged male with symptoms of dyspepsia. Your first concern in managing this patient is to determine if the dyspepsia is related to peptic ulcer disease. This generally requires endoscopy, which is a minimally invasive, but a relatively expensive diagnostic test. To decrease the use of endoscopy, you have been considering the strategy of testing for H. pylori, treating those that are positive, and then doing an endoscopy on those that remain symptomatic.

Clinical ScenarioThe standard laboratory ELISA has been the favored screening test for H. pylori but this test can take days to accomplish. Recently you have read about more convenient in-office or "near patient" whole-blood tests (such as FlexSure) that can show results within hours.Before you consider a change in practice, you want to find out if the these "near patient" tests, such as FlexSure, are as sensitive as the "gold standard" ELISA test for detecting H. pylori.

Steps in Practicing EBMConvert the need for information into an answerable question.Track down the best evidence with which to answer that question.Critically appraise the evidence for its validity, impact, and applicability.Integrate the evidence with our clinical expertise and our patients characteristics and values.Evaluating our effectiveness and efficiency in executing steps 14 and seeking ways to improve them both for next time.

The clinical question: PICO

The clinical question

In adults with suspected H. pylori, are the "near patient" whole-blood tests such as FlexSure, as accurate as the ELISA assay in aiding diagnosis?

The search strategyPubmed database: (http://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed)Using the Clinical Queries function of PubMed:Key words:h pylori ANDflexsureClinical Study Categories: DiagnosisScope: Narrow

The EvidenceDuggan AE, Elliott, C. Logan RF.Testing for Helicobacter pylori infection: validation and diagnostic yield of a near patient test in primary care. BMJ. 319(7219):1236-9,1999 Nov 6. Testing for Helicobacter pylori infection - validation and diagnostic yield of a near patient test in primary care.pdf

Appraising the EvidenceIs this evidence about the accuracy of a diagnostic test valid?Does this (valid) evidence demonstrate an important ability of this test to accurately distinguish patients who do and do not have a specific disorder?Can I apply this valid, important diagnostic test to a specific patient?

Is this evidence about the accuracy of a diagnostic test valid?

Is this evidence about the accuracy of a diagnostic test valid?MeasurementWas the reference (gold) standard measured independently, i.e. blind to our target test?Yes. A 7ml blood sample was taken from each patient. The FlexSure test was administered at the variou sites. Then the remaining blood test was sent to University Hospital were all samples were also tested with the ELISA assay by a single operator blinded to the FlexSure results. (under Methods, p. 1237).

Is this evidence about the accuracy of a diagnostic test valid?RepresentativeWas the diagnostic test evaluated in an appropriate spectrum of patients (those in whom we would use it in practice)?Yes. Patients were from an appropriate spectrum representing a wide range of ages, duration and severity of symptoms. Patients were recruited from 43 general practices in England.

Is this evidence about the accuracy of a diagnostic test valid?AscertainmentWas the reference standard ascertained regardless of the diagnostic test result?Yes, 389 of 394 patients were tested by the reference standard ELISA assay regardless of the results of the FlexSure test. 5 patients did not have serum available for the ELISA testing. (See Results p. 1237).

Are the results of this study valid? This is a well designed study according to ACP Journal Club.

Does this (valid) evidence demonstrate an important ability of this test to accurately distinguish patients who do and do not have a specific disorder?

2 x 2 TableSensitvity = a/(a+c).Specificity = d/(b+d).Positive Predictive Value = a/(a+b).Negative Predictive Value = d/(c+d).

Technical vs. Clinical Precision Baby Jeff: The case of screening for muscular dystrophy at HHTechnical Precision of CPK test:Sensitivity (ability to rule out disease): 100%Specificity (ability to identify disease): 99.98%But, The prevalence of MD is 1 in 5000 (0.02%)

Does Baby Jeff have M.D.?Of 100,000 males, 20 will have M.D. (1 in 5,000, or 0.02% prevalence)The test will correctly identify all 20 who have the disease (sensitivity = 100%)

Does Baby Jeff have M.D.?Of the 99,980 without M.D.Specificity = 99.98%99,980 x 0.9998 = 99,960 will be negativeTherefore, false positives = 20

. . . The Rest of the StoryTherefore,Out of 100,000 infants, 20 will be truly positive and 20 will be false positivePositive predictive value = 50%The child with a positive screening test only has a 50/50 chance of actually having MD!HARM!

Another Example: Lyme DiseaseAntibody assaySensitivity= 95%; specificity= 95%High Lyme Disease prevalence (20%)Positive predictive value = 83%Low Lyme Disease prevalence (2%)Positive predictive value = 28%

Brown SL. JAMA 1999;282:62-6.

Another Example: MammographyMammography in women between 40-50 yrsIf 100,000 women are screened:

6,034 mammograms will be abnormal5,998 (99.4%) will be false-positive 36 will actually have breast cancer

Why? Prevalence = 0.036%Hamm RM. J Fam Pract 1998;47:44-52.

What are the results? Results were available for 375 patients (9 patients had indeterminate ELISA results, 5 had invalid FlexSure results, and 8 had no serum available). 36% of patients had H. pylori infection.

Technical precision Sensitivity: measures the proportion of patients with the disease who also test positive for the disease in this study. Sensitivity = true positive / all disease positives= 90/134 = 67% 67% of the patient who had H. pylori infection, tested positive for the disease

Technical precisionSpecificity: measures the proportion of patients without the disease who also test negative for the disease in this study. Specificity = true negative / all disease negatives = 236/241= 98% 98% of the patients who did not have H. pylori, tested negative for the disease.

Clinical precision Positive Predictive Value: measures the proportion of patients tested positive for the disease who have H. pylori. Positive Predictive Value = true positive / all tested positive = 90/95= 95% 95% of the patients who tested positive for the disease have H. pylori.

Clinical precisionNegative Predictive Value: measures the proportion of patients test negative for the disease who do not have the disease.Negative Predictive Value = true negative / all tested negatives = 236/280= 84% 84% of the patients tested negative for the disease who did not have H. pylori.

Technical PrecisionSpecificity: Remember SpPinWhen a test has a high Specificity, a Positive test rules IN the disorder.Sensitivity: Remember SnNoutWhen a test has a high Sensitivity, a Negative result rules OUT the disorder.

Can I apply this valid, important diagnostic test to a specific patient?

Are the results of this diagnostic study applicable to my patient? Is the diagnostic test available, affordable, accurate, and precise in our setting?Yes

Are the results of this diagnostic study applicable to my patient? Can we generate a clinically sensible estimate of our patients pre-test probability?From personal experience, prevalence statistics, practice databases, or primary studies.YesAre the study patients similar to our own?YesIs it unlikely that the disease possibilities have changed since this evidence was gathered?Yes

Test-Treatment ThresholdsAB.10.20.30.40.50.60.70.80.900Do not testDo not treatTest, and treaton the basis ofthe test resultDo not testGet on withtreatmentPrevalence (pre-test probability) of target disorder

Clinical ProbabilityPre-Test ProbabilityClinical features of presentation including characteristics of patient, history, and exam.Test (can include distinct features of presentation in history or examination).Knowing likelihood ratio allows you to calculate post-test probability

Likelihood ratio Likelihood Ratio+ = sens/(1-spec) = 67/(100-98) = 67/2 = 33.5Likelihood Ratio- = (1-sens)/spec = (100-67)/98 = 33/98 = 0.34Prevalence = (a+c)/(a+b+c+d) = 134/375 = 35.7%Study pre-test odds = prevalence/(1-prevalence) = 35.7/64.3 = 0.56Post-test odds = pre-test odds x likelihood ratio = 0.56 x 33.5 = 18.76Post-test probability = post-test odds/(post-test odds +1) = 18.76/19.76 = 0.95

Are the results of this diagnostic study applicable to my patient? Will the resulting post-test probabilities affect our management and help our patient?Could it move us across a test-treatment threshold?Yes, from pre-test probability of 35.7% to post-test probability of 94.9%

Are the results of this diagnostic study applicable to my patient? Will the resulting post-test probabilities affect our management and help our patient? (cont.)Would our patient be a willing partner in carrying it out?Probably yesWould the consequences of the test help our patient reach his or her goals in all this?Yes, reassurance when negative, labeling and possibly generating awful diagnostic and prognostic news if positive, leading to further diagnostic tests and treatments, etc.

How can I apply the results to patient care? The in-office or "near patient" test had excellent specificity (98%) but a sensitivity of only 67%, which means that one third of patients infected with H. pylori and a proportionate number of those with peptic ulcer would be missed. The authors conclude that tests with such poor sensitivity should not be used for the test-and-treat strategy (remember SpPin and SnNout!).

How can I apply the results to patient care? An alternate approach exists, however. Given its high specificity, the in-office test could be used to rapidly and reliably diagnose two thirds of infected patients; the more sensitive laboratory ELISA could be reserved for those with negative results. However, the cost-effectiveness of this strategy would be highly dependent on the relative costs of the tests (the in-office test would have to be much less expensive than the ELISA) and on the prevalence of H. pylori in the population (the fewer people infected, the larger the number who would need a second test).

How can I apply the results to patient care? All of these factors should be considered before an in-office test is used for the test-and-treat strategy.

Questions?

diagnosis of helicobacter pylori infection.ppt

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