Interpreting laboratory tests results

1. Interpretation of results Interpreting laboratory tests results May 2007

2. Learning objectives • At the end of the presentation, participants should • Be able to think critically for interpreting positive and negative test results • Interpret the laboratory result in the context of the situation

3. Objectives of the lecture • Understand what can be said about the results of laboratory tests • Interpretation • Understand what can not be said about the results of laboratory tests • Limitations

4. A real life clinical example:High amylases in a febrile patient • Traveller returning from South Asia develops • Headaches • Progressive fever, 40oC after a week • Diarrhea • Examination indicates dissociated pulse, splenomegaly • Laboratory investigation show: • Leuco-neutropenia • High level of amylases • What do you make of the high level of amylases?

5. A real life clinical example:Can you interpret the amylases? • Why was a test requested for amylases? • Nothing would have led the clinician to suspect pancreatitis • Subsequent isolation of Salmonella Typhi in the stools • Working diagnosis: Typhoid • Concerns over the amylases prompts more investigations: • No abnormality of the pancreas in the ultrasound • You cannot interpret a test (i.e. the amylases) that was not requested according to a properly framed hypothesis • The same applies to laboratory-epidemiology investigations

6. Planning a collaborative epidemiology-laboratory investigation Formulating the objectives Drawing conclusions Planning Analysing Data analysis Lab analysis Data Specimens Preparing Collecting Instruments Sampling strategy Data Specimens When faced with the need to interpret laboratory results, bear in mind why they were done

7. Use of sensitive tests • A sensitive test is able to pick up affected persons • Used to rule out diagnoses • Used when there is a penalty in missing a case • Diagnosis a dangerous but treatable condition(e.g., Tuberculosis) • Blood screening for HIV • Used at an early stage of a diagnosis work-up • A sensitive test is most useful when negative • Interpretation according to the prevalence • Use of the predictive value negative

8. Use of specific tests • A sensitive test is able to pick up non-affected persons • Used to rule in diagnoses • Used when a false positive can harm a patient • HIV test for individual counselling • Cancer diagnosis before chemotherapy • Used to confirm a diagnosis suspected because of other data • A sensitive test is most useful when positive • Interpretation according to the prevalence • Use of the predictive value positive

9. Possible objectives of joint laboratory epidemiology investigations • Test a hypothesis (Qualitative outcome) • Test a hypothesis • About an etiologic agent(e.g., Is West Nile virus the cause of the outbreak?) • About the relatedness of isolates(e.g., Are the cases caused by an identical pathogen?) • Measure a quantity (Quantitative outcome) • Estimate a quantity • Prevalence • Incidence

10. Using laboratory evidence to confirm a diagnosis during an outbreak • Short list potential etiologic agents (hypothesis generating) according to: • Epidemiological characteristics • Clinical characteristics • Setting • Test for agents short listed (hypothesis testing) • Positive test • Negative test • Use predictive values positive and negatives

11. Interpreting positive tests results during an outbreak • Use the predictive value positive that depends upon: • The frequency of the disease • The specificity of the test +++ • Elements that will support the hypothesis of a true positive • The disease is frequent • The test is specific • Elements that will support the hypothesis of a false positive • The disease is rare • The test is not sufficiently specific

12. If you are trying to rule in a diagnosis • Short list possible agents well • Increases the probability that you are dealing with the agent • Increases the predictive value of a positive test • Use a specific test • Be careful before concluding when: • The disease is unlikely • The test is not specific

13. Interpreting negative tests results during an outbreak • Use the predictive value negative that depends upon: • The frequency of the disease • The sensitivity of the test +++ • Elements that will support the hypothesis of a true negative • The disease is rare • The test is sensitive • Elements that will support the hypothesis of a false negative • The disease is common • The test is not sufficiently sensitive

14. A test was negative only for the pathogens that were looked for • If the culture on a specific medium was not done, the test cannot be interpreted as negative for the specific pathogen • If you did not ask for Campylobacter culture, the “negative” stool culture is not really “negative” for Campylobacter

15. If you are trying to rule out a diagnosis • Use a sensitive test • Be careful before concluding when: • The disease is common • The test is not sensitive

16. The specific case of emergent pathogens • Epidemiological and clinical evidence are of limited usefulness to generate hypotheses regarding the agent • A progressive inductive process from the laboratory generate hypotheses about potential pathogens involved • Additional investigations, including epidemiological investigations, will test the hypothesis that the candidate agent isolated in the laboratory causes the disease • Usefulness of Koch criteria

17. Koch criteria modified by River for viral diseases • Isolation of the pathogen from the diseased host • Cultivation in host cells • Proof of filterability • Production of comparable disease in the original host species or a related one • Re-isolation of the virus • Detection of a specific immune response to the virus

18. In some cases, the agent isolated in the laboratory is not the cause of the disease • “Hepatitis G” virus identified in various patients • Epidemiological studies did not confirm the hypothesis that the agent is associated with chronic viral hepatitis

19. Host-pathogen relationship • Presence of an organism may have different interpretation according to the context • Immune system • Immunocompetent patient • Opportunistic pathogens may be innocent by-standers • Immunocompromised patient • Opportunistic pathogens may be the cause of the infection • Age • Physiological status (e.g. urinary infection in pregnancy)

20. Using laboratory evidence to confirm the relatedness of isolates • Generate hypotheses using epidemiological evidence • Studies allowing the use of statistical tests • Studies not allowing the use of statistical tests • Test hypotheses using laboratory evidence • Use typing technique adapted to: • Hypothesis • Pathogen

21. Generating hypotheses in an investigation not allowing the use of statistics (1) • Investigation of a case of HCV seroconversion in a child with clotting factor disorders in New Jersey, USA, 1996 • The child only received recombinant clotting factors • Two other household members had HCV infection • The older brother (Clotting factor disorder) • The mother (Former injection drug use) • In-depth interview gathered that: • There was no exposure to the blood of the older brother • The mother pricked herself with a needle before injecting him with factors

22. Testing hypotheses of relatedness using laboratory evidence (2) • HCV sequencing indicates that: • The sequence of the virus of the child is different from the virus of the older brother • The sequence of the virus is close from the virus of the mother • Sequencing data supports the epidemiological hypothesis that the child acquired HCV from his mother through a percutaneous exposure

23. Generating hypotheses in an investigation allowing the use of statistics (1) • A multi-state outbreak of hepatitis A among school children, USA, 1997 • Cases in three states • Michigan (more than 200) • Maine (few dozens) • Arizona (handful)

24. Epidemiological and laboratory results (2)

25. Interpretation (3) • The multi-state outbreak was caused by the consumption of the same frozen strawberries among school children • In Michigan, the epidemiological information is sufficient to conclude • In Maine, the laboratory evidence supports the unclear epidemiological evidence • In Arizona where cases are to few, only the laboratory evidence allows to conclude • The smaller number of cases in Maine and Arizona may reflect a lower level of contamination of the product distributed in these two states

26. Interpreting prevalence and incidence • A quantitative epidemiological study estimating the frequency of a disease on the basis of a laboratory test (e.g., serological survey) must be interpreted according to: • Predictive value positive • Predictive value negative • These will depend upon: • The test used (sensitivity and specificity) • The frequency of the disease

27. Be careful about what the manufacturer may say about the predictive values • The manufacturer may report values of • Sensitivity • Specificity • These probably come from panel testing • Be careful with values of predictive values positive and negative reported by manufacturers • These values depends upon specific prevalence settings • They may come from a combination of a positive and negative panels that generate an artificial prevalence of 50%

28. Take home message: Interpret epidemiological and laboratory evidence as a team • Positive tests are likely to rule in the diagnosis if the test is specific and the disease common • Negative tests are likely to rule out the diagnosis if the test is sensitive and the disease uncommon • Emergent pathogens are discovered in the laboratory and assessed according to additional studies • Laboratory investigations of relatedness must be based on hypotheses developed on the basis of the epidemiology • Interpret incidence and prevalence indicators according to predictive values positive and negative

29. Interpretation of results Developed by:The Department of Epidemic and Pandemic Alert and Response of the World Health Organization with the assistance of:European Program for Field Epidemiology Training Canadian Field Epidemiology Programme Thailand Ministry of Health Institut Pasteur