Evidence Based Diagnosis

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  • Convert clinical information needs into answerable questions. Track down the best evidence to answer question. Critically appraise evidence for its validity and usefulness. Apply results to your clinical practice. Evaluate our performance.

Transcript

  • 1. Evidence Based Diagnosis Hesham Al-Inany, MD Cairo University
  • 2. When a Patient Has a Problem
    • The doctor reaches a diagnosis by:
            • Clinical data
            • Diagnostic tools
  • 3. Increasing use of investigations: - Availability. - The urge to make use of new technology.
  • 4. The evaluation of diagnostic techniques is less advanced than that of treatments. Unlike with drugs, there are generally no formal requirements for adoption of diagnostic tests in routine care. Diagnostic tests
  • 5. The evaluation of diagnostic techniques is less advanced than that of treatments ( NO phase I, II, III, IV). New Diagnostic tests
  • 6. This is not the only problem
    • Patient oriented !!!!!!!
    • Your 45 year old patient has a mammogram. The study is interpreted as "suspicious for malignancy" by your radiologist.
    • Your patient asks you: "Does this mean I have cancer?", and you (correctly) answer "No, we have to do further testing."  
  • 7.
    • Your patient then asks, "OK, I understand that the mammogram isn't the final answer, but given what we know now, what are the chances that I have breast cancer?".
  • 8. Is it Easy!!!
    • Assume that the overall risk of breast cancer in any 45 year old woman, regardless of mammogram result, is 1%. Assume also that mammography is 90% sensitive and 95% specific. Then, select your answer below: 1%    15%      60%      85%    95%
  • 9. Apply results to your patients Understand evidence Find the best evidence to answer questions clinical information needs Answerable questions EB Diagnosis
  • 10. Articles about diagnosis
    • Relevance
    • Validity
  • 11. Relevance
    • First, the test should be one that is feasible for you in your community
    • Example: brain biopsy is an accurate test for diagnosing dementia, it’s not practical for my (living) patients!
  • 12. Ask yourself
    • Did the patient sample include an appropriate spectrum of patients to whom the diagnostic test will be applied in clinical practice?
  • 13. Rule of Thumb
    • at least 100 participants to ensure an appropriate "spectrum" of disease
  • 14. Validity
    • Did the authors use a gold reference standard?
  • 15.
    • A gold standard is needed. This may be a diagnostic test,
    • however if diagnosis is to lead to treatment, a treatment outcome may be the best gold standard.
  • 16. Sometimes
    • In some cases we are limited by ethical considerations. For example, doing an invasive test such as a biopsy in a patient with a negative test result, for the sole purposes of doing a study, might be considered unethical.
  • 17. So
    • Is reference standard used acceptable?
    • Were both reference standard and test applied to all patients?
  • 18. Blinding
    • Was there a blind comparison with the reference standard ?
    • Frequently very difficult to achieve
  • 19. Independent
    • the decision to perform the reference standard should ideally be independent of the results of the test being studied.
  • 20. Ask Yourself
    • Did the results of the test being evaluated influence the decision to perform the reference standard?
  • 21. Methodology
    • Were the methods for performing the test described in sufficient detail to permit replication?
  • 22. Statistics needed to know
    • prevalence = probability of disease in the entire population at any point in time (i.e. 8% the Egyptian population has diabetes mellitus)
    • incidence = probability that a patient without disease develops the disease during an interval (the incidence of diabetes mellitus is 0.6% per year, referring only to new cases )
  • 23. sensitivity
    • probability of a positive test among patients with disease
    • i.e Ability to diagnose
  • 24.  
  • 25. specificity
    • probability of a negative test among patients without disease
    • i.e Ability to exclude
  • 26. 2 X 2 Table d (true negative) c (false negative) b (false positive) a (true positive)
  • 27.  
  • 28.
    • Positive predictive value = probability of disease among patients with a positive test
    • Negative predictive value = probability of no disease among patients with a negative test
  • 29. d c+d True negatives All screened negative Negative Predictive value a a+b True positives All screened positive Positive Predictive value d b+d True negatives All nondiseased Specificity a a+c True positives All diseased Sensitivity Formula Definition Measure
  • 30. Keep in Mind
    • sensitivity and specificity by themselves are only useful when either is very high (over typically, 95% or higher).
  • 31. Likelihood Ratios
    • LR means
    •   probability of an individual with the condition having the test result       probability of an individual without the condition having the test result
    • LR+ =
    • probability of an individual with the condition having a positive test probability of an individual without the condition having a positive test
  • 32.
    •      LR- =
    • probability of an individual with the condition having a negative test probability of an individual without the condition having a negative test
    • LR+ = sensitivity / (1-specificity)
    •  
    • LR- = (1-sensitivity) / specificity
  • 33. Why LR
    • The LR+ corresponds to the clinical concept of "ruling-in disease"
    • The LR- corresponds to the clinical concept of "ruling-out disease“
  • 34. Interpreting likelihood ratios Large and often conclusive decrease in the likelihood of disease < 0.1 Moderate decrease in the likelihood of disease 0.1 - 0.2 Small decrease in the likelihood of disease 0.2 - 0.5 Minimal decrease in the likelihood of disease 0.5 - 1.0 No change in the likelihood of disease 1 Minimal increase in the likelihood of disease 1 - 2 Small increase in the likelihood of disease 2 - 5 Moderate increase in the likelihood of disease 5 - 10 Large and often conclusive increase in the likelihood of disease > 10 Interpretation LR
  • 35.  
  • 36. ROC curve is simply a graph of sensitivity vs (1-specificity)
  • 37. Finally
    • Will the results Help Me in Caring for My Patients?
  • 38.
    • THANK YOU