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Introduction to Evidence Based Medicine

Introduction to Evidence Based Medicine. Bradford S. Pontz, M.D. Assistant Professor of Medicine Georgetown University Medical Center. Patient W.R. 55 year old healthy male presents with 3 days right-sided back pain, 2 days rash Exam shows vesicular lesions

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Introduction to Evidence Based Medicine

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  1. Introduction to Evidence Based Medicine Bradford S. Pontz, M.D. Assistant Professor of Medicine Georgetown University Medical Center

  2. Patient W.R. • 55 year old healthy male presents with 3 days right-sided back pain, 2 days rash • Exam shows vesicular lesions • Patient says his friend was given a medication to treat the shingles and prednisone and wants to know if this will make his experience less miserable

  3. Overview and Objectives • 1. Definition • 2. How to ask Clinical Questions you can answer • 3. Searching for the Best Evidence • 4. Critically Appraising the Evidence • 5. Applying Evidence

  4. Definition • Evidence-based medicine is the conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients

  5. Definition • Making the best decision requires sound judgment based on the following: • clinical expertise • knowledge of patient values and preferences • evidence from the literature

  6. How to Ask Clinical Questions you can Answer - Four Elements of well-built clinical questions • 1. Patient or problem - ask “how would I describe a group of patients similar to mine?” compromise between precision and brevity • 2. Intervention - ask “what main intervention am I considering?” usually a treatment or exposure

  7. Four Elements of well-built clinical questions • 3. Comparison intervention - usually vs. placebo or vs. established therapy • 4. Outcome - what could this treatment hope to accomplish or what could result from this exposure?

  8. Search StrategiesAND/OR • AND ing searches for intersection of searches (studies that contain all search words) • OR ing searches for union of searches (simply adds them, but studies duplicated will appear only once in result of your OR)

  9. Search Strategies - Medline • Enter topic as subject heading (check box labelled “Map Term to Subject Heading”) • Enter any limits (years, language, etc.) • “Explode” topic if necessary • Next enter topic as index text word (do not check box) • Next enter topic as “topic.af” (all fields) • Then, combine these three searches using OR (creates union of first three searches)

  10. Search Strategies - Medline • Do this for as many aspects (in terms of search words) as seems necessary to limit search • Finally AND the results of these searches for individual topics

  11. Search Strategies - Medline • Methodologic filter is a way to further refine your search • Searches for all studies that involve the parameter you want (often a large number) • In Medline, search for your parameter with a suffix

  12. Search Strategies - Medline • Randomized controlled trial.pt (publication type) • Random.tw (text word) • Drug Therapy.sh (subject heading)

  13. Critically Appraising the Evidence • Hierarchy of Types of Studies • A few general terms • Assessing validity and importance of three main types of studies • diagnosis • prognosis • treatment

  14. Hierarchy of Types of Studies(in decreasing order of preference) • 1.Systematic reviews and meta-analyses • 2.Randomized, controlled clinical trials with definitive, significant results • 3.Randomized, controlled clinical trials with less definitive results (a point estimate suggesting a clinically significant effect, but with confidence intervals that suggest the possibility of more equivocal results)

  15. Hierarchy of Types of Studies(in decreasing order of preference) • 4. Cohort studies • 5. Case-Control studies • 6. Case reports

  16. General Terms • Hypothesis: “Purpose is to examine Treatment X in lowering blood pressure compared to standard Treatment Y” • Null hypothesis is no difference

  17. General Terms • P value is a level of probability, deemed as statistically significant, chosen as grounds for rejecting the null hypothesis. • Traditionally p<.05 = less than 5% probability that difference between treatments is due to chance or unknown reason rather than true difference in treatments

  18. General Terms • Validity - external validity is the degree to which the results of a study hold true in other settings and/or apply to populations beyond those included in a study, such as your own patients. Consider possible differences: • gender - compliance • stage or severity of disease

  19. Primary End Points • Most directly and clearly portray the actual condition of interest. Should state how possibility of end point will be assessed: • cancer prevalence by biopsy • coronary artery disease by angiogram • peptic ulcer evaluated by endoscopy

  20. Is this evidence about a diagnostic test valid? • Was there an independent, blind comparison with a reference “gold” standard of diagnosis? • Was it evaluated in an appropriate spectrum of patients?

  21. Is this evidence about prognosis valid? • Was a defined, representative sample of patients assembled at a common (usually early) point in the course of their disease? • Was follow-up sufficiently long and complete?

  22. If Follow-up not Optimal, do a “Worst-Case” Analysis • 100 patient enter, 4 die, 16 lost to follow-up • Death rate = 4/(100-16) = 4/84 = 4.8% • Survival rate = 100%-4.8% = 95.2% • Worst case - What if all 16 lost died? • Death rate=(4+16)/(84+16) = 20/100 = 20% • Survival rate = 100%-20% = 80%

  23. Is this evidence about treatment valid? • Was the assignment of patients randomized and double-blind? • Were the groups similar at the start? • Apart from the experimental intervention, were the groups treated equally? • Were all accounted for at the end of the trial and analyzed in the groups to which they were randomized? (“intention to treat”)

  24. No randomized trials found? • Refine your search • Consider whether a treatment effect is so large you can’t imagine it would be false(+) • Evidence from non-randomized trial showing that treatment is useless or harmful is somewhat acceptable. False (-) conclusions less likely than false (+)

  25. Is evidence from a systematic review valid? • Are the trials randomized? • Were the results consistent from study to study? • Does it include a methods section that describes: • search methods • methods for assessing individual study validity

  26. Critically Appraising the Evidence • Hierarchy of Types of Studies • A few general terms • Assessing validity and importance of three main types of studies • diagnosis • prognosis • treatment

  27. Is evidence about a diagnostic test important?

  28. Terms about diagnotic tests • Sensitivity = a/(a+c) = 731/809 = 90% • Specificity = d/(b+d) = 1500/1770 = 85% • LR+ = sens/(1-spec) = 90%/15% = 6 • LR- = (1-sens)/spec = 10%/85% = 0.12 • pos. pred. Value = a/(a+b) = 731/1001 = 73% • neg. pred. Value = d/(c+d) = 1500/1578 = 95%

  29. Terms about diagnostic testsLikelihood Ratios • Definition - probability of that test result in people with the disease divided by the probability of the result in people without the disease • Can be calculated for a range of values of test results rather than just pos. vs. neg. • Can be used with pre-test odds to calculate post-test odds

  30. Terms about diagnostic tests • Prevalence = (a+c)/(a+b+c+d) = 809/2579 = 32% • Pretest odds = prevalence/(1-prevalence) = 31%/69% = 0.45 • Post-test odds = pretest odds X LR • Post-test probability = post-test odds/(post-test odds + 1)

  31. Example - Ferritin for Diagnosis of iron deficiency anemia • Assume a pre-test odds of 1:1 (a 50-50 chance) • Suppose Ferritin = 60 • Post-test odds = 1X6 = 6 • Post-test probability = 6/(6+1) = 6/7 = 86%

  32. Is evidence about treatment important? • A statistically significant result (e.g. p<.05) may not be clinically significant. • May show that one treatment is better than another, but does not necessarily suggest the impact that treatment might have in your own clinical practice

  33. Bottom Line Clinical Effects • Relative Risk (RR) • Relative Risk Reduction (RRR) • Absolute Risk Reduction (ARR) • Number Needed to Treat (NNT) • Confidence Intervals

  34. Basic Statistics • CER = Control Event Rate = risk of outcome event of interest in the control group = A/(A+B) • EER = Experimental Event Rate = risk of outcome event rate in the experimental group = C/(C+D)

  35. Relative Risk • Aka Risk Ratio • is the ratio of risk of the outcome event in the experimental (intervention or treated group) to the risk in control group • RR = EER/CER = [C/(C+D)]/[A/(A+B)] • RR = [350/(350+947)]/[404/(404+921)] • RR = 0.865 or about 87%

  36. Relative Risk Reduction • Essentially the complement of RR • The percent reduction in the experimental group event rate compared with the control group event rate • RRR = [(CER-EER)/CER] X 100 OR • RRR = 1-RR • RRR = (1-0.865) = 13.5%

  37. Absolute Risk Reduction • Aka Risk Difference = difference in the event rate between a control group and an experimental group • ARR = CER-EER • ARR = A/(A+B) - C/(C+D) • ARR = 404/(404+921) - 350/(350+947) • ARR = 0.041 or 4.1%

  38. Number Needed to Treat • NNT = 1/ARR • NNT = 1/0.041 = 24 • NNT is particularly useful to clinicians who want to know whether the probable benefits of some treatments or intervention will be worthwhile in their patients

  39. Confidence Intervals • Basic research concept - experiment repeated will yield slightly different results each time • Approximation of the true effect is called the point estimate • CI = larger neighborhood in which true effect is likely to reside

  40. Confidence Intervals • Expressed with a given degree of expected certainty such as 95% • True result will lie outside the range only 5% of the time (2.5% of the time above and 2.5% of time below) • For example, an absolute risk reduction of 4.1% could have 95% CI of -1.0 to 9.2

  41. Hypothetical treatment study • Suppose experimental group is group 1. 15 of 125 patients have a given outcome. Proportion of outcome is p1=r1/n1=15/125=12%. • Suppose control group is group 2. 30 of 120 patients have a given outcome. Proportion of given outcome is p2=r2/n2=30/120=25%.

  42. Can you apply a diagnostic test? • Is it affordable, available, accurate and precise in your setting? • Can you estimate pretest probability? • Data from personal or practice experience • Data from the report itself - Speculation • Will the resulting post-test probabilities affect your management? Would you treat based on results? Would patient agree to?

  43. Applying Treatment Evidence • Can you apply evidence about prognosis to your patient? • Can you apply evidence about treatment? • Is your patient so different from those in trial that its results cannot be applied? Usually can extrapolate at least a direction of effect • How great a benefit might you expect from treatment?

  44. N of 1 Trial • Problems with classic trial of therapy • Many illnesses or lab abnormalities are self-limited • Placebo effect can lead to improvement in symptoms • Conclusions can be biased by our own expectations and those of the patient

  45. N of 1 Trial • Clinician and patient agree to trial • Patient undergoes pairs of treatment periods • Both patient and clinician are blinded • Treatment targets are monitored (symptom diary, etc.) • Pairs of treatment periods are replicated until both pt. and clinician are convinced that treatments are really different or not

  46. Patient W.R. • 1. Patient group/problem • 2. Intervention • 3. Comparison • 4. Outcome • Question: Does prednisone in addition to standard antiviral therapy, compared to standard antiviral therapy alone, improve pain in immunocompetent patients with acute herpes zoster?

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