1. AASLD Practice Guidelines Committee Meeting, Chicago 1 May 2009 Yngve Falck-Ytter, M.D. Case Western Reserve University

2. Disclosure In the past 5 years, Dr. Falck-Ytter received no personal payments for services from industry. His research group received research grants from Three Rivers, Valeant and Roche that were deposited into non-profit research accounts. He is a member of the GRADE working group which has received funding from various governmental entities in the US and Europe. Some of the GRADE work he has done is supported in part by grant # 1 R13 HS016880-01 from the Agency for Healthcare Research and Quality (AHRQ).

3. Content Part 1  Background and rationale for revisiting guideline methodology  GRADE approach  Quality of evidence  Strength of recommendations

4. Content (continued) Part 2 – practical consideration  Ideal vs. practical ad hoc approaches  Funding guideline work  Creating GRADE evidence profiles with GRADEpro  GRADE and diagnostic tests

5. Reassessment of clinical practice guidelines  Editorial by Shaneyfelt and Centor (JAMA 2009)  “Too many current guidelines have become marketing and opinion-based pieces…”  “AHA CPG: 48% of recommendations are based on level C = expert opinion…”  “…clinicians do not use CPG […] greater concern […] some CPG are turned into performance measures…”  “Time has come for CPG development to again be centralized, e.g., AHQR…”

6. Evidence based clinical decisions Research evidence Patient values and preferences Clinical state and circumstances Expertise Equal for all Haynes et al. 2002

7. Confidence in evidence  There always is evidence  “When there is a question there is evidence”  Evidence alone is never sufficient to make a clinical decision  Better research  greater confidence in the evidence and decisions

8. Hierarchy of evidence STUDY DESIGN Randomized Controlled Trials Cohort Studies and Case Control Studies Case Reports and Case Series, Non-systematic observations BIAS Expert Opinion

9. Reasons for grading evidence?  People draw conclusions about the  quality of evidence and strength of recommendations  Systematic and explicit approaches can help to  protect against errors, resolve disagreements  communicate information and fulfill needs  be transparent about the process  Change practitioner behavior  However, wide variation in approaches GRADE working group. BMJ. 2004 & 2008

10. 10

11. Which grading system? P: In patients with acute hepatitis C … I : Should anti-viral treatment be used … C: Compared to no treatment … O: To achieve viral clearance? EvidenceRecommendationOrganization BClass IAASLD (2009) VA (2006)II-1-/-SIGN (2006)1+AAGA (2006)-/-“Most authorities…”

12. Scenario (2) Should patients with risk factors for viral hepatitis be screened with a hepatitis C antibody (ELISA) test to identify patients with past hepatitis C exposure?

13. 13 Level of evidence in GI CPGs AASLD AGA ACGASGE AMultiple RCTs or meta-analysis Good Consistent, well-designed, well conducted studies […] 1. Multiple published, well-controlled (?) randomized trials or a well designed systemic (?) meta- analysis A. RCTs BSingle randomized trial, or non- randomized studies C Only consensus opinion of experts, case studies, or standard-of-care FairLimited by the number, quality or consistency of individual studies […] Poor… important flaws, gaps in chain of evidence… 2. One quality- published (?) RCT, published well- designed cohort/ case-control studies 3. Consensus of authoritative (?) expert opinions based on clinical evidence or from well designed, but uncontrolled or non-rand. clin. trials B. RCT with important limitations C. Obser- vational studies D. Expert opinion

14. What to do? 14

15. Limitations of existing systems  Confuse quality of evidence with strength of recommendations  Lack well-articulated conceptual framework  Criteria not comprehensive or transparent  GRADE unique  breadth, intensity of development process  wide endorsement and use  conceptual framework  comprehensive, transparent criteria  Focus on all important outcomes related to a specific question and overall quality

16.  G rades of R ecommendation A ssessment, D evelopment and E valuation

17. GRADE Working Group  David Atkins, chief medical officer a  Dana Best, assistant professor b  Martin Eccles, professor d  Francoise Cluzeau, lecturer x  Yngve Falck-Ytter, associate director e  Signe Flottorp, researcher f  Gordon H Guyatt, professor g  Robin T Harbour, quality and information director h  Margaret C Haugh, methodologist i  David Henry, professor j  Suzanne Hill, senior lecturer j  Roman Jaeschke, clinical professor k  Regina Kunx, Associate Professor  Gillian Leng, guidelines programme director l  Alessandro Liberati, professor m  Nicola Magrini, director n  James Mason, professor d  Philippa Middleton, honorary research fellow o  Jacek Mrukowicz, executive director p  Dianne O ’ Connell, senior epidemiologist q  Andrew D Oxman, director f  Bob Phillips, associate fellow r  Holger J Sch ü nemann, professor g,s  Tessa Tan-Torres Edejer, medical officer t  David Tovey, Editor y  Jane Thomas, Lecturer, UK  Helena Varonen, associate editor u  Gunn E Vist, researcher f  John W Williams Jr, professor v  Stephanie Zaza, project director w  a) Agency for Healthcare Research and Quality, USA  b) Children's National Medical Center, USA  c) Centers for Disease Control and Prevention, USA  d) University of Newcastle upon Tyne, UK  e) German Cochrane Centre, Germany  f) Norwegian Centre for Health Services, Norway  g) McMaster University, Canada  h) Scottish Intercollegiate Guidelines Network, UK  i) F é d é ration Nationale des Centres de Lutte Contre le Cancer, France  j) University of Newcastle, Australia  k) McMaster University, Canada  l) National Institute for Clinical Excellence, UK  m) Universit à di Modena e Reggio Emilia, Italy  n) Centro per la Valutazione della Efficacia della Assistenza Sanitaria, Italy  o) Australasian Cochrane Centre, Australia  p) Polish Institute for Evidence Based Medicine, Poland  q) The Cancer Council, Australia  r) Centre for Evidence-based Medicine, UK  s) National Cancer Institute, Italy  t) World Health Organisation, Switzerland  u) Finnish Medical Society Duodecim, Finland  v) Duke University Medical Center, USA  w) Centers for Disease Control and Prevention, USA  x) University of London, UK  Y) BMJ Clinical Evidence, UK

18. GRADE uptake

19. Where GRADE fits in Prioritize problems, establish panel Systematic review Searches, selection of studies, data collection and analysis Assess the relative importance of outcomes Prepare evidence profile: Quality of evidence for each outcome and summary of findings Assess overall quality of evidence Decide direction and strength of recommendation Draft guideline Consult with stakeholders and / or external peer reviewer Disseminate guideline Implement the guideline and evaluate GRADE

20. 20 GRADE: Quality of evidence The extent to which our confidence in an estimate of the treatment effect is adequate to support particular recommendation. Although the degree of confidence is a continuum, we suggest using four categories:  High  Moderate  Low  Very low

21. I B IIVIII Quality of evidence across studies Outcome #1 Outcome #2 Outcome #3 Quality: High Quality: Moderate Quality: Low

22. Determinants of quality  RCTs start high  Observational studies start low  What lowers quality of evidence? 5 factors:  Detailed design and execution  Inconsistency of results  Indirectness of evidence  Imprecision  Publication bias

23. 23 What is the study design?

24. 24 Types of studies Did investigator assign exposure? Experimental study Yes Observational study No Random allocation?Comparison group? RCT Yes CCT No Analytical study Yes Case-series No Direction? Cohort study Exposure  Outcome Case-control study Exposure  Outcome Cross-sectional study Exposure and outcome at the same time Before and after study Variations: cBAS ITS E  O

25. 1. Design and execution  Study limitations (risk of bias) For RCTs:  Lack of allocation concealment  No true intention to treat principle  Inadequate blinding  Loss to follow-up  Early stopping for benefit For observational studies:  Selection  Comparability  Exposure/outcome Avoid critical appraisal scoring tools!

26. Jadad AR et al. Control Clin Trials 1996 26 Tools: scales and checklists Example: Jadad score Was the study described as randomized?1 Adequate description of randomization?1 Double blind?1 Method of double blinding described?1 Description of withdrawals and dropouts?1 Max 5 points for quality

27. Schulz KF et al. JAMA 1995 27 Allocation concealment 250 RCTs out of 33 meta-analyses Allocation concealment:Effect (Ratio of OR) adequate1.00(Ref.) unclear0.67 [0.60 – 0.75] not adequate0.59 [0.48 – 0.73] * * significant

28. 5 vs 4 chemo-Rx cycles for AML

29. Studies stopped early because of benefit

30. Cochrane Risk of bias graph in RevMan 5 30

31. 2. Consistency of results  Look for explanation for inconsistency  patients, intervention, comparator, outcome, methods  Judgment  variation in size of effect  overlap in confidence intervals  statistical significance of heterogeneity I2I2

32. Pagliaro L et al. Ann Intern Med 1992;117:59-70 32 Heterogeneity

33. 3. Directness of Evidence  Indirect comparisons  Interested in head-to-head comparison  Drug A versus drug B  Tenofovir versus entecavir in hepatitis B treatment  Differences in  patients (early cirrhosis vs end-stage cirrhosis)  interventions (CRC screening: flex. sig. vs colonoscopy)  comparator (e.g., differences in dose)  outcomes (non-steroidal safety: ulcer on endoscopy vs symptomatic ulcer complications)

34. 4. Imprecision Small sample size  small number of events  wide confidence intervals  uncertainty about magnitude of effect

35. Imprecision 0.751.001.25 RR appreciable benefit appreciable harm impreciseprecise

36. 36

37. 5. Reporting Bias (Publication Bias)  Reporting of studies  publication bias  number of small studies  Reporting of outcomes

38. Egger M, Smith DS. BMJ 1995;310:752-54 38 I.V. Mg in acute myocardial infarction Publication bias Meta-analysis Yusuf S.Circulation 1993 ISIS-4 Lancet 1995

39. Egger M, Cochrane Colloquium Lyon 2001 39 Funnel plot Standard Error Odds ratio 0.10.313 3 2 1 0 100.6 Symmetrical: No reporting bias

40. Egger M, Cochrane Colloquium Lyon 2001 40 Funnel plot Standard Error Odds ratio 0.10.313 3 2 1 0 100.6 Asymmetrical: Reporting bias?

41. Egger M, Smith DS. BMJ 1995;310:752-54 41 I.V. Mg in acute myocardial infarction Reporting bias Meta-analysis Yusuf S.Circulation 1993 ISIS-4 Lancet 1995

42. 42 Quality assessment criteria Lower if… Quality of evidence High (4) Moderate (3) Low (2) Very low (1) Study limitations (design and execution) Inconsistency Indirectness Imprecision Publication bias Observational study Study design Randomized trial Higher if… What can raise the quality of evidence?

43. BMJ 2003;327:1459–61 43

44. 44 Quality assessment criteria Lower if…Higher if… Quality of evidence High (4) Moderate (3) Low (2) Very low (1) Study design Randomized trial Observational study Study limitations Inconsistency Indirectness Imprecision Publication bias Large effect (e.g., RR 0.5) Very large effect (e.g., RR 0.2) Evidence of dose-response gradient All plausible confounding would reduce a demonstrated effect

45. 45 Categories of quality Further research is very unlikely to change our confidence in the estimate of effect High Low Further research is very likely to have an important impact on our confidence in the estimate of effect and is likely to change the estimate Moderate Further research is likely to have an important impact on our confidence in the estimate of effect and may change the estimate Very lowAny estimate of effect is very uncertain

46. 46 Judgments about the overall quality of evidence  Most systems not explicit  Options:  Benefits  Primary outcome  Highest  Lowest  Beyond the scope of a systematic review  GRADE: Based on lowest of all the critical outcomes

47. GRADE evidence profile

48. Going from evidence to recommendations  Deliberate separation of quality of evidence from strength of recommendation  No automatic one-to-one connection as in other grading systems  Example: What if there is high quality evidence, but the balance between benefit and risks are finely balanced? 48

49. Strength of recommendation “The strength of a recommendation reflects the extent to which we can, across the range of patients for whom the recommendations are intended, be confident that desirable effects of a management strategy outweigh undesirable effects.” Although the strength of recommendation is a continuum, we suggest using two categories : “Strong” and “Weak”

50. Desirable and undesirable effects  Desirable effects  Mortality reduction  Improvement in quality of life, fewer hospitalizations/infections  Reduction in the burden of treatment  Reduced resource expenditure  Undesirable effects  Deleterious impact on morbidity, mortality or quality of life, increased resource expenditure

51. 4 determinants of the strength of recommendation Factors that can weaken the strength of a recommendation Explanation  Lower quality evidenceThe higher the quality of evidence, the more likely is a strong recommendation.  Uncertainty about the balance of benefits versus harms and burdens The larger the difference between the desirable and undesirable consequences, the more likely a strong recommendation warranted. The smaller the net benefit and the lower certainty for that benefit, the more likely is a weak recommendation warranted.  Uncertainty or differences in values The greater the variability in values and preferences, or uncertainty in values and preferences, the more likely weak recommendation warranted.  Uncertainty about whether the net benefits are worth the costs The higher the costs of an intervention – that is, the more resources consumed – the less likely is a strong recommendation warranted.

52. Developing recommendations

53. Implications of a strong recommendation  Patients: Most people in this situation would want the recommended course of action and only a small proportion would not  Clinicians: Most patients should receive the recommended course of action  Policy makers: The recommendation can be adapted as a policy in most situations

54. Implications of a weak recommendation  Patients: The majority of people in this situation would want the recommended course of action, but many would not  Clinicians: Be prepared to help patients to make a decision that is consistent with their own values/decision aids and shared decision making  Policy makers: There is a need for substantial debate and involvement of stakeholders

55. 6 main misconceptions 1. Isn’t GRADE expensive to realize? 2. Isn’t GRADE more complicated, takes longer and requires more resources? 3. Isn’t GRADE eliminating the expert? 4. But what about prevalence/burden of disease, diagnosis, cost? 5. But GRADE does not have an “insufficient evidence to make recommendation” category! (or: the “optional” category), no? 6. But we only “recommend” – we can’t possibly give weak recommendations!

56. Systematic review Guideline development PICOPICO Outcome Formulate question Rate importance Critical Important Critical Not important Create evidence profile with GRADEpro Summary of findings & estimate of effect for each outcome Rate overall quality of evidence across outcomes based on lowest quality of critical outcomes RCT start high, obs. data start low 1.Risk of bias 2.Inconsistency 3.Indirectness 4.Imprecision 5.Publication bias Grade down Grade up 1.Large effect 2.Dose response 3.Confounders Rate quality of evidence for each outcome Select outcomes Very low Low Moderate High Formulate recommendations: For or against (direction) Strong or weak (strength) By considering:  Quality of evidence  Balance benefits/harms  Values and preferences Revise if necessary by considering:  Resource use (cost) “We recommend using…” “We suggest using…” “We recommend against using…” “We suggest against using…” Outcomes across studies

57. Conclusions 1. GRADE is gaining acceptance as international standard 2. GRADE has criteria for evidence assessment across questions (e.g., public health interventions) and outcomes 3. Criteria for moving from evidence to recommendations 4. Simple, transparent, systematic 5. Balance between simplicity and methodological rigor