The Role of Risk Stratification and Biomarkers in Prevention of CVD

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Presented by Mark Pletcher, MD, MPH, at UCSF's symposium "The Role of Risk Stratification and Biomarkers in Prevention of Cardiovascular Disease" in Jan 2012.

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  • OK, let’s get started. We’re going to spend the morning talking about the role of risk stratification and biomarkers in prevention of CVD.
  • Nearly 50,000 articles in PubMed on “Cardiovascular Diseases” and “biomarker”, but not one post-Framingham biomarker has completed translation into routine clinical practice
  • The Role of Risk Stratification and Biomarkers in Prevention of CVD

    1. 1. The role of risk stratification and biomarkers in prevention of CVD CVD Risk Biomarker Symposium Mark J. Pletcher, MD, MPH 1/30/2012
    2. 2. Objective <ul><li>Objective : </li></ul><ul><ul><li>Complete translation of biomarkers into clinical practice when beneficial </li></ul></ul>
    3. 3. Objective <ul><li>Objective : </li></ul><ul><ul><li>Complete translation of biomarkers into clinical practice when beneficial </li></ul></ul><ul><li>Gaps : </li></ul><ul><ul><li>Need evidence that will convince guideline committees and clinicians to recommend and use the biomarker in clinical practice </li></ul></ul><ul><ul><li>Need tools to encourage implementation and a good dissemination strategy </li></ul></ul>
    4. 4. Outline <ul><li>Framework </li></ul><ul><ul><li>Phases of evaluation of a biomarkers </li></ul></ul><ul><ul><li>Measuring health impact </li></ul></ul><ul><ul><li>RCTs and decision analysis modeling </li></ul></ul>
    5. 5. Framework <ul><li>Objective : </li></ul><ul><ul><li>Complete translation of biomarkers into clinical practice when beneficial </li></ul></ul><ul><li>Gaps : </li></ul><ul><ul><li>Need evidence that will convince guideline committees and clinicians to recommend and use the biomarker in clinical practice </li></ul></ul><ul><ul><li>Need tools and strategy to implement and disseminate </li></ul></ul>What KIND of evidence?
    6. 6. Framework Circulation 2009;119: 2408-16.
    7. 7. Framework Circulation 2009;119: 2408-16. Association (Discrimination)
    8. 8. Framework Circulation 2009;119: 2408-16. Reclassification
    9. 9. Framework Circulation 2009;119: 2408-16. Impact
    10. 10. Framework <ul><li>My thesis: </li></ul><ul><ul><li>Credible evidence of positive net health impact is and should be absolutely required </li></ul></ul>Circulation 2011;123:1116-1124
    11. 11. Framework <ul><li>My thesis: </li></ul><ul><ul><li>Credible evidence of positive net health impact is and should be absolutely required </li></ul></ul><ul><ul><ul><li>Association, discrimination, reclassification measures are not enough </li></ul></ul></ul><ul><ul><ul><li>Decision analysis modeling and/or studies directly measuring health impact outcomes are required </li></ul></ul></ul>Circulation 2011;123:1116-1124
    12. 12. Framework <ul><li>My thesis: </li></ul><ul><ul><li>Credible evidence of positive net health impact is and should be absolutely required </li></ul></ul><ul><ul><ul><li>Association, discrimination, reclassification measures are not enough </li></ul></ul></ul><ul><ul><ul><li>Decision analysis modeling and/or studies directly measuring health impact outcomes are required </li></ul></ul></ul><ul><ul><li>Magnitude of impact and cost (i.e., cost-effectiveness) should also be considered </li></ul></ul>Circulation 2011;123:1116-1124
    13. 13. Framework <ul><li>2 high profile examples </li></ul>
    14. 14. Example 1: Multimarker panel <ul><li>10 biomarkers </li></ul><ul><ul><li>CRP, BNP, homocysteine, Ualb/cr ratio, etc </li></ul></ul><ul><li>“ Evaluate incremental usefulness” </li></ul>NEJM 2006;355:2631-9
    15. 15. Example 1: Multimarker panel <ul><li>Association </li></ul>NEJM 2006;355:2631-9
    16. 16. Example 1: Multimarker panel <ul><li>Association </li></ul>NEJM 2006;355:2631-9
    17. 17. Example 1: Multimarker panel <ul><li>Discrimination </li></ul>NEJM 2006;355:2631-9 C-statistic: 0.80  0.82
    18. 18. Example 1: Multimarker panel <ul><li>Conclusion </li></ul><ul><ul><li>“Adds only moderately to standard risk factors” </li></ul></ul><ul><li>Should we adopt? Abandon hope for CVD biomarkers? </li></ul>NEJM 2006;355:2631-9
    19. 19. Example 2: CRP <ul><li>C-reactive protein </li></ul><ul><li>“Compare prediction models” </li></ul>Annals 2006;145:21-29
    20. 20. Example 2: CRP <ul><li>Association </li></ul><ul><ul><li>RR-adj for ln(hsCRP) = 1.22, highly significant </li></ul></ul>Annals 2006;145:21-29
    21. 21. Example 2: CRP <ul><li>Association </li></ul><ul><ul><li>RR-adj for ln(hsCRP) = 1.22, highly significant </li></ul></ul><ul><li>Model “fits better” with CRP </li></ul><ul><ul><li>BIC improves (6969.60  6960.25) </li></ul></ul><ul><ul><li>Calibration poor without (p=.039), good with (p=.23) </li></ul></ul>Annals 2006;145:21-29
    22. 22. Example 2: CRP <ul><li>Association </li></ul><ul><ul><li>RR-adj for ln(hsCRP) = 1.22, highly significant </li></ul></ul><ul><li>Model “fits better” with CRP </li></ul><ul><ul><li>BIC improves (6969.60  6960.25) </li></ul></ul><ul><ul><li>Calibration poor without (p=.039), good with (p=.23) </li></ul></ul><ul><li>Discrimination improves a tiny bit </li></ul><ul><ul><li>C-index 0.813  0.815 </li></ul></ul><ul><ul><li>More improvement with CRP than with LDL </li></ul></ul>Annals 2006;145:21-29
    23. 23. Example 2: CRP <ul><li>Reclassification </li></ul>Annals 2006;145:21-29 Intermediate risk ppts reclassified: 20% (And reclassified persons’ expected risk closer to what was observed)
    24. 24. Example 2: CRP <ul><li>Reclassification </li></ul><ul><ul><li>Might make a different treatment decision for those 20% reclassified </li></ul></ul>Annals 2006;145:21-29
    25. 25. Example 2: CRP <ul><li>Conclusion </li></ul><ul><ul><li>CRP “improves cardiovascular risk classification” </li></ul></ul><ul><li>Should we adopt? Abandon hope for CRP? </li></ul>Annals 2006;145:21-29
    26. 26. The Common Flaw <ul><li>All presented measures: </li></ul><ul><ul><li>Ignore the clinical decisions they influence </li></ul></ul><ul><ul><li>Do not reflect the health consequences of making a good vs. bad decision </li></ul></ul><ul><ul><li>Do not account for the downsides of measuring the biomarker itself </li></ul></ul><ul><ul><ul><li>Harm, also cost </li></ul></ul></ul>Circulation 2011;123:1116-1124
    27. 27. The Common Flaw <ul><li>All presented measures: </li></ul><ul><ul><li>Ignore the clinical decisions they influence </li></ul></ul><ul><ul><li>Do not reflect the health consequences of making a good vs. bad decision </li></ul></ul><ul><ul><li>Do not account for the downsides of measuring the biomarker itself </li></ul></ul><ul><ul><ul><li>Harm, also cost </li></ul></ul></ul><ul><li>Cannot estimate net health impact! </li></ul>Circulation 2011;123:1116-1124
    28. 28. The Answer <ul><li>Measure health impact </li></ul>Circulation 2011;123:1116-1124
    29. 29. The Answer <ul><li>Measure health impact </li></ul><ul><li>Instead of: </li></ul><ul><ul><li>RR, C-statistic, Hosmer-Lemeshow, IDI, NRI </li></ul></ul><ul><li>Measure: </li></ul><ul><ul><li>Death, MI, fractures, disability scale, quality of life, life-years, QALY’s </li></ul></ul>Circulation 2011;123:1116-1124
    30. 30. Measuring Health Impact <ul><li>It’s hard to measure health impact </li></ul><ul><ul><li>Events you care about are rare </li></ul></ul><ul><ul><li>How to balance the harms against benefits? </li></ul></ul><ul><ul><li>Biomarker impact depends on specifics of the interventions, and on the algorithm for using the biomarker </li></ul></ul>Circulation 2011;123:1116-1124
    31. 31. Measuring Health Impact <ul><li>It’s hard to measure health impact </li></ul><ul><ul><li>Events you care about are rare </li></ul></ul><ul><ul><li>How to balance the harms against benefits? </li></ul></ul><ul><ul><li>Biomarker impact depends on specifics of the interventions, and on the algorithm for using the biomarker </li></ul></ul><ul><li>RCTs vs. Decision/cost-effectiveness modeling (vs. both) </li></ul>Circulation 2011;123:1116-1124
    32. 32. Measuring Health Impact <ul><li>RCTs and DCEA models </li></ul><ul><ul><li>Overview </li></ul></ul><ul><ul><li>Strengths and weaknesses </li></ul></ul>Circulation 2011;123:1116-1124
    33. 33. Measuring Health Impact <ul><li>Randomized controlled trials (RCTs) </li></ul>Study sample Randomize Do not measure biomarker Intervention tailored to biomarker level Standard intervention Measure health outcomes Measure health outcomes Measure biomarker Circulation 2011;123:1116-1124
    34. 34. Measuring Health Impact <ul><li>Randomized controlled trials (RCTs) </li></ul><ul><ul><li>Strengths: A “real-world”(?) unbiased estimate of effectiveness </li></ul></ul><ul><ul><ul><li>Imperfect test performance </li></ul></ul></ul><ul><ul><ul><li>Imperfect adherence to algorithm </li></ul></ul></ul><ul><ul><ul><li>Estimate portability of the intervention and effectiveness in a specific population </li></ul></ul></ul><ul><ul><ul><li>Can measure utilization, unexpected downsides </li></ul></ul></ul>Circulation 2011;123:1116-1124
    35. 35. Measuring Health Impact <ul><li>Randomized controlled trials (RCTs) </li></ul><ul><ul><li>Weakness #1: $$$$$ </li></ul></ul><ul><ul><ul><li>Big sample size, long follow-up </li></ul></ul></ul><ul><ul><ul><ul><li>Uncommon events like MI are the most interesting... </li></ul></ul></ul></ul><ul><ul><ul><ul><li>“ The Unreclassified Fraction” </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Power for both upwards and downwards reclassification </li></ul></ul></ul></ul><ul><ul><ul><li>You get 1 shot at this! Choose the RIGHT algorithm </li></ul></ul></ul><ul><ul><ul><ul><li>Choose the best biomarker, and best measurement method </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Choose the right intervention and treatment threshold (new drug, $  generic, new evidence, new guidelines) </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Strict vs. loose enforcement of the algorithm? </li></ul></ul></ul></ul>Circulation 2011;123:1116-1124
    36. 36. Measuring Health Impact <ul><li>Randomized controlled trials (RCTs) </li></ul><ul><ul><li>Weakness #2: Hard to blind </li></ul></ul><ul><ul><ul><li>Clinicians need to know biomarker results to change treatment </li></ul></ul></ul><ul><ul><ul><li>Co-interventions, biased outcome ascertainment </li></ul></ul></ul><ul><ul><li>Weakness #3: Long-term rare events missed </li></ul></ul><ul><ul><ul><li>e.g., Radiation-induced cancer </li></ul></ul></ul>Circulation 2011;123:1116-1124
    37. 37. Measuring Health Impact <ul><li>Decision analysis </li></ul>
    38. 38. Measuring Health Impact <ul><li>Decision analysis </li></ul><ul><ul><li>Compare strategies </li></ul></ul><ul><ul><ul><li>Effectiveness (QALY’s, etc) </li></ul></ul></ul><ul><ul><ul><li>Cost ($) </li></ul></ul></ul><ul><ul><ul><li>Incremental cost-effectiveness ratios (ICER, $/QALY) </li></ul></ul></ul>Circulation 2011;123:1116-1124
    39. 39. Measuring Health Impact <ul><li>Decision analysis </li></ul><ul><ul><li>Strengths: Relatively cheap! </li></ul></ul><ul><ul><ul><li>Can run lots of scenarios </li></ul></ul></ul><ul><ul><ul><ul><li>Different biomarkers, different interventions, different algorithms </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Systematic approach to identifying the optimal strategy </li></ul></ul></ul></ul><ul><ul><ul><li>Sensitivity analysis for “what if…” and to identify key parameters </li></ul></ul></ul>Circulation 2011;123:1116-1124
    40. 40. Measuring Health Impact <ul><li>Decision analysis </li></ul><ul><ul><li>Weakness #1: “All models are wrong…” </li></ul></ul><ul><ul><ul><li>Imperfect evidence and estimates </li></ul></ul></ul><ul><ul><ul><li>Unknown/unanticipated effects </li></ul></ul></ul><ul><ul><ul><li>Can’t run all possible sensitivity analyses </li></ul></ul></ul><ul><ul><ul><li>Fundamental structure flaws </li></ul></ul></ul>Circulation 2011;123:1116-1124
    41. 41. Measuring Health Impact <ul><li>Decision analysis </li></ul><ul><ul><li>Weakness #2: Common metric assumptions </li></ul></ul><ul><ul><ul><li>Utilitarian perspective </li></ul></ul></ul><ul><ul><ul><ul><li>Is it better to save the life of a young person than an old person? </li></ul></ul></ul></ul><ul><ul><ul><li>Weighing apples vs. oranges </li></ul></ul></ul><ul><ul><ul><ul><li>Quality of life/utility estimates differ, pt preferences </li></ul></ul></ul></ul><ul><ul><ul><li>Ideal strategy depends on </li></ul></ul></ul><ul><ul><ul><ul><li>Willingness to pay threshold: ICER<$50,000/QALY? </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Total available resources </li></ul></ul></ul></ul>Circulation 2011;123:1116-1124
    42. 42. Measuring Health Impact <ul><li>Which to use? </li></ul>
    43. 43. Measuring Health Impact <ul><li>Which to use? </li></ul><ul><li>ANSWER: USE BOTH </li></ul>RCT DCEA Model
    44. 44. Measuring Health Impact <ul><li>ANSWER: USE BOTH </li></ul><ul><ul><li>RCT </li></ul></ul><ul><ul><ul><li>Proof of concept </li></ul></ul></ul><ul><ul><ul><li>Identify unanticipated effects </li></ul></ul></ul><ul><ul><ul><li>Test real world implementation </li></ul></ul></ul><ul><ul><li>DCEA model </li></ul></ul><ul><ul><ul><li>Systematic evaluation of strategies </li></ul></ul></ul><ul><ul><ul><li>Identification of key uncertainties </li></ul></ul></ul><ul><ul><ul><li>Synthesize evidence including long-term effects </li></ul></ul></ul><ul><ul><ul><li>Include cost and cost-effectiveness </li></ul></ul></ul>
    45. 45. Measuring Health Impact <ul><li>Comparative effectiveness iterations </li></ul><ul><ul><li>1: RCT shows treatment X is effective </li></ul></ul><ul><ul><li>2: Cohort shows biomarker Y predicts risk </li></ul></ul><ul><ul><li>3: Model shows test-and-treat strategy might be cost-effective, identifies key parameters/uncertainty </li></ul></ul><ul><ul><li>4: Observational study nails down key parameters </li></ul></ul><ul><ul><li>5: Redo model to identify an “ideal” strategy </li></ul></ul><ul><ul><li>6: RCT to test the “ideal” strategy shows benefit but identifies unanticipated harm and implementation barriers </li></ul></ul><ul><ul><li>7: Redo model again including long-term harms and imperfect implementation: still cost-effective?… </li></ul></ul>
    46. 46. For the rest of the day… <ul><li>Focus on: </li></ul><ul><ul><li>Cardiovascular disease interventions </li></ul></ul><ul><ul><li>Biomarkers of CVD risk </li></ul></ul><ul><ul><li>Coronary calcium </li></ul></ul><ul><li>Present both RCT and DCEA plans </li></ul><ul><li>Present DCEA preliminary results </li></ul><ul><li>Get your feedback on theory and how to optimize </li></ul>
    47. 47. Thank you to our sponsors! <ul><li>UCSF Clinical and Translational Science Institute (CTSI) </li></ul><ul><ul><li>Provided food and administrative support </li></ul></ul><ul><li>NHLBI </li></ul><ul><ul><li>Grant support! </li></ul></ul>

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