The Role of Risk Stratification and Biomarkers in Prevention of CVD

The role of risk stratification and biomarkers in prevention of CVD

CVD Risk Biomarker SymposiumMark J. Pletcher, MD, MPH

1/30/2012

Objective

• Objective: – Complete translation of biomarkers into

clinical practice when beneficial

Objective

• Objective: – Complete translation of biomarkers into

clinical practice when beneficial

• Gaps: – Need evidence that will convince guideline

committees and clinicians to recommend and use the biomarker in clinical practice

– Need tools to encourage implementation and a good dissemination strategy

Outline

• Framework– Phases of evaluation of a biomarkers– Measuring health impact– RCTs and decision analysis modeling

Framework

• Objective: – Complete translation of biomarkers into clinical

practice when beneficial

• Gaps: – Need evidence that will convince guideline

committees and clinicians to recommend and use the biomarker in clinical practice

– Need tools and strategy to implement and disseminate

What KIND of evidence?

Framework

Circulation 2009;119: 2408-16.

Framework

Circulation 2009;119: 2408-16.

Association

(Discrimination)

Framework

Circulation 2009;119: 2408-16.

Reclassification

Framework

Circulation 2009;119: 2408-16.

Impact

Framework

• My thesis:

– Credible evidence of positive net health impact is and should be absolutely required

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Framework

• My thesis:

– Credible evidence of positive net health impact is and should be absolutely required

• Association, discrimination, reclassification measures are not enough

• Decision analysis modeling and/or studies directly measuring health impact outcomes are required

Circulation 2011;123:1116-1124

Framework

• My thesis:

– Credible evidence of positive net health impact is and should be absolutely required

• Association, discrimination, reclassification measures are not enough

• Decision analysis modeling and/or studies directly measuring health impact outcomes are required

– Magnitude of impact and cost (i.e., cost-effectiveness) should also be considered

Circulation 2011;123:1116-1124

Framework

• 2 high profile examples

Example 1: Multimarker panel

• 10 biomarkers– CRP, BNP, homocysteine, Ualb/cr ratio, etc

• “Evaluate incremental usefulness”

NEJM 2006;355:2631-9

Example 1: Multimarker panel

• Association

NEJM 2006;355:2631-9

Example 1: Multimarker panel

• Association

NEJM 2006;355:2631-9

Example 1: Multimarker panel

• Discrimination

NEJM 2006;355:2631-9

C-statistic:

0.80 0.82

Example 1: Multimarker panel

• Conclusion

– “Adds only moderately to standard risk factors”

• Should we adopt? Abandon hope for CVD biomarkers?

NEJM 2006;355:2631-9

Example 2: CRP

• C-reactive protein

• “Compare prediction models”

Annals 2006;145:21-29

Example 2: CRP

• Association– RR-adj for ln(hsCRP) = 1.22, highly significant

Annals 2006;145:21-29

Example 2: CRP

• Association– RR-adj for ln(hsCRP) = 1.22, highly significant

• Model “fits better” with CRP– BIC improves (6969.60 6960.25)– Calibration poor without (p=.039), good with

(p=.23)

Annals 2006;145:21-29

Example 2: CRP

• Association– RR-adj for ln(hsCRP) = 1.22, highly significant

• Model “fits better” with CRP– BIC improves (6969.60 6960.25)– Calibration poor without (p=.039), good with

(p=.23)

• Discrimination improves a tiny bit– C-index 0.813 0.815– More improvement with CRP than with LDL

Annals 2006;145:21-29

Example 2: CRP

• Reclassification

Annals 2006;145:21-29

Intermediate risk ppts reclassified:

20%

(And reclassified persons’ expected risk closer to what was observed)

Example 2: CRP

• Reclassification– Might make a different treatment decision for

those 20% reclassified

Annals 2006;145:21-29

Example 2: CRP

• Conclusion

– CRP “improves cardiovascular risk classification”

• Should we adopt? Abandon hope for CRP?

Annals 2006;145:21-29

The Common Flaw

• All presented measures:– Ignore the clinical decisions they influence– Do not reflect the health consequences of

making a good vs. bad decision– Do not account for the downsides of

measuring the biomarker itself• Harm, also cost

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The Common Flaw

• All presented measures:– Ignore the clinical decisions they influence– Do not reflect the health consequences of

making a good vs. bad decision– Do not account for the downsides of

measuring the biomarker itself• Harm, also cost

• Cannot estimate net health impact!

Circulation 2011;123:1116-1124

The Answer

• Measure health impact

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The Answer

• Measure health impact

• Instead of:– RR, C-statistic, Hosmer-Lemeshow, IDI, NRI

• Measure:– Death, MI, fractures, disability scale, quality of

life, life-years, QALY’s

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Measuring Health Impact

• It’s hard to measure health impact– Events you care about are rare– How to balance the harms against benefits?– Biomarker impact depends on specifics of the

interventions, and on the algorithm for using the biomarker

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Measuring Health Impact

• It’s hard to measure health impact– Events you care about are rare– How to balance the harms against benefits?– Biomarker impact depends on specifics of the

interventions, and on the algorithm for using the biomarker

• RCTs vs. Decision/cost-effectiveness modeling (vs. both)

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Measuring Health Impact

• RCTs and DCEA models– Overview– Strengths and weaknesses

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Measuring Health Impact

• Randomized controlled trials (RCTs)

Study sample Randomize

Do not measure biomarker

Intervention tailored to biomarker level

Standard intervention

Measure health outcomes

Measure health outcomes

Measure biomarker

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Measuring Health Impact

• Randomized controlled trials (RCTs)– Strengths: A “real-world”(?) unbiased estimate

of effectiveness• Imperfect test performance• Imperfect adherence to algorithm• Estimate portability of the intervention and

effectiveness in a specific population• Can measure utilization, unexpected downsides

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Measuring Health Impact

• Randomized controlled trials (RCTs)– Weakness #1: $$$$$

• Big sample size, long follow-up– Uncommon events like MI are the most interesting...– “The Unreclassified Fraction”– Power for both upwards and downwards reclassification

• You get 1 shot at this! Choose the RIGHT algorithm– Choose the best biomarker, and best measurement method– Choose the right intervention and treatment threshold (new

drug, $generic, new evidence, new guidelines)– Strict vs. loose enforcement of the algorithm?

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Measuring Health Impact

• Randomized controlled trials (RCTs)– Weakness #2: Hard to blind

• Clinicians need to know biomarker results to change treatment

• Co-interventions, biased outcome ascertainment

– Weakness #3: Long-term rare events missed• e.g., Radiation-induced cancer

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Measuring Health Impact

• Decision analysis

Measuring Health Impact

• Decision analysis– Compare strategies

• Effectiveness (QALY’s, etc)• Cost ($)• Incremental cost-effectiveness ratios (ICER,

$/QALY)

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Measuring Health Impact

• Decision analysis– Strengths: Relatively cheap!

• Can run lots of scenarios– Different biomarkers, different interventions, different

algorithms– Systematic approach to identifying the optimal strategy

• Sensitivity analysis for “what if…” and to identify key parameters

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Measuring Health Impact

• Decision analysis– Weakness #1: “All models are wrong…”

• Imperfect evidence and estimates• Unknown/unanticipated effects • Can’t run all possible sensitivity analyses• Fundamental structure flaws

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Measuring Health Impact

• Decision analysis– Weakness #2: Common metric assumptions

• Utilitarian perspective– Is it better to save the life of a young person than an old

person?

• Weighing apples vs. oranges– Quality of life/utility estimates differ, pt preferences

• Ideal strategy depends on– Willingness to pay threshold: ICER<$50,000/QALY?– Total available resources

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Measuring Health Impact

• Which to use?

Measuring Health Impact

• Which to use?

• ANSWER: USE BOTH

RCT

DCEA Model

Measuring Health Impact

• ANSWER: USE BOTH– RCT

• Proof of concept• Identify unanticipated effects• Test real world implementation

– DCEA model• Systematic evaluation of strategies• Identification of key uncertainties• Synthesize evidence including long-term effects• Include cost and cost-effectiveness

Measuring Health Impact

• Comparative effectiveness iterations– 1: RCT shows treatment X is effective– 2: Cohort shows biomarker Y predicts risk– 3: Model shows test-and-treat strategy might be cost-

effective, identifies key parameters/uncertainty– 4: Observational study nails down key parameters– 5: Redo model to identify an “ideal” strategy– 6: RCT to test the “ideal” strategy shows benefit but

identifies unanticipated harm and implementation barriers

– 7: Redo model again including long-term harms and imperfect implementation: still cost-effective?…

For the rest of the day…

• Focus on:– Cardiovascular disease interventions– Biomarkers of CVD risk– Coronary calcium

• Present both RCT and DCEA plans

• Present DCEA preliminary results

• Get your feedback on theory and how to optimize

Thank you to our sponsors!

• UCSF Clinical and Translational Science Institute (CTSI)– Provided food and administrative

support

• NHLBI– Grant support!