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Comparative Effectiveness Research, Personalized Medicine, and Health Reform

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Comparative Effectiveness Research, Personalized Medicine, and ... Round1 Voting = 1,268 nominated topics 200 topics. Round 2 Voting = 145 rank-ordered topics ... – PowerPoint PPT presentation

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Title: Comparative Effectiveness Research, Personalized Medicine, and Health Reform


1
Comparative Effectiveness Research, Personalized
Medicine, and Health Reform
  • Harold C. Sox, M.D., MACP
  • Co-chair, the IOM committee
  • for Initial Priority Setting for CER
  • Editor Emeritus
  • Annals of Internal Medicine

2
Personalized Medicine
  • The United States Congress defines personalized
    medicine as "the application of genomic and
    molecular data to better target the delivery of
    health care, facilitate the discovery and
    clinical testing of new products, and help
    determine a person's predisposition to a
    particular disease or condition."

3
Personalized Medicine The Health Policy Context
4
Seriously, we basically have to solve the health
cost problem, or nothing else matters.
  • Paul Krugman
  • NY Times blog on restoring a healthy US economy,
    September 28, 2009

5
Cutting Costs the Senate Finance Bill
  • Reduce market-basket updates of Medicare payments
    to providers.
  • Reduce subsidies to pre-paid Medicare
  • Link Medicare payments to quality of care
  • Reduce Part D subsidies for the wealthy
  • Independent commission to advise Congress on
    Medicare rates.
  • Reduce Medicare DSH payments.
  • Initiate Accountable Care Organizations (like a
    medical home)

6
Cutting Costs Senate Finance
  • Create an Innovation Center in CMS
  • Test strategies for patient-centered care,
    reduced costs, and better quality.
  • Reduce payment for preventable hospitalizations.
  • Increase Part D drug cost rebates

http//www.kff.org/healthreform/sidebyside.cfm
7
Will current legislation control costs?
  • A member of the group, Elizabeth A. McGlynn,
    associate director of RAND Health, said that her
    firms research showed that the legislation would
    do more to provide benefits for the uninsured
    than to change the overall upward trajectory in
    spending.
  • We are not really seeing a lot of evidence that
    the trajectory would change very much, Ms.
    McGlynn said.

8
Personalized Medicine
  • The United States Congress defines personalized
    medicine as "the application of genomic and
    molecular data to better target the delivery of
    health care, facilitate the discovery and
    clinical testing of new products, and help
    determine a person's predisposition to a
    particular disease or condition."

9
Comparative Effectiveness Research (CER) What
is it? Why all the interest?
10
What drives the costs of health care?
  • The availability of expensive technology
  • Technological innovation
  • High prices
  • Uncertainty about effectiveness
  • Profit-taking
  • Imperfect markets
  • Patients need doctors decide someone else pays.

11
What drives the costs of health care?
  • The availability of expensive technology
  • Technological innovation
  • High prices
  • Uncertainty about effectiveness
  • Profit-taking
  • Imperfect markets
  • Patients need doctors decide someone else pays.

12
Per-capita spending across intensity quintiles
Per-capita Medicare Spending 1996
2000
Ratio High to Low 1.61 1.58
13
What expenditures drive small area variations?
Wennberg. Health Affairs. February 13, 2002
14
A rationale for better evidence
  • When the evidence is good, service rates dont
    vary across low and high utilization regions.
  • That should be reassuring.
  • When evidence is lacking, rates are higher in
    regions with high utilization.
  • Perhapsjust perhapsbetter evidence will reduce
    unwanted variation in health care practices.

15
CER in the American Recovery and Reinvestment Act
of 2009
  • 1.1B for CER research
  • 400M to NIH
  • 300M to AHRQ
  • 400M to the Secretary, DHHS
  • Mandated IOM study to establish initial
    priorities for conditions to study with CER
    funding.
  • Due date June 30, 2009

16
The IOM Committees working definition of CER
  • The generation and synthesis of evidence that
    compares the benefits and harms of alternative
    methods to prevent, diagnose, treat, and monitor
    a clinical condition, or to improve the delivery
    of care.
  • The purpose of CER is to assist consumers,
    clinicians, purchasers, and policy makers to make
    informed decisions that will improve health care
    at both the individual and population levels.

Source iom.edu/cerpriorities
17
Whats unique about CER? It includes all of the
following
  • Direct, head-to-head comparisons.
  • Broad range of topics.
  • tests, treatments, strategies for prevention,
    care delivery and monitoring
  • A broad range of beneficiaries
  • patients, clinicians, purchasers, and policy
    makers.
  • Study populations representative of clinical
    practice
  • Focus on patient-centered decision-making
  • tailor the test or treatment to the specific
    characteristics of the patient.

18
Patient-centered
  • Suppose a RCT shows that AgtB, but many patients
    got better on B.
  • Lacking any additional knowledge, you should
    prefer A.
  • Is it possible that some patients would have done
    better on B than A?
  • Can we identify them in advance?
  • Demographic predictors
  • Clinical predictors

19
The Promise of CER
  • Information to help doctors and patients make
    better decisions

20
IOM Committees Voting Process
2,606 recommended CER topics received from 1758
respondents to web-based questionnaire
Round1 Voting 1,268 nominated topics? 200 topics
Round 2 Voting 145 rank-ordered topics
Committee discusses each topic Round 3 Voting on
155 nominated topics
Round 3 Results Final 100 priority topics
21
Figure 5.1 Distribution of the recommended
research priorities by primary and secondary
research areas
22
The IOM the CER program should also
  • Do priority-setting on an ongoing basis.
  • Have a broadly representative oversight committee
  • Engage public participation at all levels of CER
  • Support large-scale, clinical and administrative
    data networks
  • Do research on dissemination of CER findings
  • Support research and innovation in the methods of
    CER
  • Expand and support the CER workforce

23
CER Senate Finance
  • Support comparative effectiveness research by
    establishing a public-private Center for
    Comparative Effectiveness Research to conduct,
    support, and synthesize research on outcomes,
    effectiveness, and appropriateness of health care
    services and procedures.
  • An independent CER Commission will oversee the
    activities of the Center.
  • EC Committee amendment Prohibit use of
    comparative effectiveness research findings to
    deny or ration care or to make coverage decisions
    in Medicare.

http//www.kff.org/healthreform/sidebyside.cfm
24
CER is coming. Everyone has an interest in
seeing it succeed
  • What can you do to help?

25
Helping CER to succeed
  • Learn what CER can do (and what it cant or wont
    do).
  • Speak up. Share your knowledge with others.

26
How could CER improve decision making about
personalized medicine?
27
Measuring the value of genetic tests
  • Genetic markers are tests
  • Whats the best way to measure the value of
    tests?
  • Diagnostic predicting current disease status
  • Prognostic predicting future outcomes

28
What do tests do?
  • Disease detection
  • Diagnostic tests
  • What is the present state of this patient?
  • What is the probability that this patient has
    this disease?
  • How to measure do a cross-sectional study
  • Disease prediction
  • Prognostic tests
  • What is the probability that this patient will
    develop this disease in the future?
  • How to measure Do a cohort study.

29
Tests arent perfect
  • They miss disease, and they give false alarms.
  • Therefore, we have to interpret them in terms of
    probability, not certainty.
  • The question to ask
  • Diagnostic tests how much will the test change
    the probability that the patient has a disease?
  • Prognostic tests how much will the test change
    the probability that the patient will develop a
    disease?

30
Evaluating diagnostic tests
  • Measures of test performance
  • Sensitivity and specificity
  • Sensitivity
  • of diseased patients with test
  • Specificity
  • of non-diseased patients with - test

31
Types of test results
32
Types of test results
Sensitivity TP/ND
Specificity TN/ND-
33
Evaluating diagnostic tests
  • Sensitivity and specificity do not necessarily
    imply health effects
  • Need to measure consequences of test results
  • PET scanning in cancer a political challenge for
    Medicare
  • ? a method for using test performance measures to
    estimate health effects

34
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35
Yes
No
Dont do test
Do test
36
How much does the probability of disease change
after a test result?
  • Bayes Theorem
  • Post-test odds pre-test odds x LR
  • LR sens / (1-spec)
  • LR- (1-sens) / spec

37
Example PET scanning to detect scar recurrence
of colon cancer
  • Is an firm area near the original incision
  • scar tissue?
  • a local recurrence of cancer?
  • The choice
  • Do a biopsy now
  • Do a PET scan and biopsy if its positive.

38
The effect of PET on management
  • Does a negative PET scan lower the probability of
    recurrence enough to alter the decision to biopsy
    the mass?
  • Pre-test probability of recurrence 0.69
  • Sensitivity of PET 0.96
  • Specificity of PET 0.98
  • Use Bayes theorem to calculate post-test
    probability of recurrence

Post-test odds pre-test odds x Likelihood Ratio
39
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40
Prognostic tests
  • What is the probability that this person will
    develop diabetes in 10 years?
  • Age, BP, blood sugar, body weight, TG level,
    family history of diabetes, body mass index.
  • How much will the probability change if the
    patient has genetic polymorphisms that predict
    future diabetes?

41
Joint Effects of Common Genetic Variants on the
Risk for Type 2 Diabetes in U.S. Men and Women of
European Ancestry
Cornelis et al. Ann Intern Med. 2009 150 541 -
550.
42
  • Genome-wide association studies have identified
    genetic polymorphisms associated with diabetes
    mellitus (DM).
  • Individual variants are weakly associated
  • Study questions
  • With more polymorphisms, does the risk of DM
    increase?
  • How much does genetic information improve the
    prediction of DM compared with clinical
    information alone?

43
  • Use 2 large cohorts (NHS 1976 and HPFS 1980)
    followed through 2002.
  • Blood collected 23 and 13 years after start.
  • Case control design
  • Cases 1297 men and 1612 women who developed DM
  • Controls 1338 men and 2163 women without
    diabetes.
  • Tested for 17 SNPs from 13 genetic loci.
  • Calculated genetic risk score (GRS)
  • Tested association of SNP score with development
    of DM, adjusting for
  • Body mass index, exercise, family history of
    diabetes, diet

44
Analysis
  • Tested whether SNP score predicts the development
    of DM, adjusting for
  • Predictors of DM BMI, exercise, FHx, diet
  • Calculated area under ROC curve (a measure of
    discrimination)
  • Clinical factors only
  • Clinical factors GRS
  • Area under ROC probability that someone who
    gets DM has a higher GRS than someone who does
    not get DM.

45
Association of reported loci and risk for type 2
diabetes in pooled analysis of men and women
Cornelis, M. C. et. al. Ann Intern Med
2009150541-550
46
Genetic risk score and risk for type 2 diabetes
Cornelis, M. C. et. al. Ann Intern Med
2009150541-550
47
Receiver-operating characteristic curves for type
2 diabetes
Cornelis, M. C. et. al. Ann Intern Med
2009150541-550
48
Study conclusions
  • The GRS significantly improved casecontrol
    discrimination beyond that afforded by
    conventional risk factors, but the magnitude of
    this improvement was marginal
  • Addition of the GRS increased the AUC by only 1.
  • Caveat given the design of our study, we could
    not precisely estimate the predictive power of
    the GRS and were limited to discriminatory
    analysis.
  • Comment they did not do a net reclassification
    analysis.
  • Would show directly how many subjects change risk
    category due to genetic information.

49
Conclusions
  • The goal of CER help doctors and patients make
    better decisions.
  • CER can help measure the extra value of a test
  • Diagnostic tests difference in probability of
    disease.
  • Prognostic tests difference in discrimination or
    the probability of getting a disease.
  • Better evidence about tests could reduce the cost
    of health care.

50
Questions for the future
  • Will Congress enact a national CER program?
  • Will a CER Program promote research to improve
    decision making?
  • Will doctors and patients use the results of CER?
  • Will better evidence narrow differences in
    utilization rates in high and low geographic
    areas ?lower health care costs.
  • For which diseases will genetic testing improve
    prediction of disease susceptibility?
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