Maximizing Comparative Effectiveness Research The DECIDE CV Consortia Eric D. Peterson, MD, MPH Eric D. Peterson, MD, MPH Professor of Medicine Professor of Medicine Vice Chair for Quality, Duke DOM Vice Chair for Quality, Duke DOM Associate Director, Duke Clinical Associate Director, Duke Clinical Research Institute (DCRI) Research Institute (DCRI) David Magid, MD, MPH David Magid, MD, MPH Director of Research, Colorado Director of Research, Colorado Permanente Medical Group Permanente Medical Group Associate Professor, University of Associate Professor, University of Colorado Colorado
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Maximizing Comparative Effectiveness Research The DECIDE CV Consortia
Maximizing Comparative Effectiveness Research The DECIDE CV Consortia. Eric D. Peterson, MD, MPH Professor of Medicine Vice Chair for Quality, Duke DOM Associate Director, Duke Clinical Research Institute (DCRI) David Magid, MD, MPH Director of Research, Colorado Permanente Medical Group - PowerPoint PPT Presentation
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Maximizing Comparative Effectiveness
Research The DECIDE CV Consortia
Eric D. Peterson, MD, MPHEric D. Peterson, MD, MPH
Professor of MedicineProfessor of Medicine
Vice Chair for Quality, Duke DOMVice Chair for Quality, Duke DOM
Associate Director, Duke Clinical Research Associate Director, Duke Clinical Research Institute (DCRI)Institute (DCRI)
David Magid, MD, MPHDavid Magid, MD, MPHDirector of Research, Colorado Permanente Director of Research, Colorado Permanente
Medical GroupMedical GroupAssociate Professor, University of Colorado Associate Professor, University of Colorado
Comparative Effectiveness Research
Wilensky G Health Affairs Nov 2006:w572-w588
"There is a wealth of data available from large databases that enable us to research important clinical questions,"
"Robust methodology exists for comparing different therapies through observational database analysis.”
Elements Stimulating Comparative Effectiveness Research
As part of ARRA: $1.1 billion set aside for comparative effectiveness research (CER)
Lack of Evidence in Guidelines: Recommendation Based on RCT Data
11.7%11.7%
26.4%26.4%
15.3%15.3%
13.5%13.5%
12.0%12.0%
22.9%22.9%
6.4%6.4%
6.1%6.1%
23.6%23.6%
0.3%0.3%
9.7%9.7%
11.0%11.0%
19.0%19.0%
3.5%3.5%
4.8%4.8%
0%0% 10%10% 20%20% 30%30%
AFAF
Heart failureHeart failure
PADPAD
STEMISTEMI
PerioperativePerioperative
Secondary preventionSecondary prevention
Stable anginaStable angina
SV arrhythmiasSV arrhythmias
UA/NSTEMIUA/NSTEMI
Valvular diseaseValvular disease
VA/SCDVA/SCD
PCIPCI
CABGCABG
PacemakerPacemaker
Radionuclide imagingRadionuclide imaging
Tricoci P et al JAMA 2009
ConceptConcept
OutcomesOutcomes
Clinical EvidenceClinical
Evidence
GuidelinesGuidelines
PerformanceIndicators
PerformanceIndicators
MeasurementMeasurement+ Feedback+ Feedback
MeasurementMeasurement+ Feedback+ Feedback
Cycle of Evidence Development and Dissemination
Large CV Large CV RegistriesRegistriesLarge CV Large CV RegistriesRegistries
Adapted from Califf RM, Peterson ED et al. JACC 2002;40:1895-901
QI InitiativesQI Initiatives
Role of Clinical Registries for Evidence Development:E. Stead: Using the Past to Guide the Future
“Chronic diseases can be studied, but not by the methods of the past. If one wishes to create useful data … computer technology must be exploited.” —Eugene Stead, MD
Led to the concept of “computerized textbook of medicine”
Formed foundation of the Duke Databank for CV Diseases
Spurred a generation of clinical and quantitative researchers
Types of Multicenter Registries
Claims: eg. CMS Advantages: Comprehensive, longitudinal, cover in + out-pt
services Disadvantages: Limited clinical data, age 65+
Managed Care/EHR: eg. Kaiser/VA Advantages: longitudinal, meds, labs, other clinical info Disadvantages: select pts, miss out of coverage care
DES vs BMS Comparative Effectiveness (2008) ACC NCDR +CMS part A
DES vs BMS Subgroups + Imaging (2009) ACC NCDR +CMS part A +B
Aortic Valves (2009) STS + CMS part A
TMR Evaluation (2003) STS
DES vs BMS Comparative Effectiveness (2008) ACC NCDR +CMS part A
DES vs BMS Subgroups + Imaging (2009) ACC NCDR +CMS part A +B
Aortic Valves (2009) STS + CMS part A
Diffusion of TMR into Clinical PracticeDiffusion of TMR into Clinical Practice
0
5
10
15
20
25
30
35
40
1998 1999 2000
Cum
ula
tive
pro
port
ion o
f STS s
ites
per
form
ing T
MR
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
% T
MR
of
tota
l pro
cedure
s
% Sites performing TMR % Total TMR procedures
% TMR+CABG procedures % TMR only
Peterson E. JACC 2003;42:1611-6.
NCDR DES vs BMS Longitudinal Analysis Methods
NCDR DES vs BMS Longitudinal Analysis Methods
Objective: To examine comparative effectiveness and safety of DES vs BMS in a national PCI cohort
Population: All NCDR PCI pts 1/04-12/06
Follow up: Linkage to CMS inpatient claims data using indirect identifiers; 76% matched
Final cohort: 262,700 pts 83% DES; 46% Cypher, 55% Taxus
Analysis: Inverse propensity weighted model
• 102 covariates; Cox PH to verify mortality
Objective: To examine comparative effectiveness and safety of DES vs BMS in a national PCI cohort
Population: All NCDR PCI pts 1/04-12/06
Follow up: Linkage to CMS inpatient claims data using indirect identifiers; 76% matched
Final cohort: 262,700 pts 83% DES; 46% Cypher, 55% Taxus
Analysis: Inverse propensity weighted model
• 102 covariates; Cox PH to verify mortality
Douglas P JACC. 2009 May 5;53(18):1629-41.
ACC 2009 LBCT: NCDR DES vs BMS 30-Month Event Rates
ACC 2009 LBCT: NCDR DES vs BMS 30-Month Event Rates
0
5
10
15
20
25
Death MI Revasc Bleeding Stroke
BMS
DES
HR = 0.91(0.85,0.98)
HR = 0.96(0.88,1.04)
HR = 0.75(0.73,0.77)
HR = 0.76(0.72,0.80)
HR = 0.91(0.89,0.94)
Rat
e /
100
pat
ien
ts
HMORNHMORNHMORNHMORN
Consortium of 15 Health PlansConsortium of 15 Health Plans
Collectively provide community-based healthcare to Collectively provide community-based healthcare to ~11 million persons~11 million persons
Broad age, gender, and racial/ethnic diversity Broad age, gender, and racial/ethnic diversity across sitesacross sites
High patient retention ratesHigh patient retention rates
Consortium of 15 Health PlansConsortium of 15 Health Plans
Collectively provide community-based healthcare to Collectively provide community-based healthcare to ~11 million persons~11 million persons
Broad age, gender, and racial/ethnic diversity Broad age, gender, and racial/ethnic diversity across sitesacross sites
High patient retention ratesHigh patient retention rates
HMORN CentersHMORN Centers
HMORN Health PlansHMORN Health PlansHMORN Health PlansHMORN Health Plans
Established Research Centers Established Research Centers
Diverse delivery settings (e.g. inpatient, outpatient) Diverse delivery settings (e.g. inpatient, outpatient) and care modelsand care models
Provide longitudinal care (including prevention, Provide longitudinal care (including prevention, diagnosis, and treatment)diagnosis, and treatment)
Linked lab, pharmacy, ambulatory care and hospital Linked lab, pharmacy, ambulatory care and hospital datadata
14/15 sites have implemented an electronic medical 14/15 sites have implemented an electronic medical record (EMR)record (EMR)
Established Research Centers Established Research Centers
Diverse delivery settings (e.g. inpatient, outpatient) Diverse delivery settings (e.g. inpatient, outpatient) and care modelsand care models
Provide longitudinal care (including prevention, Provide longitudinal care (including prevention, diagnosis, and treatment)diagnosis, and treatment)
Linked lab, pharmacy, ambulatory care and hospital Linked lab, pharmacy, ambulatory care and hospital datadata
14/15 sites have implemented an electronic medical 14/15 sites have implemented an electronic medical record (EMR)record (EMR)
Registry Data StandardizationRegistry Data Standardization Virtual Data Warehouse (VDW) Virtual Data Warehouse (VDW) Registry Data StandardizationRegistry Data Standardization
Virtual Data Warehouse (VDW) Virtual Data Warehouse (VDW) Common data dictionaryCommon data dictionary Data arrayed using identical names, formats, and Data arrayed using identical names, formats, and
specificationsspecifications
SAS program written at one site can be run at SAS program written at one site can be run at other sites other sites
Increases efficiency of multi-site studiesIncreases efficiency of multi-site studies
NOT a Data Coordinating Center or Centralized NOT a Data Coordinating Center or Centralized Data WarehouseData Warehouse
Common data dictionaryCommon data dictionary Data arrayed using identical names, formats, and Data arrayed using identical names, formats, and
specificationsspecifications
SAS program written at one site can be run at SAS program written at one site can be run at other sites other sites
Increases efficiency of multi-site studiesIncreases efficiency of multi-site studies
NOT a Data Coordinating Center or Centralized NOT a Data Coordinating Center or Centralized Data WarehouseData Warehouse
HMORN VDW Registry HMORN VDW Registry Standardized Data TablesStandardized Data TablesHMORN VDW Registry HMORN VDW Registry
Size – Over 1 million patientsSize – Over 1 million patients
Exposure Assessment – properly identified and Exposure Assessment – properly identified and excluded patients receiving ACE or BB for reasons other excluded patients receiving ACE or BB for reasons other than HTNthan HTN
Ability to control for baseline BP (higher in patient Ability to control for baseline BP (higher in patient receiving BB as 2receiving BB as 2ndnd-line therapy-line therapy
Control for confounding bias using both diagnostic and Control for confounding bias using both diagnostic and lab data (e.g. renal function)lab data (e.g. renal function)
Assess BP controlAssess BP control
Assess progression to renal diseaseAssess progression to renal disease
Size – Over 1 million patientsSize – Over 1 million patients
Exposure Assessment – properly identified and Exposure Assessment – properly identified and excluded patients receiving ACE or BB for reasons other excluded patients receiving ACE or BB for reasons other than HTNthan HTN
Ability to control for baseline BP (higher in patient Ability to control for baseline BP (higher in patient receiving BB as 2receiving BB as 2ndnd-line therapy-line therapy
Control for confounding bias using both diagnostic and Control for confounding bias using both diagnostic and lab data (e.g. renal function)lab data (e.g. renal function)
Assess BP controlAssess BP control
Assess progression to renal diseaseAssess progression to renal disease
BP control at 1 year(adjusted model results)
BP control at 1 year(adjusted model results)
• Control Rates• ACE 70.5%
• β-blocker 69.0% (p=0.09 for comparison)
• Results consistent in subgroup analysis by site, gender and year
• Control Rates• ACE 70.5%
• β-blocker 69.0% (p=0.09 for comparison)
• Results consistent in subgroup analysis by site, gender and year
Conduct and disseminate high-quality CV research with potential to improve health outcomes and care delivery
Engage with Stakeholders group in setting research priorities
Work collaboratively to leverage our joint data Work collaboratively to leverage our joint data resources and expertise resources and expertise
Actively and transparently communicate with external audiences to allow accountability
Conduct and disseminate high-quality CV research with potential to improve health outcomes and care delivery
Engage with Stakeholders group in setting research priorities
Work collaboratively to leverage our joint data Work collaboratively to leverage our joint data resources and expertise resources and expertise
Actively and transparently communicate with external audiences to allow accountability
2008 Kick-off Meeting 2008 Kick-off Meeting
CVC Stakeholder Committee had this initial meeting in October 14, 2008 Project Investigators: HMORN, Duke Governmental Agencies: AHRQ, FDA, NIH, CMS Professional Socities: ACC, AHA, STS Other Observers: Major payors
Topics: Coronary stenting, antiplatelet therapy and aortic valve disease
CVC Stakeholder Committee had this initial meeting in October 14, 2008 Project Investigators: HMORN, Duke Governmental Agencies: AHRQ, FDA, NIH, CMS Professional Socities: ACC, AHA, STS Other Observers: Major payors
Topics: Coronary stenting, antiplatelet therapy and aortic valve disease
Future of CV ConsortiumFuture of CV Consortium
Define and Prioritize Topic Areas Many existing and emerging CV therapies and
diagnostic technologies, including:
─Heart Failure─Coronary Artery Disease─Sudden Cardiac Death─Valvular Heart Disease─Atrial Fibrillation─Hypertension and other risk factor control─Peripheral Vascular Disease─Stroke
Define and Prioritize Topic Areas Many existing and emerging CV therapies and
diagnostic technologies, including:
─Heart Failure─Coronary Artery Disease─Sudden Cardiac Death─Valvular Heart Disease─Atrial Fibrillation─Hypertension and other risk factor control─Peripheral Vascular Disease─Stroke
Future of CV ConsortiumFuture of CV Consortium
Broaden Stakeholders American College of Physicians American Association of Family Physicians Patients
Strengthen Collaborations DEcIDE Network Professional Societies Other Non-governmental agencies
Broaden Stakeholders American College of Physicians American Association of Family Physicians Patients
Strengthen Collaborations DEcIDE Network Professional Societies Other Non-governmental agencies
Proposed CV Consortium Organization
Proposed CV Consortium Organization
At the End of the Day…At the End of the Day…
The CV DEcIDE Consortium and Collaboration can:The CV DEcIDE Consortium and Collaboration can:
capture high quality clinical data efficientlycapture high quality clinical data efficiently
be used for scientific discoverybe used for scientific discovery track patients’ longitudinal caretrack patients’ longitudinal care track drugs/devisestrack drugs/devises be linked to biological/imaging databe linked to biological/imaging data
complement/support traditional and practical RCTscomplement/support traditional and practical RCTs
helps drive new evidence into routine practicehelps drive new evidence into routine practice