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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
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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 Associate Professor, University of Colorado
Comparative Effectiveness Research "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.” Wilensky G Health Affairs Nov 2006:w572-w588
Elements Stimulating Comparative Effectiveness Research As part of ARRA: $1.1 billion set aside for comparative effectiveness research (CER)
Leading Causes of Death in US Htttp://www.cdc.gov/mmwr/preview/mmwrhtml/mm5539a9.htm
Lack of Evidence in Guidelines: Recommendation Based on RCT Data AF Heart failure PAD STEMI Perioperative Secondary prevention Stable angina SV arrhythmias UA/NSTEMI Valvular disease VA/SCD PCI CABG Pacemaker Radionuclide imaging 11.7% 26.4% 15.3% 13.5% 12.0% 22.9% 6.4% 6.1% 23.6% 0.3% 9.7% 11.0% 19.0% 3.5% 4.8% 0% 10% 20% 30% Tricoci P et al JAMA 2009
Cycle of Evidence Development and Dissemination Clinical Evidence Concept Guidelines Large CV Registries Outcomes Performance Indicators QI Initiatives Measurement + Feedback Adapted from Califf RM, Peterson ED et al. JACC 2002;40:1895-901
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 • Clinical Registries: eg. ACC/STS/AHA • Advantages: targeted in-depth clinical data • Disadvantages: selective participation, traditionally in-patient focus
CV Provider Led Clinical Registries Society of Thoracic Surgery: 900+ centers Coronary artery bypass surgery Valve surgery Congenital heart surgery Thoracic surgery National Cardiovascular Data Registry: 1600+ Hospitals Cath/Percutaneous coronary intervention Implantable cardiac defibrillators (ICD) Acute coronary syndromes (ACS) Carotid stenting Ambulatory CV disease (launching) AHA-Get With The Guideline Program: 1500+ hospitals Coronary artery disease (CAD) Heart failure Stroke Ambulatory module (launching)
These CV Clinical Registries are… large and growing more representative of US patients, providers, settings detailed...with rich clinical data presenting features, treatments, acute outcomes use standardized data elements With and among registries are high quality complete, accurate audited
ACTION GWTG HF, CVA ACC-PCI, ICD PVD, Congenital STS-CABG, Valve CV Registries across the Care Spectrum HF/Stroke AMI/Care Post-Event: Cardiac rehabilitation Secondary Prevention Admitting Event Primary Prevention D/C Admit In pt Care AHA H360 ACC IC3 GWTG Outpatient TRANSLATE ACS ORBIT-AF
Clinical Registries as Engines for Evidence Development In-hospital Registry Cross sectional studies In-hospital Registry Claims Data Longitudinal studies In-hospital Registry Longitudinal Outcomes Comparative Effectiveness Device/Drug Information In-hospital Registry Longitudinal Outcomes Translational Discovery BiomarkerGentics Samples
Duke DEcIDE and FDA CV Work(to Date) • 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 Practice Peterson E. JACC 2003;42:1611-6.
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 Douglas P JACC. 2009 May 5;53(18):1629-41.
ACC 2009 LBCT: NCDR DES vs BMS 30-Month Event Rates Rate / 100 patients HR = 0.76 (0.72,0.80) HR = 0.91 (0.89,0.94) HR = 0.91 (0.85,0.98) HR = 0.96 (0.88,1.04) HR = 0.75 (0.73,0.77)
HMORN Consortium of 15 Health Plans Collectively provide community-based healthcare to ~11 million persons Broad age, gender, and racial/ethnic diversity across sites High patient retention rates
HMORN Health Plans Established Research Centers Diverse delivery settings (e.g. inpatient, outpatient) and care models Provide longitudinal care (including prevention, diagnosis, and treatment) Linked lab, pharmacy, ambulatory care and hospital data 14/15 sites have implemented an electronic medical record (EMR)
Registry Data Standardization Virtual Data Warehouse (VDW) Common data dictionary Data arrayed using identical names, formats, and specifications SAS program written at one site can be run at other sites Increases efficiency of multi-site studies NOT a Data Coordinating Center or Centralized Data Warehouse
HMORN VDW Registry Standardized Data Tables Patient Identification - Unique patient ID Membership - Enrollment status Demographics - Age, gender, race/ethnicity Laboratory - Lab tests and results Medications - Name, dose, route, date, # pills Ambulatory - Diagnoses, tests, and procedures Hospital - Diagnoses and procedures Benefits - co-payments, co-insurance, deductibles Vital Signs – BP, HR, BMI Mortality
AHRQ Sponsored CV Research Projects - HMORN Comparative Effectiveness Research 2nd-line Anti-hypertensive therapy β-blockers in patients with heart failure Benefit/Harms of Medications in Routine Practice Clopidogrel duration vs MI, Death, and Bleeding Interaction of Clopidogrel and PPIs Outcomes of Medical Devices in Routine Practice Use of DES in off-label indications Safety and Effectiveness of of ICDs
CER of BB vs ACE as 2nd-line Anti-Hypertensive Agents BP Control usually requires > 1 med Optimal 2nd-line agent for pts whose BP is not controlled on a thiazide is unknown Objective: To compare the effectiveness of ACE-inhibitors (ACE) vs. β-blockers (BB) for HTN patients who are started on a thiazide but whose BP is inadequately controlled on a thiazide alone
HMORN HTN Registry Unique Characteristics Size – Over 1 million patients Exposure Assessment – properly identified and excluded patients receiving ACE or BB for reasons other than HTN Ability to control for baseline BP (higher in patient receiving BB as 2nd-line therapy Control for confounding bias using both diagnostic and lab data (e.g. renal function) Assess BP control Assess progression to renal disease
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
Hypertension Sequelae:Cox proportional hazards models * Additionally adjusted for eGFR
DEcIDE CV ConsortiumVision Created as part of the Effective Health Care program with the Duke University and the HMO Research Network DEcIDE Centers Bring expertise in multiple scientific areas to provide comparative effectiveness research Develop a framework that aligns interests from the clinical community, governmental agencies, payers, professional societies
CV Consortium – Guiding Principals 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 resources and expertise Actively and transparently communicate with external audiences to allow accountability
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
Future 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
Future 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
At the End of the Day… The CV DEcIDE Consortium and Collaboration can: capture high quality clinical data efficiently be used for scientific discovery track patients’ longitudinal care track drugs/devises be linked to biological/imaging data complement/support traditional and practical RCTs helps drive new evidence into routine practice
Thank you Questions?