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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.

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Presentation on theme: "Maximizing Comparative Effectiveness Research The DECIDE CV Consortia Eric D. Peterson, MD, MPH Professor of Medicine Vice Chair for Quality, Duke DOM."— Presentation transcript:

1 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

2 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.”

3 Elements Stimulating Comparative Effectiveness Research As part of ARRA: $1.1 billion set aside for comparative effectiveness research (CER)

4 IOM CER Priorities 2009

5 Leading Causes of Death in US Htttp://www.cdc.gov/mmwr/preview/mmwrhtml/mm5539a9.htm

6 Lack of Evidence in Guidelines: Recommendation Based on RCT Data11.7% 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%AF Heart failure PAD STEMI Perioperative Secondary prevention Stable angina SV arrhythmias UA/NSTEMI Valvular disease VA/SCD PCI CABG Pacemaker Radionuclide imaging Tricoci P et al JAMA 2009

7 Concept Outcomes Clinical Evidence Guidelines Performance Indicators Performance Indicators Measurement + Feedback Measurement Cycle of Evidence Development and Dissemination Large CV Registries Registries Adapted from Califf RM, Peterson ED et al. JACC 2002;40: QI Initiatives

8 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 n Led to the concept of “computerized textbook of medicine” n Formed foundation of the Duke Databank for CV Diseases n Spurred a generation of clinical and quantitative researchers

9 Types of Multicenter Registries n Claims: eg. CMS l Advantages: Comprehensive, longitudinal, cover in + out-pt services l Disadvantages: Limited clinical data, age 65+ n Managed Care/EHR: eg. Kaiser/VA l Advantages: longitudinal, meds, labs, other clinical info l Disadvantages: select pts, miss out of coverage care n Clinical Registries: eg. ACC/STS/AHA l Advantages: targeted in-depth clinical data l Disadvantages: selective participation, traditionally in-patient focus

10 CV Provider Led Clinical Registries n Society of Thoracic Surgery: 900+ centers l Coronary artery bypass surgery l Valve surgery l Congenital heart surgery l Thoracic surgery n National Cardiovascular Data Registry: Hospitals l Cath/Percutaneous coronary intervention l Implantable cardiac defibrillators (ICD) l Acute coronary syndromes (ACS) l Carotid stenting l Ambulatory CV disease (launching) n AHA-Get With The Guideline Program: hospitals l Coronary artery disease (CAD) l Heart failure l Stroke l Ambulatory module (launching)

11 These CV Clinical Registries are… n large and growing more representative l of US patients, providers, settings n detailed...with rich clinical data l presenting features, treatments, acute outcomes n use standardized data elements l With and among registries n are high quality l complete, accurate l audited

12 CV Registries across the Care Spectrum Primary Prevention Admitting Event Post-Event: Cardiac rehabilitation Secondary Prevention D/C In pt Care Admit HF/Stroke AMI/Care ACTION GWTG HF, CVA ACC-PCI, ICD PVD, Congenital STS-CABG, Valve ACC IC3 GWTG Outpatient TRANSLATE ACS ORBIT-AF AHA H360

13 In-hospital Registry Claims Data In-hospital Registry In-hospital Registry Longitudinal Outcomes Device/Drug Information In-hospital Registry Longitudinal Outcomes Biomarker Gentics Samples Cross sectional studies Longitudinal studies Comparative Effectiveness Translational Discovery Clinical Registries as Engines for Evidence Development

14 Duke DEcIDE and FDA CV Work (to Date) n TMR Evaluation (2003) l STS n DES vs BMS Comparative Effectiveness (2008) l ACC NCDR +CMS part A n DES vs BMS Subgroups + Imaging (2009) l ACC NCDR +CMS part A +B n Aortic Valves (2009) l STS + CMS part A n TMR Evaluation (2003) l STS n DES vs BMS Comparative Effectiveness (2008) l ACC NCDR +CMS part A n DES vs BMS Subgroups + Imaging (2009) l ACC NCDR +CMS part A +B n Aortic Valves (2009) l STS + CMS part A

15 Diffusion of TMR into Clinical Practice Peterson E. JACC 2003;42:

16 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 May 5;53(18):

17 ACC 2009 LBCT: NCDR DES vs BMS 30-Month Event Rates 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) Rate / 100 patients

18 HMORN n Consortium of 15 Health Plans n Collectively provide community-based healthcare to ~11 million persons n Broad age, gender, and racial/ethnic diversity across sites n High patient retention rates

19 HMORN Centers

20 HMORN Health Plans n Established Research Centers n Diverse delivery settings (e.g. inpatient, outpatient) and care models n Provide longitudinal care (including prevention, diagnosis, and treatment) n Linked lab, pharmacy, ambulatory care and hospital data n 14/15 sites have implemented an electronic medical record (EMR)

21 Registry Data Standardization Virtual Data Warehouse (VDW) n Common data dictionary n Data arrayed using identical names, formats, and specifications n SAS program written at one site can be run at other sites n Increases efficiency of multi-site studies n NOT a Data Coordinating Center or Centralized Data Warehouse

22 HMORN VDW Registry Standardized Data Tables n Patient Identification - Unique patient ID n Membership - Enrollment status n Demographics - Age, gender, race/ethnicity n Laboratory - Lab tests and results n Medications - Name, dose, route, date, # pills n Ambulatory - Diagnoses, tests, and procedures n Hospital - Diagnoses and procedures n Benefits - co-payments, co-insurance, deductibles n Vital Signs – BP, HR, BMI n Mortality

23 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

24 CER of BB vs ACE as 2 nd -line Anti-Hypertensive Agents n BP Control usually requires > 1 med n Optimal 2nd-line agent for pts whose BP is not controlled on a thiazide is unknown n 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 n BP Control usually requires > 1 med n Optimal 2nd-line agent for pts whose BP is not controlled on a thiazide is unknown n 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

25 HMORN HTN Registry Unique Characteristics n Size – Over 1 million patients n Exposure Assessment – properly identified and excluded patients receiving ACE or BB for reasons other than HTN n Ability to control for baseline BP (higher in patient receiving BB as 2 nd -line therapy n Control for confounding bias using both diagnostic and lab data (e.g. renal function) n Assess BP control n Assess progression to renal disease

26 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

27 Hypertension S equelae : Cox proportional hazards models Outcome# eventsHazard ratio ACE vs. BB 95% CI MI961.05( ) Stroke (0.68, 1.52) CKD* (stage 3) 1, (0.91, 1.13) * Additionally adjusted for eGFR

28 DEcIDE CV Consortium Vision n Created as part of the Effective Health Care program with the Duke University and the HMO Research Network DEcIDE Centers n Bring expertise in multiple scientific areas to provide comparative effectiveness research n Develop a framework that aligns interests from the clinical community, governmental agencies, payers, professional societies n Created as part of the Effective Health Care program with the Duke University and the HMO Research Network DEcIDE Centers n Bring expertise in multiple scientific areas to provide comparative effectiveness research n Develop a framework that aligns interests from the clinical community, governmental agencies, payers, professional societies

29 CV Consortium – Guiding Principals n Conduct and disseminate high-quality CV research with potential to improve health outcomes and care delivery n Engage with Stakeholders group in setting research priorities n Work collaboratively to leverage our joint data resources and expertise n Actively and transparently communicate with external audiences to allow accountability

30 2008 Kick-off Meeting n CVC Stakeholder Committee had this initial meeting in October 14, 2008 l Project Investigators: HMORN, Duke l Governmental Agencies: AHRQ, FDA, NIH, CMS l Professional Socities: ACC, AHA, STS l Other Observers: Major payors n Topics: Coronary stenting, antiplatelet therapy and aortic valve disease n CVC Stakeholder Committee had this initial meeting in October 14, 2008 l Project Investigators: HMORN, Duke l Governmental Agencies: AHRQ, FDA, NIH, CMS l Professional Socities: ACC, AHA, STS l Other Observers: Major payors n Topics: Coronary stenting, antiplatelet therapy and aortic valve disease

31 Future of CV Consortium n Define and Prioritize Topic Areas l 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 n Define and Prioritize Topic Areas l 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

32 Future of CV Consortium n Broaden Stakeholders l American College of Physicians l American Association of Family Physicians l Patients n Strengthen Collaborations l DEcIDE Network l Professional Societies l Other Non-governmental agencies n Broaden Stakeholders l American College of Physicians l American Association of Family Physicians l Patients n Strengthen Collaborations l DEcIDE Network l Professional Societies l Other Non-governmental agencies

33 Proposed CV Consortium Organization Executive-Operations Committee (AHRQ, Duke, HMORN) Data and Methods Stakeholders (CMS, FDA, NIH, Professional Societies) Project Working Groups Steering Committee (Clinical and Methodologists)

34 At the End of the Day… The CV DEcIDE Consortium and Collaboration can: n capture high quality clinical data efficiently n be used for scientific discovery l track patients’ longitudinal care l track drugs/devises l be linked to biological/imaging data n complement/support traditional and practical RCTs n helps drive new evidence into routine practice

35 Thank you Questions?


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