Maximizing Comparative Effectiveness Research The DECIDE CV Consortia Slide presentation from the AHRQ 2009 conference. On September 14, 2009, Eric Peterson made this presentation at the 2009 Annual Conference. Select to access the PowerPoint® presentation (1.3 MB) (Plugin Software Help).Slide 1 Maximizing Comparative Effectiveness Research The DECIDE CV ConsortiaEric D. Peterson, MD, MPHProfessor of MedicineVice Chair for Quality, Duke DOMAssociate Director, Duke Clinical Research Institute (DCRI)David Magid, MD, MPHDirector of Research, Colorado Permanente Medical GroupAssociate Professor, University of ColoradoSlide 2 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-w588Slide 3 Elements Stimulating Comparative Effectiveness ResearchAn graph of the "Total Federal Spending for Medicare and Medicaid Under DIfferent Assumptions About Excell Cost Growrh, 1966 to 2050" is shown.As part of ARRA: $1.1 billion set aside for comparative effectiveness research (CER)Slide 4 IOM CER Priorities 2009Health Care Delivery SystemsRacial and Ethical DisparitiesFunctional Limitations and DisabilitiesCardiovascular and Perioheral Vascular DiseaseSlide 5 Leading Causes of Death in USAn image of a graph showing the leading causes of death in the US is shown.Heart diseaseCancerCerebrovascular diseaseChronic lower respiratory diseaseUnintentional injurieswww.cdc.gov/mmwr/preview/mmwrhtml/mm5539a9.htmSlide 6 Lack of Evidence in Guidelines: Recommendation Based on RCT DataAF: 11.7%Heart failure: 26.4%PAD: 15.3%STEMI: 13.5%Perioperative: 12.0%Secondary prevention: 22.9%Stable angina: 6.4%SV arrhythmias: 6.1%UA/NSTEMI: 23.6%Valvular disease: 0.3%VA/SCD: 9.7%PCI: 11.0%CABG: 19.0%Pacemaker: 3.5%Radionuclide imaging: 4.8%Tricoci P et al JAMA 2009Slide 7 Cycle of Evidence Development and DisseminationAn image of the Cycle of Evidence development and dissemination is shown. The image contains:ConceptClinical EvidenceGuidelinesPerformance IndicatorsMeasurement+ FeedbackQI InitiativesOutcomesLarge CV RegistriesAdapted from Califf RM, Peterson EDet al. JACC 2002;40:1895-901Slide 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, MDLed to the concept of "computerized textbook of medicine"Formed foundation of the Duke Databank for CV DiseasesSpurred a generation of clinical and quantitative researchersSlide 9 Types of Multicenter RegistriesClaims: eg. CMS Advantages: Comprehensive, longitudinal, cover in + out-pt servicesDisadvantages: Limited clinical data, age 65+Managed Care/EHR: eg. Kaiser/VA Advantages: longitudinal, meds, labs, other clinical infoDisadvantages: select pts, miss out of coverage careClinical Registries: eg. ACC/STS/AHA Advantages: targeted in-depth clinical dataDisadvantages: selective participation, traditionally in-patient focusSlide 10 CV Provider Led Clinical RegistriesSociety of Thoracic Surgery: 900+ centers Coronary artery bypass surgeryValve surgeryCongenital heart surgeryThoracic surgeryNational Cardiovascular Data Registry: 1600+ Hospitals Cath/Percutaneous coronary interventionImplantable cardiac defibrillators (ICD)Acute coronary syndromes (ACS)Carotid stentingAmbulatory CV disease (launching)AHA-Get With The Guideline Program: 1500+ hospitals Coronary artery disease (CAD)Heart failureStrokeAmbulatory module (launching)Slide 11 These CV Clinical Registries are.Large and growing more representative Of US patients, providers, settingsDetailed...with rich clinical data presenting features, treatments, acute outcomesUse standardized data elements With and among registriesAre high quality Complete, accurateAuditedSlide 12 CV Registries across the Care SpectrumHF/Stroke AMI/Care Primary PreventionAdmitting Event AdmitD/C In pt CarePost-Event: Cardiac rehabilitation Secondary PreventionAHA H360—ACTION GWTG HF, CVA ACC-PCI, ICD PVD, Congenital STS-CABG, Valve—ACC IC3 GWTG Outpatient TRANSLATE ACS ORBIT-AFSlide 13 Clinical Registries as Engines for Evidence DevelopmentIn-hospital RegistryCross sectional studiesIn-hospital RegistryClaims DataLongitudinal studiesIn-hospital RegistryDevice/Drug InformationLongitudinal OutcomesComparative EffectivenessIn-hospital RegistryBiomarker Gentics SamplesLongitudinal OutcomesTranslational DiscoverySlide 14 Duke DEcIDE and FDA CV Work (to Date)TMR Evaluation (2003) STSDES vs BMS Comparative Effectiveness (2008) ACC NCDR +CMS part ADES vs BMS Subgroups + Imaging (2009) ACC NCDR +CMS part A +BAortic Valves (2009) STS + CMS part ASlide 15 Diffusion of TMR into Clinical PracticeDescription199819992000% Sites performing TMR8.71734.4% Total TMR procedures0.080.210.7% TMR+CABG procedures0.060.150.56% TMR only0.020.060.14Peterson E. JACC 2003;42:1611-6.Slide 16 NCDR DES vs BMS Longitudinal Analysis MethodsObjective: To examine comparative effectiveness and safety of DES vs BMS in a national PCI cohortPopulation: All NCDR PCI pts 1/04-12/06Follow up: Linkage to CMS inpatient claims data using indirect identifiers; 76% matchedFinal cohort: 262,700 pts 83% DES; 46% Cypher, 55% TaxusAnalysis: Inverse propensity weighted model 102 covariates; Cox PH to verify mortalityDouglas P JACC. 2009 May 5;53(18):1629-41.Slide 17 ACC 2009 LBCT: NCDR DES vs BMS 30-Month Event Rates DeathMIRevascBleedingStrokeBMS16.58.923.53.62.7DES13.57.523.43.43.1HR = 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 patientsSlide 18 HMORNConsortium of 15 Health PlansCollectively provide community-based healthcare to ~11 million personsBroad age, gender, and racial/ethnic diversity across sitesHigh patient retention ratesSlide 19 HMORN CentersA map of the United States is shown also listing the HMORN Centers.Slide 20 HMORN Health PlansEstablished Research CentersDiverse delivery settings (e.g. inpatient, outpatient) and care modelsProvide longitudinal care (including prevention, diagnosis, and treatment)Linked lab, pharmacy, ambulatory care and hospital data14/15 sites have implemented an electronic medical record (EMR)Slide 21 Registry Data Standardization Virtual Data Warehouse (VDW)Common data dictionaryData arrayed using identical names, formats, and specificationsSAS program written at one site can be run at other sitesIncreases efficiency of multi-site studiesNOT a Data Coordinating Center or Centralized Data WarehouseSlide 22 HMORN VDW Registry Standardized Data TablesPatient Identification—Unique patient IDMembership—Enrollment statusDemographics—Age, gender, race/ethnicityLaboratory— Lab tests and resultsMedications—Name, dose, route, date, # pillsAmbulatory—Diagnoses, tests, and proceduresHospital—Diagnoses and proceduresBenefits—co-payments, co-insurance, deductiblesVital Signs—BP, HR, BMIMortalitySlide 23 AHRQ Sponsored CV Research Projects—HMORNComparative Effectiveness Research 2nd-line Anti-hypertensive therapy�-blockers in patients with heart failureBenefit/Harms of Medications in Routine Practice Clopidogrel duration vs MI, Death, and BleedingInteraction of Clopidogrel and PPIsOutcomes of Medical Devices in Routine Practice Use of DES in off-label indicationsSafety and Effectiveness of of ICDsSlide 24 CER of BB vs ACE as 2nd-line Anti-Hypertensive AgentsBP Control usually requires >1 medOptimal 2nd-line agent for pts whose BP is not controlled on a thiazide is unknownObjective: To compare the effectiveness of ACE-inhibitors (ACE) vs. beta-blockers (BB) for HTN patients who are started on a thiazide but whose BP is inadequately controlled on a thiazide aloneSlide 25 HMORN HTN Registry Unique CharacteristicsSize—Over 1 million patientsExposure Assessment—properly identified and excluded patients receiving ACE or BB for reasons other than HTNAbility to control for baseline BP (higher in patient receiving BB as 2nd-line therapyControl for confounding bias using both diagnostic and lab data (e.g. renal function)Assess BP controlAssess progression to renal diseaseSlide 26 BP control at 1 year (adjusted model results)Control Rates ACE 70.5%�-blocker 69.0% (p=0.09 for comparison)Results consistent in subgroup analysis by site, gender and yearSlide 27 Hypertension Sequelae: Cox proportional hazards modelsOutcome# eventsHazard ratioACE vs. BB95% CIMI961.05(0.69-1.58)Stroke1011.01(0.68, 1.52)CKD*(stage 3)1,4461.02(0.91, 1.13)* Additionally adjusted for eGFR.Slide 28 DEcIDE CV Consortium VisionCreated as part of the Effective Health Care program with the Duke University and the HMO Research Network DEcIDE CentersBring expertise in multiple scientific areas to provide comparative effectiveness researchDevelop a framework that aligns interests from the clinical community, governmental agencies, payers, professional societiesSlide 29 CV Consortium—Guiding PrincipalsConduct and disseminate high-quality CV research with potential to improve health outcomes and care deliveryEngage with Stakeholders group in setting research prioritiesWork collaboratively to leverage our joint data resources and expertiseActively and transparently communicate with external audiences to allow accountabilitySlide 30 2008 Kick-off MeetingCVC Stakeholder Committee had this initial meeting in October 14, 2008 Project Investigators: HMORN, DukeGovernmental Agencies: AHRQ, FDA, NIH, CMSProfessional Socities: ACC, AHA, STSOther Observers: Major payorsTopics: Coronary stenting, antiplatelet therapy and aortic valve diseaseSlide 31 Future of CV ConsortiumDefine and Prioritize Topic Areas Many existing and emerging CV therapies and diagnostic technologies, including: Heart FailureCoronary Artery DiseaseSudden Cardiac DeathValvular Heart DiseaseAtrial FibrillationHypertension and other risk factor controlPeripheral Vascular DiseaseStrokeSlide 32 Future of CV ConsortiumBroaden Stakeholders American College of PhysiciansAmerican Association of Family PhysiciansPatientsStrengthen Collaborations DEcIDE NetworkProfessional SocietiesOther Non-governmental agenciesSlide 33 Proposed CV Consortium OrganizationExecutive-Operations Committee (AHRQ, Duke, HMORN)Steering Committee (Clinical and Methodologists) Data and MethodsStakeholders (CMS, FDA, NIH, Professional Societies)Project Working GroupsSlide 34 At the End of the Day.The CV DEcIDE Consortium and Collaboration can:Capture high quality clinical data efficientlyBe used for scientific discovery Track patients' longitudinal careTrack drugs/devisesBe linked to biological/imaging dataComplement/support traditional and practical RCTsHelps drive new evidence into routine practiceSlide 35 Thank youQuestions? Current as of December 2009 Internet Citation: Maximizing Comparative Effectiveness Research The DECIDE CV Consortia. December 2009. Agency for Healthcare Research and Quality, Rockville, MD. http://www.ahrq.gov/news/events/conference/2009/peterson/index.html