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Using Virtual Population Simulation to Generate Evidence for Reimbursement Discussions Generating Evidence for Reimbursement Decisions Conference Badri.

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Presentation on theme: "Using Virtual Population Simulation to Generate Evidence for Reimbursement Discussions Generating Evidence for Reimbursement Decisions Conference Badri."— Presentation transcript:

1 Using Virtual Population Simulation to Generate Evidence for Reimbursement Discussions Generating Evidence for Reimbursement Decisions Conference Badri Rengarajan, MD November 6, 2012

2 Todays Objectives Understand how virtual population simulation can help inform reimbursement discussions Provide an overview of the Archimedes Model Review case studies 2

3 Topics Development, Commercialization and Simulation Modeling Overview of Archimedes Model Case Study: Lynch Syndrome Closing Thoughts Q&A Appendix: – Case Study: DPP Trial Expansion and Extension (ADA, DHHS) – Illustration: ARCHeS desktop simulation tool 3

4 Development and commercialization are challenging Preclinical & Ph1 Ph2 Registration Development Commercialization Commercial PMCs Ph3 Payor Poor prediction of downstream efficacy/safety Suboptimal patient targeting Inadequate powering: Uncertainty around baseline event rate Synchronizing CDx and Tx development Recruiting challenges Large N for Ph4 study Lack of data in real-world settings Comorbidities Poor compliance Poor adherence Comparative effectiveness Competition/Head-to-head Change in SOC Fitting into current clinical workflow Lack of clarity on reg path Delay Rejection Illustrative 4

5 Reimbursement is challenging, especially in diagnostics Chicken-egg situation – Payors want to see outcomes and data from real-world settings, which will take time to generate – …Yet reimbursement is set today – Coming back later with data in hand unlikely to change reimbursement Financial base of Dx companies much smaller than therapeutics companies – thus cannot conduct several outcomes and post- approval-type studies How to price? – Is Genomic Healths Oncotype Dx my best proxy? – How to avoid getting slotted into precedent stacked codes? – For CDx, what is the Dx value in context of Tx? How can I build a compelling case for payors? 5

6 What To Do Now? Four-leaf clover Rain dance Fed intervention Crystal ball 6

7 Simulation modeling is already used in several areas 7

8 Topics Development, Commercialization and Simulation Modeling Overview of Archimedes Model Case Study: Lynch Syndrome Closing Thoughts Q&A Appendix: – Case Study: DPP Trial Expansion and Extension (ADA, DHHS) – Illustration: ARCHeS desktop simulation tool 8

9 What comes to mind when we hear virtual population simulation? PK/PD simulationMonte Carlo simulation Source: Google Images; Healthcare IT News (April 12, 2012) Mannequins 9 Telemedicine

10 What is Archimedes virtual population simulation not? Simulation – Not PK/PD simulations – Not Monte Carlo simulation for enrollment or marketing – Not mannequins Virtual – Not virtual R&D or outsourced clinical trials – Not telemedicine 10

11 What is Archimedes virtual population simulation? Industrial-strength, full-scale modeling Playing out the lives of thousands of trial subjects approximating real people, without recruiting a single live person Captures physiological/disease outcomes and healthcare system interactions, including patient/provider behaviors – Across different populations – Across different possible trial protocols – Across different healthcare systems and cost frameworks Supplements development and HEOR programs 11

12 The Archimedes Model is used for clinical exploration Virtual world with simulated people, each with simulated physiological outcomes – Virtual patients based on the profiles of real people – Represented as a series of trajectories of correlated risk factors and clinical biomarkers over a lifetime – Evolving through different health states, accumulating disease burden Forecasts the clinical outcomes of drug, device, diagnostic, and care interventions by capturing: – Their influence on these underlying trajectories – Secondary and tertiary effects – Resultant changes in risk of disease and clinical events 12

13 The Archimedes Model is used in HEOR analyses Used to run virtual clinical trials, registries, and observational studies – Longitudinal insight: Project several years forward – Scenario insight: Test multiple alternate realities Captures clinical outcomes, utilization, and costs, thereby facilitating economic analyses The core of the Model is hundreds of algebraic and differential equations translated into 150,000 lines of Java code Continuously validated and updated 13

14 The scope of the Model is clinical 14

15 Patient: Has chest pain and presents to the ER Receives EKG, chest x-ray, and blood tests Is diagnosed with MI, admitted to the hospital, and given an angioplasty with drug-eluting stent Remains in the hospital for 2.1 days and is discharged Who is reimbursedService performedCodes CardiologistConsultation Read EKG Perform cardiac catheterization Perform angioplasty with drug-eluting stent CPT CPT (professional component only) CPT RadiologistRead x-rayCPT (professional component only) AnesthesiologistProvide anesthesia during angioplastyRVG code and time patient is anesthetized (can be converted to a CPT) Other physiciansInpatient consultationsCPT HospitalAll services performed during stayDRG The Model is clinically and administratively detailed, enabling detailed costing analysis 15

16 Several diseases exist in the Model Diabetes (type 1 and 2) Diabetes complications Chronic kidney disease Coronary artery disease Atrial fibrillation Hypertension Stroke (ischemic and hemorrhagic) Lung cancer Breast cancer Colon cancer Bladder Cancer Congestive heart failure Dyslipidemia Obesity Metabolic syndrome Hypertriglyceridemia Asthma COPD 16

17 Case Study Health Benefits and Cost- Effectiveness of Primary Genetic Screening for Lynch Syndrome 17

18 The Model was used to answer a critical question in Lynch Syndrome Situation Lynch syndrome (LS) is a genetically inherited predisposition to multiple types of cancer including colorectal, endometrial, liver, urinary tract, and others. It is autosomal dominant. Patients with suspicious family history are referred for genetic consult, but uptake is low Currently, most testing for LS occurs after an individual develops cancer, at which point the unaffected relatives may also be tested. Genetic test for LS costs about $3500. 18 What if we screen individuals for their risk before cancer occurs, and offer genetic tests to those whose risk exceeds a certain threshold ?

19 Rationale and Approach Rationale: Identify people at risk at a time when prophylaxis, surveillance, and early detection might be most effective Study objective was to identify: 1)Whether primary screening for LS leads to improved health outcomes 2)Whether such a strategy is cost-effective 3)An appropriate age to initiate screening by risk assessment 4)An optimal risk threshold at which to implement genetic testing Approach was to compare: – 1) experimental arm of at-risk individuals and families using a four- gene panel as screening tool, with – 2) control arm of same individuals receiving standard of care for diagnosis and care 19

20 Archimedes convened an advisory panel to help guide model-building Archimedes Approach We recruited a Steering Committee of 5 world-renowned Lynch Syndrome experts to assist in the development of a mathematical model of Lynch Syndrome. The model was built to include: – A virtual population of 100,000 individuals representative of U.S. population with noncarriers and carriers of several mutations – The development (natural history) of colorectal cancer and endometrial cancer in carriers of Lynch Syndrome mutations – Mutation testing and cancer surveillance/screening – The effects of prevention activities (e.g. colonoscopy) and treatments (e.g., colorectal surgery, hysterectomy, chemo, radiation) on cancer outcomes The model accounts for a five-generation family history of all LS- related cancers to allow accurate risk prediction based on family history 20

21 The overall modeling and simulation approach had four stages Build modelValidateSimulate Models of: Natural history of disease (CRC, EC) Mutation prevalence Family history Etc. Other elements: Test characteristics Costs Utility Surgical/procedure mortality Compliance (e.g., CRC screening, colonoscopy, endometrial biopsy) Validate against LS registry for # affected relatives, prevalence of 1 st degree family history of CRC Conduct virtual clinical trial with: 100,000 subjects 20 primary screening strategies Current care as a control Screening strategies based on: Risk assessment at different ages 4-gene mutation testing for individuals exceeding different risk thresholds for carrying mutation Post-test screening of 1 st -degree relatives of mutation carriers 21 Run sensitivities Examine effect of variations in key assumptions and metrics (e.g., cancer incidence, gene test cost, compliance)

22 Strategy #4 Screening Strategy #1 Strategy #2 Strategy #3 Etc. (up to #20) Twenty age/risk screening strategies were examined Age Threshold 20 25 30 35 40 22 Risk Threshold* 0.0% 2.5% 5.0% 10.0% 20 Screening strategies Current Care (Control) Schematic Approach * Risk thresholds represent the probability of carrying a mismatch repair gene mutation above which to initiate genetic testing

23 Pre-test probability =0% =2.5% =5.0% =10% Screening start age: Black = 20, Red = 25, Yellow = 30, Blue = 35, White = 40 Cost Effectiveness 23 Note: For 100,000 patients

24 Pre-test probability =0% =2.5% =5.0% =10% ACER vs Screening Start Age 24 Note: For 100,000 patients

25 Sensitivity analysis of ACER around important model parameters was performed 25

26 Simulation revealed optimal screening strategies Universal screening (age 20 start, 0% risk threshold) leads to good clinical results but is expensive with cost/QALY >$400K An intermediate approach is optimal – Family history–based risk assessment beginning between the ages of 25 and 35 years followed by genetic testing of anyone with a 5% or higher risk of having mutations – Substantial life savings (12-14% reduction in CRC incidence, 8-9% reduction in EC incidence; 1 LY saved) – At average cost-effectiveness ratio of $26,000 per QALY Cost-effectiveness is comparable to that of other screening measures (e.g., screening for colorectal, cervical, and breast cancer) Cost-effectiveness is much more sensitive to risk threshold than starting age of screening The results are published in AACR Cancer Prevention Research. The AACR organized a press conference on Nov 18, 2010 to discuss the findings 26 Key Results

27 Reference Dinh, T.A. et al, Health Benefits and Cost- Effectiveness of Primary Genetic Screening for Lynch Syndrome in the General Population, Cancer Prev Res: 4(1) January 2011. Supplementary data for this article are available at Cancer Prevention Research Online ( 27

28 Topics Development, Commercialization and Simulation Modeling Overview of Archimedes Model Case Study: Lynch Syndrome Closing Thoughts Q&A Appendix: – Case Study: DPP Trial Expansion and Extension (ADA, DHHS) – Illustration: ARCHeS desktop simulation tool 28

29 Archimedes virtual population simulation can help reimbursement discussions Not a simple Markov-type model – captures many relevant variables and is configurable Generates clinical outcomes, utilization, and costs Longitudinal and scenario insight Captures real-world settings (adjustable compliance) Helps find the optimal health economic situation Can supplement and fill gaps in evidence base Overcomes chicken-egg scenario with data/insight available today Less expensive and time-consuming than real-world studies 29

30 Corporate Overview Healthcare modeling company HQ in San Francisco Core technology - Archimedes Model – Mathematical model of human physiology, diseases, interventions, and healthcare systems Highly detailed Carefully validated – In development since 1993 David Eddy MD, PhD Len Schlessinger PhD Owned by Kaiser Permanente – Spun out as independent organization 2006 30

31 Archimedes Clients and Collaborators (Not all can be shown) 31

32 Recent Highlights Sep 17: ARCHeS upgrade – The disease and intervention models in the ARCHeS engine (Simulator) have been upgraded and validated against the most current scientific research and includes a new congestive heart failure (CHF) model. Jul 27: IndiGO API used in HHS app challenge – App developers for the Million Hearts Risk Check Challenge can use the IndiGO API. More...More... June 6, 2012: IndiGO Receives Best of Care Applications Award – The award was presented at the Health Data Initiative III Forum. More...More... May 24, 2012: Major ARCHeS upgrade – As of today ARCHeS users can customize the delivery of care in their clinical trial simulations. More...More... May 3, 2012: HHS Contract – We announced that the U.S. Department of Health and Human Services has contracted with us to use ARCHeS in HHS agencies. More...More... Mar. 28, 2012: Quintiles Agreement – We announced an agreement with Quintiles where they will incorporate ARCHeS into their existing solutions and our customers with have access to Quintiles expertise. More...More... Jan. 19, 2102: IndiGO at Tulsa Health System – MyHealth Access Network, a Beacon Community in Oklahoma, is deploying our IndiGO platform. This is the third deployment of IndiGO in as many months.More...More... Dec. 8, 2011: ARCHeS upgrade and Model (validation) reports available – Upgrade of ARCHeS included numerous improvements to the healthcare processes as well as significant enhancements to the physiology model. Processing speed was increased and several intervention enhancements were made available. Model (validation) reports are now available for downloadavailable Dec.7, 2011: IndiGO at Colorado Beacon Consortium – We entered into an agreement with the Colorado Beacon Consortium (CBC) for the use of the Individualized Guidelines and Outcomes (IndiGO) platform. More...More... Nov. 17, 2011: IndiGO Program Underway at Fairview – We entered into an agreement with Minnesotas Fairview Health Services for the use of the Individualized Guidelines and Outcomes (IndiGO) platform. More...More...

33 Model Description and Validation Report available at Please direct questions to: Badri Rengarajan, MD Medical Director

34 Appendix 34

35 The Archimedes Model The Archimedes Model is a mathematical population simulation model of physiology and diseases, interventions, patient/provider behaviors, and healthcare systems 35

36 Selected Publications Cardiovascular outcomes associated with a new once-weekly GLP-1 receptor agonist vs. traditional therapies for type 2 diabetes: a simulation analysis [ »Diabetes, Obesity, and Metabolism 9/6/2011]Diabetes, Obesity, and Metabolism 9/6/2011 Estimating Health and Economic Benefits from Using Prescription Omega-3 Fatty Acids in Patients with Severe Hypertriglyceridemia. [ »Am J Cardiol. 9/1/2011]Am J Cardiol. 9/1/2011 Individualized Guidelines: The Potential for Increasing Quality and Reducing Costs. [ »Annals of Internal Medicine, 5/2/2011 ]Annals of Internal Medicine, 5/2/2011 Cost-effectiveness of chemoprevention of breast cancer using tamoxifen in a postmenopausal US population [ »CANCER, 3/14/2011 ]CANCER, 3/14/2011 Health Benefits and Cost-Effectiveness of Primary Genetic Screening for Lynch Syndrome in the General Population. [ »Cancer Prevention Research, 11/18/2010 ]Cancer Prevention Research, 11/18/2010 Modeling the effects of omalizumab over 5 years among patients with moderate- to-severe persistent allergic asthma. [ »Current Medical Research and Opinion, 11/04/2010 ]Current Medical Research and Opinion, 11/04/2010 Cost-effectiveness of adding information about common risk alleles to current decision models for breast cancer chemoprevention. [ »Journal of Clinical Oncology, 6/07/2010 ]Journal of Clinical Oncology, 6/07/2010 Age at Initiation and Frequency of Screening to Detect Type 2 Diabetes: A Cost- Effectiveness Analysis [ »The Lancet, 4/30/2010 ] [ »View Technical Appendix ]The Lancet, 4/30/2010View Technical Appendix Model-Based Benefit-Risk Assessment: Can Archimedes Help? [ »Clinical Pharmacology & Therapeutics, 12/15/2009 ]Clinical Pharmacology & Therapeutics, 12/15/2009 Effect of Smoking Cessation Advice on Cardiovascular Disease [ »American Journal of Medical Quality, 5/01/2009 ]American Journal of Medical Quality, 5/01/2009 The Relationship between Insulin Resistance and Related Metabolic Variables to Coronary Artery Disease: A Mathematical Analysis [ »Diabetes Care Publish Ahead of Print, 11/18/2008 ]Diabetes Care Publish Ahead of Print, 11/18/2008 A Physiology-Based Mathematical Model of Coronary Heart Disease Accurately Predicts CHD Event Rates in Real Populations [ »Circulation, 11/08/2008 ]Circulation, 11/08/2008 The potential effects of HEDIS performance measures on the quality of care [ »Health Affairs, 9/15/2008 ]Health Affairs, 9/15/2008 The Impact of Prevention on Reducing the Burden of Cardiovascular Disease [ »Circulation, 7/29/2008 ]Circulation, 7/29/2008 Validation of Prediction of Diabetes by Archimedes and Comparison with Other Predicting Models. [ »Diabetes Care, 5/28/2008 ]Diabetes Care, 5/28/2008 The Metabolic Syndrome and Cardiovascular Risk: Implications for Clinical Practice. [ »International Journal of Obesity, 5/1/2008 ]International Journal of Obesity, 5/1/2008 Diabetes Risk Calculator: A Simple Tool for Detecting Undiagnosed Diabetes and Prediabetes. [ »Diabetes Care, 5/1/2008 ]Diabetes Care, 5/1/2008 Cure, Care, and Commitment: What Can We Look Forward To? [ »Diabetes Care, 4/15/2008 ]Diabetes Care, 4/15/2008 Reflections on science, judgment, and value in evidence-based decision making: a conversation with David Eddy [ »Health Affairs, 6/19/2007 ]Health Affairs, 6/19/2007 Medical Decision-making: Why it must, and how it can, be improved [ »Expert Voices, 5/15/2007 ]Expert Voices, 5/15/2007 Archimedes: A Bold Step Into The Future [ »Health Affairs, 1/26/2007 ]Health Affairs, 1/26/2007 Linking Electronic Medical Records To Large-Scale Simulation Models: Can We Put Rapid Learning On Turbo? [ »Health Affairs, 1/26/2007 ]Health Affairs, 1/26/2007 Accuracy versus transparency in Pharmacoeconomic modelling: finding the right balance. [ »Pharmacoeconomics, 6/6/2006 ]Pharmacoeconomics, 6/6/2006 Bringing health economic modeling to the 21st century. [ »Value in Health, 5/30/2006 ]Value in Health, 5/30/2006 Clinical outcomes and cost-effectiveness of strategies for managing people at high risk for diabetes. [ »Annals of Internal Medicine, 8/16/2005 ]Annals of Internal Medicine, 8/16/2005 Earlier intervention in type 2 diabetes: The case for achieving early. [ »International Journal of Clinical Practice, 11/28/2005 ]International Journal of Clinical Practice, 11/28/2005 Evidence-based medicine: a unified approach. [ »Health Affairs, 02/15/2005 ]Health Affairs, 02/15/2005 Validation of the Archimedes diabetes model. [ »Diabetes Care, 11/15/2003 ]Diabetes Care, 11/15/2003 Archimedes: a trial-validated model of diabetes. [ »Diabetes Care, 11/15/2003 ]Diabetes Care, 11/15/2003 Archimedes: a new model for simulating health care systems - the mathematical formulation. [ »Journal of Biomedical Informatics, 02/06/2002Journal of Biomedical Informatics, 02/06/2002 36

37 Representative Projects – Estimating baseline rate for CV events with different DM therapies – Prioritizing phase 1 portfolio – Forecasting long-term benefits of DM renal drug – Simulating head-to-head trial – Analyzing biomarkers and imaging tests for cardiovascular disease screening Clinical Care – Analyzing prevention and screening programs in DM, CVD, cancer – Evaluating multiple cancer screening modalities in CRC – Analyzing cost effectiveness of genetic screening tool in breast cancer – Building case for superiority of drug regimen change in staff model HMO care guidelines – Assessing cost effectiveness of several health interventions Registration – FDA research collaboration – simulating risk and benefits of weight loss drug sibutramine – Developing physiology and healthcare system model for Lynch Syndrome – Pricing for gene- based cancer diagnostic – Building health economics case for higher-priced cancer drug – Forecasting benefits of Xolair in decreasing asthma symptoms, exacerbations, and hospitalizations over 5 years – Analyzing cost of obesity for national payor Payor / Managed Care Case (Coverage, Reimbursement) Commercial / Launch Clinical Development 37

38 Same (Virtual) People Same (Virtual) Treatments Randomized Controlled Trials People Treatments Real Outcomes Virtual Outcomes Same? The validation approach is rigorous 38

39 Over 50 trials have been used to validate the Model 39

40 There are many reasons to consider using simulation in clinical research Large trial population required Several years before data readout Ethical use precludes high-dose or placebo arm Unknown size and profile of eligible population Need for a preview of trial outcomes Need for effectively testing impact of variations in trial design/protocol elements Budget constraints 40

41 Simulation modeling can help development and commercialization Baseline/control arm event rates Eligible population size and composition Preview of trial outcomes Real-world settings CV outcomes and safety studies Timely Results 41

42 The Model consists of multiple interconnected physiology modules Prob(T2DM) Age BMI FPG FPG 0 E(df2) HbA1C Gender/Race FPG for non-diabetic Fitted to NHANES Family History df2 Disease progression function hits 1 when FPG = 126mg/dL Example: Type II Diabetes Model 42

43 Case Study DPP Trial Expansion and Extension 43

44 Only simulation modeling could have enabled expansion and extension of the DPP trial American Diabetes Association (ADA) sought to understand cost-effectiveness of screening and management guidelines to prevent/delay development of T2DM in high-risk individuals Three-year DPP (Diabetes Prevention Program) trial comparing current care, metformin, and lifestyle modification was nearly complete However, ADA and Department of Health and Human Services (HHS) were interested in long-term health and economic outcomes of different strategies, as well as several questions outside scope of DPP trial Existing trial had already cost many $millions Situation 44

45 The approach involved matching populations & protocols, adding arm, and extending duration Created a simulated population matching DPP inclusion/exclusion criteria and patient baseline characteristics Conducted prospective simulation of DPP trial (same duration, interventions) to validate Models ability to reproduce population, interventions, and outcomes Added intervention arm (lifestyle intervention initiated after diagnosis – FPG >125) Extended duration of simulated trial to 30 years Approach 45

46 The simulation was prospectively validated against the original trial DPP: Diabetes Progression 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0 11.522.533.54 Time (years) Cumulative Incidence of Diabetes lifestyle metformin control 3-Yr Timepoint ActualSimulated Current care 28.9% 27.4% Metformin21.7%21.9% Lifestyle14.4%13.2% 46

47 In simulation, the DPP trial was simultaneously expanded (fourth arm) and extended (30 yrs) Prevent 11% Postpone one decade 47

48 Without Lifestyle (baseline)Difference with Lifestyle Years of follow-up102030102030 Diabetes56.9%68.6%72.2%-14.3%-11.6%-10.8% CAD//CHF Have an MI4.0%8.5%12.0%-0.4%-1.1%-1.7% Develop CHF (systolic or diastolic)0.2%0.7%1.2%-0.1% Stroke (ischemic or hemorrhagic)2.9%7.0%11.6%-0.5%-1.0%-1.4% Some serious complication11.2% 26.1% 38.2% -3%-6.6%-8.4% Deaths CHD2.2%6.6%11.9%-0.6%-1.1%-2.0% Stroke0.4%0.9%1.5%-0.1%-0.3% Renal disease0.00%0.02%0.1%0.00%-0.01%-0.04% Death from any complication2.6%7.6%13.5%-0.7%-1.3%-2.3% Life Years 24.032 0.288 Estimating longitudinal health outcomes generated comparative effectiveness insight (Baseline vs Lifestyle) 22% decrease 48

49 Estimating longitudinal cost outcomes also generated comparative effectiveness insight (Baseline vs. Lifestyle) 49 Expected Costs in a Health Plan with 100,000 Members $MillionsWithout Lifestyle (Baseline)Difference with Lifestyle Years of follow-up51020305102030 Admissions$10$23$58$96$0.8$0.7$1.5$2.2 Visits$8.3$16$33$48-$0.4-$11-$1.7-$2 Procedures$7.4$16$38$57-$0.8-$2-$4-$5.5 Interventions$3.4$6.6$16$26$14$26$48$64 Total$29$62$144$227$14$24$44$59 PMPM for high-risk $57$50$46$41 PMPM for all members $2.29$2.00$1.83$1.64 49

50 The cost effectiveness of each arm over 30 years was revealed 50 $62,600 $24,523 $35,523 $201,800 50

51 Case Illustration Planning Trial and Previewing Outcomes at Your Desktop (ARCHeS Software Application) 51

52 ARCHeS Desktop Tool Screenshots 52

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