Using Virtual Population Simulation to Generate Evidence for

Reimbursement DiscussionsGenerating Evidence for Reimbursement

Decisions ConferenceBadri Rengarajan, MD

November 6, 2012

Today’s Objectives

• Understand how virtual population simulation can help inform reimbursement discussions

• Provide an overview of the Archimedes Model• Review case studies

2

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 tool3

Development and commercialization are challenging

Preclinical & Ph1 Ph2 Registration

Development Commercialization

Commercial

PMCsPh3

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

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 Health’s 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

What To Do Now?

Four-leaf clover

Rain dance

Fed intervention

Crystal ball

6

Simulation modeling is already used in several areas

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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 tool8

What comes to mind when we hear“virtual population simulation”?

PK/PD simulation Monte Carlo simulation

Source: Google Images; Healthcare IT News (April 12, 2012)

Mannequins

9

Telemedicine

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

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

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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

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 updated13

The scope of the Model is clinical• Incidence and mortality

rates• Associations between risk

factors and disease

Epidemiological (Populations)

• Risk factor modification• Clinical, lab, and imaging results• Treatments

Clinical (Individuals)

• Hormone levels• Tumor Growth• Metastasis

Physiological

• Mutations• Cell growth signals• Loss of programmed cell death• Agonist and antagonist activity

Cellular

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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 reimbursed Service performed CodesCardiologist Consultation

Read EKGPerform cardiac catheterizationPerform angioplasty with drug-eluting stent

CPT CPT (professional component only)CPTCPT

Radiologist Read x-ray CPT (professional component only)

Anesthesiologist Provide anesthesia during angioplasty RVG code and time patient is anesthetized (can be converted to a CPT)

Other physicians Inpatient consultations CPT

Hospital All services performed during stay DRG

The Model is clinically and administratively detailed, enabling detailed costing analysis

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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

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Case Study

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

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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.

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What if we screen individuals for their risk before cancer occurs, and offer genetic tests

to those whose risk exceeds a certain threshold ?

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

outcomes2) Whether such a strategy is cost-effective3) An appropriate age to initiate screening by risk assessment4) 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

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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

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The overall modeling and simulation approach had four stages

Build model Validate Simulate

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 1st 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 1st-degree relatives of mutation carriers

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Run sensitivities

Examine effect of variations in key assumptions and metrics (e.g., cancer incidence, gene test cost, compliance)

Strategy #4

Screening Strategy #1

Strategy #2

Strategy #3

Etc. (up to #20)

Twenty age/risk screening strategies were examined

Age Threshold

2025303540

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

Pre-test probability =0% =2.5% =5.0% =10%

Screening start age: Black = 20, Red = 25, Yellow = 30, Blue = 35, White = 40

Cost Effectiveness

23Note: For 100,000 patients

Pre-test probability =0% =2.5% =5.0% =10%

ACER vs Screening Start Age

24Note: For 100,000 patients

Sensitivity analysis of ACER around important model parameters was performed

25

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 findings26

Key Results

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 (http://cancerprevres.aacrjournals.org/)

27

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 tool28

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

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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

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Archimedes Clients and Collaborators(Not all can be shown)

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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...

• June 6, 2012: IndiGO Receives Best of Care Applications Award– The award was presented at the Health Data Initiative III Forum. More...

• May 24, 2012: Major ARCHeS upgrade– As of today ARCHeS users can customize the delivery of care in their clinical trial simulations. 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...

• 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...• 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...

• 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 download

• 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...• Nov. 17, 2011: IndiGO Program Underway at Fairview

– We entered into an agreement with Minnesota’s Fairview Health Services for the use of the Individualized Guidelines and Outcomes (IndiGO) platform. More...

Model Description and Validation Report available at http://archimedesmodel.com/resource-center

Please direct questions to:Badri Rengarajan, MDMedical DirectorBadri.Rengarajan@archimedesmodel.com

Appendix

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The Archimedes Model

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

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Selected PublicationsCardiovascular 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]

Estimating Health and Economic Benefits from Using Prescription Omega-3 Fatty Acids in Patients with Severe Hypertriglyceridemia.[ »Am J Cardiol. 9/1/2011]

Individualized Guidelines: The Potential for Increasing Quality and Reducing Costs.[ »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 ]

Health Benefits and Cost-Effectiveness of Primary Genetic Screening for Lynch Syndrome in the General Population.[ »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 ]

Cost-effectiveness of adding information about common risk alleles to current decision models for breast cancer chemoprevention.[ »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 ]

Model-Based Benefit-Risk Assessment: Can Archimedes Help? [ »Clinical Pharmacology & Therapeutics, 12/15/2009 ]

Effect of Smoking Cessation Advice on Cardiovascular Disease[ »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 ]

A Physiology-Based Mathematical Model of Coronary Heart Disease Accurately Predicts CHD Event Rates in Real Populations[ »Circulation, 11/08/2008 ]

The potential effects of HEDIS performance measures on the quality of care[ »Health Affairs, 9/15/2008 ]

The Impact of Prevention on Reducing the Burden of Cardiovascular Disease[ »Circulation, 7/29/2008 ]

Validation of Prediction of Diabetes by Archimedes and Comparison with Other Predicting Models.[ »Diabetes Care, 5/28/2008 ]

The Metabolic Syndrome and Cardiovascular Risk: Implications for Clinical Practice.[ »International Journal of Obesity, 5/1/2008 ]

Diabetes Risk Calculator: A Simple Tool for Detecting Undiagnosed Diabetes and Prediabetes.[ »Diabetes Care, 5/1/2008 ]

Cure, Care, and Commitment: What Can We Look Forward To?[ »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 ]

Medical Decision-making: Why it must, and how it can, be improved[ »Expert Voices, 5/15/2007 ]

Archimedes: A Bold Step Into The Future [ »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 ]

Accuracy versus transparency in Pharmacoeconomic modelling: finding the right balance.[ »Pharmacoeconomics, 6/6/2006 ]

Bringing health economic modeling to the 21st century.[ »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 ]

Earlier intervention in type 2 diabetes: The case for achieving early.[ »International Journal of Clinical Practice, 11/28/2005 ]

Evidence-based medicine: a unified approach.[ »Health Affairs, 02/15/2005 ]

Validation of the Archimedes diabetes model.[ »Diabetes Care, 11/15/2003 ]

Archimedes: a trial-validated model of diabetes.[ »Diabetes Care, 11/15/2003 ]

Archimedes: a new model for simulating health care systems - the mathematical formulation. [ »Journal of Biomedical Informatics, 02/06/2002

36

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 / LaunchClinical Development

37

Same (Virtual)People

Same (Virtual)

Treatments

Randomized Controlled

Trials

People

Treatments

Real Outcomes

VirtualOutcomes

Same?

The validation approach is rigorous

38

4S CPS-II Lung Look-AHEAD SOLVD

ADVANCE DCCT MERIT STENO-2

AGE DPP MicroHOPE SYSTEUR

ALLHAT European Orlistat Obesity Study

Minnesota FOBT Screening Trial TNT

ANBP2 FHS National Polyp Study TRACE

ARIC Flechtner-Mors PROactive UKPDS 33

ASCOTT LLA GLAI PROSPER UKPDS 34

ATBC HOPE RIO-Europe UKPDS 45

CAPRICORN HPS RIO-Lipids UKPDS 80

CARDS IDEAL RIO-North America VALUE

CARE Lieberman Colonoscopy Screening SAVE WESDR

CPS-II Breast LIFE all SEATTLE

CPS-II Colon LIFE diabetes SHEP

Over 50 trials have been used to validate the Model

39 39

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

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

The Model consists of multiple interconnected physiology modules

Prob(T2DM)

Age

BMI

FPG

FPG0

E(df2)

HbA1C

Gender/Race

)( 2

0

dfE

FPGFPG

FPG for non-diabetic Fitted to NHANES

Family History

df2

)2Pr(

2DMType

df

Disease progression function

hits 1 when FPG = 126mg/dL

Example: Type II Diabetes Model

42

Case Study

DPP Trial Expansion and Extension

43

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

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 Model’s 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

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 0.5 1 1.5 2 2.5 3 3.5 4

Time (years)

Cu

mu

lativ

e I

nci

de

nce

of

Dia

be

tes

lifestyle

metformin

control

3-Yr Timepoint

Actual Simulated

Current care 28.9% 27.4%

Metformin 21.7% 21.9%

Lifestyle 14.4% 13.2%

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In simulation, the DPP trial was simultaneously expanded (fourth arm) and extended (30 yrs)

Effect of Four Programs on Progression to Diabetes

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 5 10 15 20 25 30Time (years)

Fra

ctio

n d

evel

op

ing

dia

bet

es

Baseline

LifeStyle

Metformin

Lifestyle when FPG>125

Prevent 11%Postpone one decade

47 47

  Without Lifestyle (baseline) Difference with Lifestyle

Years of follow-up 10 20 30 10 20 30

Diabetes 56.9% 68.6% 72.2% -14.3% -11.6% -10.8%

CAD//CHF            

Have an MI 4.0% 8.5% 12.0% -0.4% -1.1% -1.7%

Develop CHF (systolic or diastolic) 0.2% 0.7% 1.2% -0.1% -0.1% -0.1%

Stroke (ischemic or hemorrhagic) 2.9% 7.0% 11.6% -0.5% -1.0% -1.4%

Some serious complication 11.2% 26.1% 38.2% -3% -6.6% -8.4%

Deaths  

CHD 2.2% 6.6% 11.9% -0.6% -1.1% -2.0%

Stroke 0.4% 0.9% 1.5% -0.1% -0.3% -0.3%

Renal disease 0.00% 0.02% 0.1% 0.00% -0.01% -0.04%

Death from any complication 2.6% 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 48

Estimating longitudinal cost outcomes also generated comparative effectiveness insight (Baseline vs. Lifestyle)

49

Expected Costs in a Health Plan with 100,000 Members

$Millions Without Lifestyle (Baseline) Difference with Lifestyle

Years of follow-up 5 10 20 30 5 10 20 30

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 49

The cost effectiveness of each arm over 30 years was revealed

QALY vs Cost for Four Programs

11.30

11.32

11.34

11.36

11.38

11.40

11.42

11.44

11.46

11.48

11.50

$37,000 $39,000 $41,000 $43,000 $45,000 $47,000 $49,000

30-year Cost/person

30-y

ear

QA

LY

/per

son

No Program

Lifestyle when FPG>125

DPP Lifestyle

Metformin

50

$62,600

$24,523 $35,523

$201,800

50 50

Case Illustration

Planning Trial and Previewing Outcomes at Your Desktop

(ARCHeS Software Application)

51

ARCHeS Desktop Tool Screenshots

52