Introduction to the Archimedes Model

Introduction to the Archimedes Model Archimedes, Inc. Corporate Overview Healthcare Modeling Company Based in San Francisco Core technology Archimedes Model Mathematical of human physiology, diseases, interventions and healthcare systems Highly detailed Rigorously validated In development since 1993 David Eddy MD, PhD Len Schlessinger PhD Owned by Kaiser Permanente Spun out as independent organization 2006 Clients Include: American Cancer Society American Association American Heart Association Bristol Myers Squibb CDC Daiichi Sankyo Eli Lilly GlaxoSmithKline Humana Kaiser Permanente NCQA Robert Wood Johnson Foundation... a KAISER PERMANENTE Innovation 2 Publications The Basics The Relationship between Resistance and Related Metabolic Variables to Coronary Artery Disease: A Mathematical Analysis [» Care Publish Ahead of Print, 11/18/2008 ] 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 by Archimedes and Comparison with Other Predicting Models. [» Care, 5/28/2008 ] The Metabolic Syndrome and Cardiovascular Risk: Implications for Clinical Practice. [»International Journal of Obesity, 5/1/2008 ] Risk Calculator: A Simple Tool for Detecting Undiagnosed and Prediabetes. [» Care, 5/1/2008 ] Cure, Care, and Commitment: What Can We Look Forward To? [» Care, 4/15/2008 ] Reflections on science, judgment, and value in evidencebased decision making: a conversation with David Eddy [»Health Affairs, 6/19/2007 ] Medical Decision making: How it can, be improved [»Expert Voices, 5/15/2007 ] Linking Electronic Medical Records To Large Scale Simulation Models: Putting Rapid Learning On Turbo? [»Health Affairs, 1/26/2007 ] Accuracy versus transparency in Pharmacoeconomic ling: finding the right balance. [»Pharmacoeconomics, 6/6/2006 ] Bringing health economic ing 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 control. [»International Journal of Clinical Practice, 11/28/2005 ] Evidence based medicine: a unified approach. [»Health Affairs, 02/15/2005 ] Validation of the Archimedes diabetes. [» Care, 11/15/2003 ] Archimedes: a trial validated of diabetes. [» Care, 11/15/2003 ] Archimedes: a new for simulating health care systems: the mathematical formulation [Journal of Biomedical Informatics, 2/2002] The Archimedes Model is a mathematical of human physiology, diseases, interventions, and healthcare systems Physiology based Realistic i Rigorously validated Comprehensive Clinically and administratively detailed Enables decision makers to understand likely outcomes of various interventions 3 4 Physiology Based Family history Gliburide level Unexplained variance in T test Sex Race/ Age Type 1 Type 2 Amount Joint Efficiency of insulin use Glucose uptake by muscle Glucose by liver error and variation HbA1c Glucose test test HbA1c test diagnosis BMI Age, sex, race/ it Metformin Untreated insulin level Normal liver Care processes Fractional change in Urine ketone test Ketoacidosis To s Height Weight blood Diet and exercise HDL cholesterol LDL Triglyceride cholesterol s Smoking cardiac risk to blurred Hypoglycemia Blurred Patient takes action Memory Peripheral resistance Mean arterial Systolic blood Coronary artery stenosis to polyuria Polyuria Perception Cardiac output Arterial compliance Pulse \ to fatigue Fatigue To the To the To the Nephropathy To the CAD Retinopathy Neuropathy to thirst Thirst 5 6 1

Family history Sex Race/ Age BMI Height Type 1 Type 2 Age, sex, race/ Weight blood Peripheral resistance Cardiac output Gliburide level Unexplained variance in Joint (Pancreas) Glucose uptake by muscle efficiency (Muscle) Glucose by liver efficiency Untreated (Liver) insulin level Normal liver Metformin UKPDS data Diet and exercise HDL LDL Triglyceride Smoking cardiac risk cholesterol cholesterol s Mean Systolic Coronary arterial blood artery stenosis Arterial Pulse \ compliance To the To the To the To the Coronary Retinopathy Nephropath Neuropathy artery y disease error and variation Care processes Fractional change in to blurred to polyuria to fatigue to thirst T test diagnosis test test HbA1c test Urine ketone test Blurred Memory Polyuria Perception Fatigue Thirst To s Patient takes action Family history Sex Type 1 Race/ Type 2 Age Age, sex, BMI race/ Me tformin Height Weight Gliburide level Joint (Pancreas) Glucose uptake by muscle efficiency (Muscle) Glucose by li ver efficiency Untreated (Liver) insulin level Normal liver UKPDS data Diet and exercise blood HDL LDL Triglyceride Smoking cholesterol cholesterol s Mean Systolic Coronary Peripheral arterial blood artery resistance stenosis Cardiac Arterial Pulse \ output compliance To the To the To the Retinopathy Nephropa th Neuropathy y Unexplained variance in error and variation Care processes Fractional change in cardiac risk to blurred to polyuria to fatigue To the Coronary artery to thirst disease mo del Hypoglycemia Ketoacidosis T test diagnosis test te st HbA 1c test Urine ketone test To tre atment Ketoacidosis s Hypoglycemia Patient takes action Blurred Memory Polyuria Perception Fatigue Thirst 6/4/2009 7 8 Realistic Biological variables that are continuous in reality are continuous in the Model No discrete states or strata Time is continuous No intervals, steps, or annual jumps Any event can occur at any time Years, months, hours, minutes, seconds... Comprehensive Behaviors Symptoms Signs Diseases Outcomes 9 10 Sample Mapping to DRG Sample Mapping to DRG ID Event Type Event Description Time (days) 32 Visit CHF_Repeat--primaryCare 4,492 32 Procedure bloodpressure 4,492 32 Procedure ekg 4,492 32 Procedure fulllipidpanel 4,492 32 Procedure standardhistory 4,492 32 Procedure standardphysical 4,492 32 Encounter marking beginencounter 4,964 32 Health outcome Myocardial_Infarction 4,964 32 Admission emergencydepartmenttriage--chestpain 4,964 32 Admission emergencydepartment--myocardialinfarction 4,964 32 Procedure bloodpressure 4,964 32 Procedure chestpainhistory 4,964 32 Procedure chestpainphysical 4,964 32 Procedure chestxray 4,964 32 Procedure CPK_CPKMB 4,964 32 Procedure CPK_CPKMB 4,964 32 Procedure ekg 4,964 32 Health outcome Myocardial_Infarction_Diagnosed 4,965 32 Admission cardiaccatheterization--cardiaccatheterization 4,965 32 Procedure cardiaccatheterization 4,965 32 Procedure angioplasty 4,965 32 Procedure drugelutingstent 4,965 32 Admission CCU--myocardialInfarction 4,965 32 Procedure ekg 4,965 32 Admission telemetry--myocardialinfarction 4,965 32 Procedure ekg 4,965 32 Encounter marking endencounter 4,968 32 Health outcome RetinopathyThreeStepProgression 4,972 Begin encounter MI diagnosed Cardiac catheterization Angioplasty Drug eluting stent End encounter Steps in determining DRG: a) Locate inpatient encounters b) Search for key events which contribute to the DRG Diagnoses Procedures c) Use key events to determine the most appropriate DRG ID Event Type Event Description Time (days) 32 Visit CHF_Repeat--primaryCare 4,492 32 Procedure bloodpressure 4,492 32 Procedure ekg 4,492 32 Procedure fulllipidpanel 4,492 32 Procedure standardhistory 4,492 32 Procedure standardphysical 4,492 32 Encounter marking beginencounter 4,964 32 Health outcome Myocardial_Infarction 4,964 32 Admission emergencydepartmenttriage--chestpain 4,964 32 Admission emergencydepartment--myocardialinfarction 4,964 32 Procedure bloodpressure 4,964 32 Procedure chestpainhistory 4,964 32 Procedure chestpainphysical 4,964 32 Procedure chestxray 4,964 32 Procedure CPK_CPKMB 4,964 32 Procedure CPK_CPKMB 4,964 32 Procedure ekg 4,964 32 Health outcome Myocardial_Infarction_Diagnosed 4,965 32 Admission cardiaccatheterization--cardiaccatheterization 4,965 32 Procedure cardiaccatheterization 4,965 32 Procedure angioplasty 4,965 32 Procedure drugelutingstent 4,965 32 Admission CCU--myocardialInfarction 4,965 32 Procedure ekg 4,965 32 Admission telemetry--myocardialinfarction 4,965 32 Procedure ekg 4,965 32 Encounter marking endencounter 4,968 32 Health outcome RetinopathyThreeStepProgression 4,972 Begin encounter MI diagnosed Cardiac catheterization Angioplasty Drug eluting stent End encounter DRG 557: Percutaneous Cardiovascular Procedure with Drug-Eluting Stent with Major Cardiovascular Diagnosis 2007 Medicare base payment: $13,468 Medicare average length of stay: 4.1 days 11 12 2

Single integrated Enables analysis of Co morbidities Syndromes that affect multiple organ systems Multiple drugs Drugs with multiple effects Enables comparisons and priority setting across full spectrum of populations, diseases and interventions Is continually updated and validated Currently in the Model Coronary artery disease Stroke and complications Obesity Metabolic syndrome Hypertension Dyslipidemia Congestive heart failure Asthma Lung cancer Breast cancer Colon cancer 13 14 Validation of the Model We Compare its Results to Real Results Validate the Model by simulating clinical trials and comparing results Select simulated population to match real population Simulated protocols match real protocols Follow up protocols match real ones Definitions of outcomes match real ones Use highest level of detail possible with trial description and People Treatments Same (Virtual) People Same (Virtual) Treatments ized Controlled Trials Real Outcomes Same? Virtual Outcomes 15 16 Some Trials Against Which the Model has been Validated MRC HOPE DCCT 1º HOT SHEP MICRO HOPE DCCT 2º PPP HHS CARE 4 S CARDS LLRC LIPID IRMA Syst Eur WOSCOPS DPP Lewis ATO Z HPS UKPDS IDNT LIFE VA HIT ALL HAT ETDRS 14 LIFEdiab ANBP2 CAPRICORN MERIT HPS IDEAL TNT PROSPER SAVE Distinguishing Features of the Model Physiologically based Single integrated Co morbidities Combinations across populations, diseases and interventions Calculates the effects of interventions in physiologically realistic way Includes protocols and behaviors Includes the full range of outcomes Clinical Logistic and utilization (e.g. admissions) Financial Validated Flexible 17 17 18 3

Distinguishing Features of the Model Can tailor to specific populations, settings and costs Populations Demographics, behaviors, test results, symptoms, past medical histories, current conditions, current s All at individual level Protocols and practice patterns Guidelines Performance levels Costs Tests, s, visits, hospitalizations, procedures Distinguishing Features of the Model Can calculate effects of changes and new s New populations New science e.g. role of diet New tests or biomarkers New s New guidelines New goals New performance measures Changes in levels of performance or compliance Progress from year to year 19 20 ISPOR 2009 Questions? Prioritization of Interventions in Metabolic Disease Pat McCollam, Pharm.D. Principal Research Scientist Global Health Outcomes Eli Lilly and Company Indianapolis, IN, USA... a KAISER PERMANENTE Innovation 21 Disclosure- Full Time Employee of Eli Lilly and Company Early Phase Drug Development Role of Global Health Outcomes Function Clinical Trials- economic and/or PRO measures within trials Work outside Clinical Trials to Examine- Burden of illness, epidemiology, cost of illness (including cost effectiveness), patterns regarding standard of care Early Phase Drug Development Therapeutic Areas may have multiple candidate molecules targeting similar clinical trial endpoints Examine novel methods to compare potential value of candidate molecules (clinical outcomes, cost effectiveness) Assess population characteristics for potential inclusion/exclusion criteria, event rates, size of clinical trial, drivers of economic costs, etc. 4

Early Phase Drug Development Prioritization Process- Fit in portfolio Expert opinion Published literature Probabilistic ing Others (incl Archimedes) Archimedes in the Literature Multiple examples of predicting clinical trial results Recent publications in diabetes and heart disease- Clinical Outcomes and Cost-Effectiveness of Strategies for Managing People at High Risk for. Eddy DM, Schlessinger L, Kahn R. Arch Int Med 2005;143:251-64 64. Also contains an appendix describing differences between Archimedes and Markov The Impact of Prevention on Reducing the Burden of Cardiovascular Disease. Kahn R, Robertson RM, Smith R, Eddy D. Circulation 2008;118:576-585585 Authors point out the ability to estimate burden of illness and examine multiple interventions in the same Archimedes in the Literature Objective: Evaluate 11 recommended prevention activities to reduce CVD morbidity, mortality, and costs in the US. Design & Methods: Data source- Person-specific specific data from NHANES IV (US), adults aged 20-80 years Estimated number and characteristics for prevention candidates Archimedes created simulated population matched to the real US population (person by person) Simulate clinical trials (30 years duration) to examine each prevention activity alone or altogether Vary performance and compliance levels of the interventions Extensive sensitivity analyses Endpoints- health outcomes, quality of life, medical costs Circulation 2008;118:576-585585 Archimedes in the Literature Note that many are primary prevention activities Circulation 2008;118:576-585585 Archimedes in the Literature Results (1)- Approximately 78% of US adults were candidates for 1 or more interventions (wow!) Assuming maximum performance and full implementation of interventions over the 30 year period MI and stroke would decrease by 63% and 31%, respectively average life expectancy yg gain of 1.3 years possible for all adults Greatest benefits were seen for: ASA in high-risk, controlling pre- diabetes, weight reduction in obesity, BP reduction in diabetes, LDL reduction in CAD While many interventions had cost/qaly values <$50,000 (US), budget impact is not negligible given population sizes and long (30 years) period Provided guidance regarding capabilities Circulation 2008;118:576-585585 Archimedes Model Was Next Step in Evaluating Potential Health Outcomes Novel method to compare potential value of candidate molecules Assess population characteristics for potential inclusion/exclusion criteria, event rates, clinical trial sample size, drivers of economic costs, etc. Attributes of Archimedes Trial-validated Incorporates disease progression and healthcare delivery Disease occurs in continuous fashion without the need for transition probabilities 5

New Molecule Development Unmet Medical Need Commercially Viable Probability Of Success Goal: Conduct a series of simulated clinical trials exploring multiple compounds with assumed effects on single or combinations of key biomarkers Design & Methods: Data source- NHANES IV adults age 20-70 (US) Estimate number and characteristics for candidates Create simulated population matched to the real US population (person by person) Simulate clinical trials (10 years duration) to examine each intervention relative to standard of care (SOC) Multiple variables examined via sensitivity analyses Control arm to simulate full (100%) compliance with SOC, and separate control arm to simulate compliance levels of SOC seen in NHANES Each compound was also ed on a particular subpopulation p with high risk characteristics (metabolic syndrome, diabetes, or existing CVD) Endpoints- biomarker changes, outcomes, quality of life, direct medical costs, cost effectiveness Some information is proprietary and unable to be shared at this time Results (1)- Patient demographics and health characteristics at start MeS DM CVD Control 2 Subgroup Subgroup Subgroup Mean Age 46.2 51.7 56 59.3 % of population Age > 65 yrs old 9.1 15.4 21.6 34.9 Gender (% male) 47.6 45.9 43.7 67.1 Total cholesterol (mg/dl) 197.6 184.7 164.11 164.7 LDL cholesterol (mg/dl) 114.7 95.4 77.9 81.2 HDL cholesterol (mg/dl) 52.5 43.2 50.2 48.1 Triglycerides (mg/dl) 153.2 233 182.2 179 Waist circumference (cm) 96.8 108.3 105.3 104.6 BMI (kg/m^2) 28.3 32.6 32 30.1 Systolic blood (mmhg) 120.5 128.2 133 124.1 Diastolic blood (mmhg) 73 76 77.4 71.5 Fasting (mg/dl) 98.8 113.2 147.2 110.6 % Metabolic Syndrome 31.8 100 79.2 58 % > 110 or diagnosed DM 23 50.4 100 46.1 Number alive at baseline 47,850 14,671 4,218 1,591 Results (2)- Outcomes Example, Major Adverse Cardiac Events Results (3)- Outcomes Example, 1 st Myocardial Infarction in 6

Results (4)- Outcomes Example, 1 st MI in pre-existing existing CVD Results (5)- Outcomes Example, 1 st MI in Metabolic Syndrome Results (6)- Outcomes Example, Coronary Revascularization zard (%) Integrated Haz Results (7)- Outcomes Example, Estimate of 10 year MACE by High Risk Subgroup 35 30 25 20 15 10 5 0 Control (n=48,000) MeS (n=14,700) (n=4,250) CVD (n=1,600) MI Stroke Note denominator change when moving to subgroups Next Steps- Explore cost effectiveness sensitivity analysis Web-based based tool to perform additional analyses for simulated population Limitations- US data. How can we understand OUS environment? How to estimate outcomes when a biomarker does not exist? How to efficacy and safety without large scale clinical data? Does one assume simple additive effects of new compound to SOC? Carefully specify initial cohort in order to maximize analyses Conclusions- Several interventions produced similar levels of outcome reduction by different mechanisms (biomarkers) of action Model allows examination of particular items of clinical (MACE) and economic (Revascularization) interest to estimate frequency and timing Powerful tool to examine potential trial outcomes if good inputs (biomarkers) are available 7

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