1. Estimating Absolute Risk Reductions Associated with Interventions in Patients with Type 2 Diabetes Jim Mold, M.D., M.P.H. Brian Firestone, MS2

2. Background  Type 2 diabetes mellitus is a complex condition  Multiple complications are possible and multiple modifiable risk factors have been identified  Clinical efficacy trials of interventions emphasize relative risk reductions (RRR)  Absolute risk reductions (ARR) are more important for decision-making in individual cases

3. Point Estimates of RRR and ARR Base risk X RRR = Absolute Risk Reduction If Base risk = 0.10, and RRR = 0.50, then ARR = 0.10 - 0.05 = 0.05 If Base risk = 0.20, and RRR = 0.50, then ARR = 0.20 – 0.10 = 0.10

4. Estimating Overall ARR Roughly the sum of the ARR for each year of remaining life  Need ARR per year  Base risk may change substantially from year to year  Benefit of an intervention may require time  Need estimated life expectancy

5. Objectives  Review Diabetes PHD, a powerful risk engine developed for use in diabetes;  Estimate absolute risk reductions for various interventions in a typical patient with type 2 diabetes;  Estimate the effects of age, gender, race, and duration of diabetes on these estimates; and  Demonstrate some other interesting questions that can be addressed using this risk engine

6. Risk Engines DiabForecaster Sheffield University Risk Engine UKPDS Risk Engine CDC/RTI Risk Engine CORE EAGLE Diabetes Personal Health Decisions (Diabetes PHD)

7. Risk Engine Modeling Three types: Regression models from single clinical trials (e.g. UKPDS) Markov modeling (e.g. CDC/RTI Engine) Physiological modeling (e.g. PHD)

8. Diabetes Personal Health Decisions (PHD) Archimedes software program Attempts to model diabetes by including >100 biological variables, symptoms, signs, tests, treatments, and outcomes Uses differential equations and object- oriented programming to model the links between variables Keeps all continuous variables continuous

9. Diabetes Personal Health Decisions (PHD) Engine Addresses co-morbidities and treatments with multiple effects Includes not only individual patients, but also aspects of the health care delivery system (facilities, equipment, policies and procedures, costs, and utilities) Data based upon knowledge of pathophysiology, clinical trials, and data from Kaiser Permanente

10. PHD Validation Subjected to a series of 74 validation exercises involving 18 clinical trials, 10 of which were not used in the construction of the engine Correlation between results of PHD simulations and clinical trials overall was astounding (r=0.99) Correlation between absolute differences in outcomes also amazing (r=0.97)

11. Using Diabetes PHD Enter required variables (age, gender, race, age of onset, duration, values of individual risk factors, medications, level of exercise) Each PHD run produces average risks for 1000 cases (different each time). Variability of estimates is small for 10yr risks (<1%) but larger for 20-yr risks (2-3%) and 30-yr risks (3-4%) for risks in the range of 40%. Variability increases with increasing age. www.diabetes.comwww.diabetes.com then search for “risk”

12. PHD Graphical Presentation 80 year old AAM with moderate Alzheimer’s Disease Effect of reducing BP 150 to 130, LDL 150 to 100, A1c 8% to 7%

13. ARR for Reaching ADA Targets Same 80 year old AAM with Alzheimer’s Disease: Effect of reducing BP 150 to 130; LDL 150 to 100; A1c 8% to 7% Estimated Age at Death

14. Mount Hood Case #3 Case used for the Third Mt. Hood Conference on DM Risk Engines 65 y.o. WM nonsmoker with 5 yr history of Type 2 diabetes mellitus with no complications; on no medications; sedentary BMI 28 A1c 10% LDL 120 HDL 45 BP 140/90

15. Effects of Interventions on MI 10-yr Risk ARR Baseline 30% ASA 19% 11% Mod Exercise 24% 6% BP to 130 21.8% 5% A1c to 7% 19% 9% LDL to 100 22% 8% LDL to 70 22% 8% All of Above 11% 19%

16. Effects of Interventions on CVA 10-yr Risk ARR Baseline 17% ASA 15% 2% Mod Exercise 10% 7% BP to 130 15% 2% A1c to 7% 14% 3% LDL to 100 17% 0% LDL to 70 17% 0% All of Above 4% 13%

17. Cumulative Effects of Interventions on MI Risk 10-yr Risk ARRCum. RR Baseline 30% ASA 19% 11% 11% Mod Exercise 24% 6% 13% BP to 130 25% 5% 16% A1c to 7% 21% 9% 18% A1c to 6.5% 19% 11% 18% LDL to 100 22% 8% 19% LDL to 70 22% 8% 19% All of Above 11% 21% 21% =

18. Probabilities of Other Adverse Events (10-year risks) Foot Ulcers: 8% Amputation: 0% ESRD: 0% Eye Problems:1% Blindness: 0% Risk of Foot Ulcers can be reduced by lowering A1c but not by other interventions

19. Comparison of Effects of Interventions by Gender 10-yr. Risk of MI 30% 25% ARR male ARR female Aspirin 11% 6% Lower BP to 130 5% 3% Lower A1c to 7% 9% 6% Lower LDL to 100 8% 1% Lower LDL to 70 8% 4%

20. Comparison of Effects of Interventions by Age 10-yr. Risk MI (male) Base Case 17% 30% ARR 50 y.o. ARR 65 y.o. Aspirin 5% 11% Mod. Exercise 6% Lower BP to 130 5% 5% Lower A1c to 7% 3% 9% Lower LDL to 100 4% 8% All Interventions 19%

21. Effects of Race = Black 10-yr. Risk of MI 30% 19% ARR White ARR Black Aspirin 11% 7% Lower BP to 130 5% 5% Lower A1c to 7% 9% 6% Lower LDL to 100 8% 2% Lower LDL to 70 8% 4%

22. Effect of Lowering Systolic BP on Risk of MI and CVA

23. Effect of Lowering LDL on Risk of MI and CVA with Systolic BP 150

24. Effect of ASA on Risk of MI and CVA with Systolic BP 220

25. Effect of ASA on Risk of MI and CVA with Systolic BP 150

26. Lifetime Benefits of Interventions Started at Different Ages Mt Hood white male; moderate exercise; DM diagnosed at age 60 55 y.o. 65 y.o. Life Expectancy23 years 17 years Current MI Risk/Yr 0.5% 2% Lifetime MI Risk17% 42% ARR Aspirin 5% 12% ARR BP to 130 4% 6% ARR LDL to 70 7% 9%

27. Other Outcomes The Archimedes software contains information on the following outcomes, but this information is not accessible through the internet: Life expectancy Quality-adjusted life expectancy Utilization of health care services Cost of care

28. Conclusions The Archimedes risk engine is a very sophisticated and accurate prediction tool that ought to be useful to clinicians and patients It can also be used to answer interesting research questions Collaboration between primary care researchers and the software developers could potentially be extremely productive