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Modeling Prioritization of Health Care for Complex Patients Using Archimedes.

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A plethora of guidelines have been created by professional organizations and government agencies over the last 20 years. While these guidelines offer the potential benefits of improving the quality of medical care and reducing health care costs, they have the drawback of not addressing the complexity of individual patients. The proposed research will use a unique simulation software program, Archimedes, to examine the impacts of health care interventions for a set of prototypical patients with diabetes mellitus, hypertension, and hypercholesterolemia with and without pre-existing macrovascular and/or microvascular complications (coronary artery disease, myocardial infarction, congestive heart failure, stroke, retinopathy, nephropathy, and peripheral vascular disease). The set of patient prototypes will include a variety of gender, age, and racial ethnic combinations in order to generalize the results to the U.S. population. The patient prototypes will then be expanded by setting initial condition severity according to action thresholds embedded within national guidelines. Each basic patient prototype will have one of four blood pressure values (130/80, 140/90, 160/100, 180/110), one of five LDL values (70, 100, 130, 160, 190), one of four HDL values (30, 40, 50, 60), one of four triglyceride values (100, 150, 300, 500), and one of five hemoglobin A1c values (7, 8, 9, 10, 12). We will examine the effects of lifestyle interventions and different classes of medications, including aspirin, antihypertensive medications, hypoglycemic agents, and lipid-lowering drugs. We will vary the intensity of treatment goals as recommended by national guidelines. Archimedes will be used to estimate absolute reductions expected at 10, 20, and 30 years in risk of myocardial infarction, stroke, renal failure, blindness, and amputation. Outcomes will be compared for different interventions, combinations of interventions, for each prototypical patient and between prototypical patients.

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