Shelby D Reed1, Matthew P Neilson2, Matthew Gardner3, Yanhong Li3, Andrew H Briggs2, Daniel E Polsky4, Felicia L Graham3, Margaret T Bowers5, Sara C Paul6, Bradi B Granger5, Kevin A Schulman7, David J Whellan8, Barbara Riegel9, Wayne C Levy10. 1. Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC; Department of Medicine, Duke University School of Medicine, Durham, NC. Electronic address: shelby.reed@duke.edu. 2. Institute of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom. 3. Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC. 4. Division of General Internal Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA. 5. Duke University School of Nursing, Durham, NC. 6. Catawba Valley Cardiology, Conover, NC. 7. Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC; Department of Medicine, Duke University School of Medicine, Durham, NC. 8. Department of Medicine, Jefferson Medical College, Thomas Jefferson University, Philadelphia, PA. 9. University of Pennsylvania School of Nursing, Philadelphia, PA. 10. University of Washington, Seattle, WA.
Abstract
BACKGROUND: Heart failure disease management programs can influence medical resource use and quality-adjusted survival. Because projecting long-term costs and survival is challenging, a consistent and valid approach to extrapolating short-term outcomes would be valuable. METHODS: We developed the Tools for Economic Analysis of Patient Management Interventions in Heart Failure Cost-Effectiveness Model, a Web-based simulation tool designed to integrate data on demographic, clinical, and laboratory characteristics; use of evidence-based medications; and costs to generate predicted outcomes. Survival projections are based on a modified Seattle Heart Failure Model. Projections of resource use and quality of life are modeled using relationships with time-varying Seattle Heart Failure Model scores. The model can be used to evaluate parallel-group and single-cohort study designs and hypothetical programs. Simulations consist of 10,000 pairs of virtual cohorts used to generate estimates of resource use, costs, survival, and incremental cost-effectiveness ratios from user inputs. RESULTS: The model demonstrated acceptable internal and external validity in replicating resource use, costs, and survival estimates from 3 clinical trials. Simulations to evaluate the cost-effectiveness of heart failure disease management programs across 3 scenarios demonstrate how the model can be used to design a program in which short-term improvements in functioning and use of evidence-based treatments are sufficient to demonstrate good long-term value to the health care system. CONCLUSION: The Tools for Economic Analysis of Patient Management Interventions in Heart Failure Cost-Effectiveness Model provides researchers and providers with a tool for conducting long-term cost-effectiveness analyses of disease management programs in heart failure.
BACKGROUND:Heart failure disease management programs can influence medical resource use and quality-adjusted survival. Because projecting long-term costs and survival is challenging, a consistent and valid approach to extrapolating short-term outcomes would be valuable. METHODS: We developed the Tools for Economic Analysis of Patient Management Interventions in Heart Failure Cost-Effectiveness Model, a Web-based simulation tool designed to integrate data on demographic, clinical, and laboratory characteristics; use of evidence-based medications; and costs to generate predicted outcomes. Survival projections are based on a modified Seattle Heart Failure Model. Projections of resource use and quality of life are modeled using relationships with time-varying Seattle Heart Failure Model scores. The model can be used to evaluate parallel-group and single-cohort study designs and hypothetical programs. Simulations consist of 10,000 pairs of virtual cohorts used to generate estimates of resource use, costs, survival, and incremental cost-effectiveness ratios from user inputs. RESULTS: The model demonstrated acceptable internal and external validity in replicating resource use, costs, and survival estimates from 3 clinical trials. Simulations to evaluate the cost-effectiveness of heart failure disease management programs across 3 scenarios demonstrate how the model can be used to design a program in which short-term improvements in functioning and use of evidence-based treatments are sufficient to demonstrate good long-term value to the health care system. CONCLUSION: The Tools for Economic Analysis of Patient Management Interventions in Heart Failure Cost-Effectiveness Model provides researchers and providers with a tool for conducting long-term cost-effectiveness analyses of disease management programs in heart failure.
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