M-3 DEVELOPMENT OF A WEB-BASED SIMULATION TOOL TO MODEL COST-EFFECTIVENESS OF DISEASE MANAGEMENT PROGRAMS IN CHRONIC HEART FAILURE

Wednesday, October 23, 2013: 10:30 AM
Key Ballroom 8,11,12 (Hilton Baltimore)
Applied Health Economics (AHE)

Shelby D. Reed, PhD1, Matthew P. Neilson, PhD2, Matthew Gardner3, Andrew Briggs, DPhil4, Yanhong LI, MS1, Sara Paul, RN, MSN, FNP5, David Whellan, MD6, Barbara J. Riegel, DNSc, RN, FAAN, FAHA7 and Wayne C. Levy, M.D., F.A.C.C.8, (1)Duke Clinical Research Institute, Durham, NC, (2)Beatson Institute for Cancer Research, Glasgow G44 4SR, United Kingdom, (3)Duke Clinical Research Institute, dURHAM,, NC, (4)University of Glasgow, Glasgow, United Kingdom, (5)Hickory Cardiology Associates, Western Piedmont Heart Centers, Hickory, NC, (6)Jefferson University, Philadelphia, PA, (7)University of Pennsylvania, Philadelphia, PA, (8)University of Washington Medical Center, Seattle, WA
Purpose: With support from the National Institute of Nursing Research (NINR), we sought to develop economic tools to assist researchers or healthcare managers to estimate costs and perform cost-effectiveness analyses of disease management programs in heart failure.

Methods: With guidance from a steering committee of potential users, we aimed to develop a flexible web-based simulation model that could be applied to various study designs (i.e. parallel groups, pre-post, or hypothetical cases to assist in designing a cost-effective DM program).  The model would be designed to predict medical resource use, survival, quality of life and associated costs across time for simulated patients representing clinical characteristics and unit costs specified by the user. 

 

Results: We developed the Tools for Economic Analysis of Patient Management Interventions in Heart Failure (TEAM-HF) Cost-Effectiveness Model.  To generate simulated sets of patients defined by the user, we incorporated a multivariate distribution wherein the global correlation structure was derived from several randomized trials and prospective cohort studies in heart failure. We also used these empirical data to modify the Seattle Heart Failure Model (SHFM), an externally validated prognostic model that incorporates 15 demographic, clinical and laboratory variables, as well as benefits with evidence-based medications and devices, to generate long-term survival estimates.  In our modification of the SHFM, we applied calibrated Gompertz-based hazard functions for competing causes of death (i.e. sudden death, heart failure, other cause).  We used data from a recent randomized trial in heart failure (HF-ACTION) to generate model parameters for medical resource use and health utilities as a function of SHFM scores. The model applies Monte Carlo simulations to generate patient-level estimates, which are then averaged across cohorts. To extend application of the TEAM-HF model to a broader user group, we developed a user-friendly, web-based interface that allows individuals to specify characteristics of their patient cohort(s), study design, DM program, unit costs and apply other options (e.g. discount rates, time horizons) relevant to conducting cost-effectiveness analyses.

Conclusion:   The TEAM-HF Cost-Effectiveness Model is available at no cost at www.team-hf.org to assist users in estimating the long-term cost effectiveness of disease management programs in heart failure.