Decision‐analytic evaluation of the clinical effectiveness and cost‐effectiveness of management programmes in chronic heart failure

Abstract

Background and aims: While management programmes (MPs) for chronic heart failure (CHF) are clinically effective, their cost-effectiveness remains uncertain. Thus, this study sought to determine the cost-effectiveness of MPs. Methods and results:We developed a Markov model to estimate life expectancy, quality-adjusted life expectancy, lifetime costs, and the incremental cost-effectiveness of MPs as compared to standard care. Standard care was defined by the EuroHeart Failure Survey for Germany, MP efficacy was derived from our recent meta-analysis and cost estimates were based on the German healthcare system. For a population with a mean age 67 years (35% female) at onset of CHF, our model predicted an average quality-adjusted life expectancy of 2.64 years for standard care and 2.83 years for MP. MP yielded additional lifetime costs of €1700 resulting in an incremental cost-utility ratio (ICUR) of €8900 (95% CI: dominant to 177,100) per quality-adjusted life year (QALY) gained. Sensitivity analyses demonstrated that the ICUR was sensitive to age and sex. Conclusion: MPs increase life expectancy in patients with CHF by an average of 84 days and increase lifetime cost of care by approximately €1700. MPs improve outcomes in a cost-effective manner, although they are not cost-saving on a lifetime horizon.

Estimating cost adjustments required to accomplish target savings in chronic disease management interventions: a simulation study

“…They find that the patients under disease management have a longer lifespan and, hence, produce higher future costs to the system. Göhler et al 36 and Miller et al 37 conduct similar studies and report comparable results. The US Congressional Budget Office 38 has expressed concern that, while interventions are appropriate, from a budgeting perspective the chronic disease management interventions may contribute to projected increases in demand for medical services, and hence costs to the overall system, in the long run.…”

Decision-Analytic Models to Simulate Health Outcomes and Costs in Heart Failure

“…Nineteen studies reported QALYs. [21,22,27,32,33,[35][36][37]39,40,42,[44][45][46][48][49][50][51][52] Most studies presented a reasonable number of deterministic sensitivity analyses on key model parameters. Many recent studies also performed probabilistic sensitivity analyses.…”

“…Many recent studies also performed probabilistic sensitivity analyses. [26,27,33,35,[38][39][40]42,44,46,47,49,51,52] Subgroup analyses for different target populations [35,37,42] and device applications [39] were performed on a limited basis. Eight studies reported validation of the results, including cross-model comparison, face validity through presentation of results to experts or comparison of model results against independent data.…”

“…Strategies investigated included diagnostic procedures (n = 1), [34] screening strategies (n = 1), [33] pharmaceuticals (n = 15), [19][20][21][22][23][24][25][26][27][28][29][30][31]38,52] devices (n = 13), [32,[35][36][37]39,40,[42][43][44][45][46]48,51] disease management interventions (n = 3) [47,49,50] and heart transplantation (n = 1). [41] Modelling approaches used included Modelling Costs and Outcomes in Heart Failure 755…”

Heart failure in primary care with special emphasis on costs and benefits of a disease management programme

“…HC was consequently the most apparent cost driving factor which after all was remarkable since the intervention was executed in a PHC-based setting without directly focusing on HC. There are data showing that HFMPs in PHC is cost-effective compared to conventional standard care of HF (132).…”