Approaches to modelling the cost-effectiveness of interventions for heart failure: a systematic review

Abstract

OBJECTIVES: To review modelling methods used to assess the cost-effectiveness of interventions for heart failure (HF). METHODS: A systematic search of the literature up to September 2016 across Medline, Embase, Cochrane Library, EconLit and CINAHL databases. We included studies that reported a model-based evaluation, including both costs and health impacts, of a HF intervention. Studies reporting only cost-effectiveness analyses alongside a clinical trial were excluded. RESULTS: We identified 54 publications describing 52 economic models associated with HF interventions. The model-based evaluations comprised surgical (n=20), medical (n=16), service-level (e.g. telehealth, specialist clinics) (n=9) or screening-/monitoring-type interventions (n=4), or assessed disease management (n=2). One study compared multiple interventions. The most common modelling framework was a Markov cohort method (n=41); with models predominantly modelling disease progression via New York Heart Association grade or using a simple two-state (alive/dead) model. Several studies additionally included transition states for hospitalisation events. Two studies adapted the Markov cohort approach for sub-group analyses using risk equations. Eight studies reported a patient-level discrete event simulation approach, and four studies were decision trees. Key structural inputs to model development were data used to model mortality and to predict hospital admissions. CONCLUSIONS: A range of modelling approaches have been used successfully to assess the cost-effectiveness of HF interventions. Whilst the simple Markov cohort approach appears appropriate for the decision problem stated in most cases (i.e. estimating cost effectiveness), other methods have been used to good effect. To date modelling has not addressed the specific nature of the impact of HF on quality of life/ wellbeing, other than via use of NYHA and/or impact of hospital admissions. Future modelling may further consider this through use of natural history states using health states informed by health outcome measures commonly used in HF.