A Markov cohort model approach was most commonly used (41% of publications) to evaluate the cost-effectiveness of pharmacologic treatments in heart failure, typically relying on NYHA classes.
Systematic Review (n=64)
A systematic review of 64 publications found that Markov cohort models relying on NYHA classes are the most common approach for evaluating the cost-effectiveness of pharmacologic treatments in heart failure.
BACKGROUND: Heart failure (HF) is a well-recognized public health concern and imposes high economic and societal costs. Decision analytic models exist for evaluating the economic ramifications associated with HF. Despite this, studies that appraise these modelling approaches for augmenting best-practice decisions remain scarce. OBJECTIVE: Our objective was to conduct a systematic literature review (SLR) of published economic models for the management of HF and describe their general and methodological features. METHODS: This SLR employed a combination of relevant search terms associated with HF, which were used in a number of databases, including MEDLINE, Embase, the National Health Service Economic Evaluation Database, Cost-Effectiveness Analysis Registry, ScHARR Health Utilities Database and Cochrane Library Database. A number of model features (i.e. model structure, specification, outcomes assessed, scenario and sensitivity analysis, key model drivers) were extracted and subsequently summarized. RESULTS: Of 64 publications retained, a selection of modelling approaches were identified, including Markov (n = 28), trial-based analytic (n = 22), discrete-event simulation (n = 6), survival analytic (n = 7) and decision-tree modelling (n = 1) approaches. The bulk of publications employed either a cost-utility (n = 27) or cost-effectiveness (n = 36) analysis and evaluated more than one study outcome, which typically included overall costs (n = 59), incremental cost-effectiveness ratios (n = 55), life-years gained (n = 48) and willingness-to-pay thresholds (n = 37). Most publications focused on patients with chronic HF (n = 40) and used New York Heart Association (NYHA) disease classifications to categorize patients and determine disease severity. Few (n = 19) publications documented the use of hospitalization states for modelling patient outcomes and associated costs. A quality assessment of the included publications revealed most articles demonstrated reasonable methodological value. CONCLUSIONS: We identified numerous decision analytic modelling approaches for evaluating the cost effectiveness of pharmacologic treatments in HF. A Markov cohort model approach was most commonly used, and most models relied on NYHA classes as a proxy of HF severity, disease progression and prognosis.
Tanna et al. (2018) conducted a systematic review in Heart failure (n=64). Pharmacologic interventions was evaluated on Methodological features of economic models. A Markov cohort model approach was most commonly used (41% of publications) to evaluate the cost-effectiveness of pharmacologic treatments in heart failure, typically relying on NYHA classes.