OBJECTIVES: Heart failure (HF) remains a critical public health concern with significant economic implications. The treatment landscape is expanding, and the potential role of finerenone is being studied. Attempts have been made to assess the economic impact of emerging therapies, specifically SGLT-2 inhibitors, for HF patients through cost-effectiveness modelling. This study aims to synthesise the key challenges in such economic modelling and to propose a new conceptual model structure for HF patients with mildly reduced or preserved ejection fraction (HFmr/pEF).

METHODS: A systematic literature review was conducted to identify previously conducted cost-effectiveness models among patients with HF. Based on these findings, including critique of previous models submitted for health technology assessment to the UK and Canada, a model structure was conceptualised to evaluate the cost-effectiveness of finerenone as add-on therapy to standard of care for HFmr/pEF.

RESULTS: Most of the models identified utilised a Markov model structure and lifetime horizon. The models were driven by short-term HF events and cardiovascular death based on trial primary endpoints, with disease progression measured with either the Kansas City Cardiomyopathy Questionnaire (KCCQ) or with the New York Heart Association (NYHA) classification. The proposed conceptual model uses a Markov approach capable of considering HF severity via either KCCQ or NYHA health states within the same model structure, providing flexibility to align with the preferences of different jurisdictions. Health event states are included for hospitalisation due to HF and urgent HF visits to align with previous HF models, and to reflect the clinical trial for finerenone. The proposed model also includes health states for renal outcomes, including an exploratory health state for end-stage renal disease.

CONCLUSIONS: The conceptual model provides a basis for modelling emerging treatments in HFmr/pEF by considering critique of previous models and by introducing flexibility for the definition of disease health states.