A Critical Systematic Review of Modelling Approaches and Methodologies used in Hyperlipidaemia Economic Evaluations

Health economic modelling integrates evidence from multiple sources and relies on transparency to support reimbursement decisions. Hyperlipidaemia is a major contributor to cardiovascular disease and is routinely evaluated within health technology assessment frameworks. This systematic review examines health economic models of hyperlipidaemia, evaluates the methodological approaches used in the model development and identifies opportunities to improve model quality and transparency.

A systematic literature search was conducted in MEDLINE and Embase between 1987 and 2025 to identify hyperlipidaemia health economic models. Screening, data extraction and quality assessment were performed manually, and artificial intelligence (AI) software was used for additional checking, this was followed by conflict resolution. Findings are presented through narrative synthesis. This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO; CRD420251043922). The review assessed key aspects of model structure including model type, type of hyperlipidaemia, population, hyperlipidaemia-related events, model outcomes, time horizon, software used and discounting approach. We assessed methodological quality through the Philips checklist, with a focus on model structure, data usage, the way in which studies addressed uncertainty through sensitivity analysis and model validation.

A total of 154 unique model-based economic evaluations were identified, comprising 132 Markov models, 9 microsimulation models and 1 discrete event simulation. Most economic evaluations explored general hypercholesterolaemia in 138 studies, followed by familial hypercholesterolaemia in 23 studies, while lipoprotein(a) was investigated in two studies. Primary prevention was examined in 89 models, secondary prevention in 50 models and a combination of both in 15 evaluations. Overall methodological quality was assessed as high for models' structure and data usage; however, it was moderate for model consistency.

Hyperlipidaemia models generally had transparent assumptions and a justified structure. However, current models often lack systematic data-selection practices to identify the most appropriate evidence for evaluation. Extensive uncertainty analysis and model validation were frequently absent in the assessed models. To support decision-making, model results should be displayed in an open-source format with publicly available code. Furthermore, patient values are rarely incorporated in current modelling practices, representing missed opportunities for patient-centred care.