Cardiac resynchronization therapy (CRT) in heart failure - A model to assess the economic value of this new medical technology

Abstract

Objectives: This article describes the framework of a comprehensive European model developed to assess clinical and economic outcomes of cardiac resynchronization therapy (CRT) versus optimal pharmacological therapy (OPT) alone in patients with heart failure. Methods: The model structure is based on information obtained from the literature, expert opinion, and a European CRT Steering Committee. The decision-analysis tool allows a consideration of direct medical and indirect costs, and computes outcomes for distinctive periods of time up to 5 years. Qualitative data can also be entered for cost-utility analysis. Model input data for a preliminary economic appraisal of the economic value of CRT in Germany were obtained from clinical trials, experts, health statistics, and medical tariff lists. Results: The model offers comprehensive analysis capabilities and high flexibility so that it can easily be adapted to any European country or special setting. The illustrative analysis for Germany indicates that CRT is a cost-effective intervention. Although CRT is associated with average direct medical net costs of €5880 per patient, this finding means that 22% of its upfront implantation cost is recouped already within 1 year because of significantly decreased hospitalizations. With €36,600 the incremental cost per quality-adjusted life-year (QALY) gained is below the euro equivalent (€41,300, €1 = $US1.21) of the commonly used threshold level of $US50,000 considered to represent cost-effectiveness. The sensitivity analysis showed these preliminary results to be fairly robust towards changes in key assumptions. Conclusions: The European CRT model is an important tool to assess the economic value of CRT in patients with moderate to severe heart failure. In the light of the planned introduction of Diagnosis Related Group (DRG) based reimbursement in various European countries, the economic data generated by the model can play an important role in the decision-making process.