Cost-effectiveness analysis of screening for first-degree relatives of patients with bicuspid aortic valve. (14th October 2021) - Tessler, I; Leshno, M; Shmueli, A; Shpitzen, S; Durst, R; Gilon, D
Abstract: Introduction: Bicuspid aortic valve (BAV) is the commonest congenital heart valve defect, found in 1% to 2% of the general population and associated with life-threatening complications. Given the high heritability index of BAV, many experts recommend echocardiography for first-degree relatives (FDRs) of an index patient. However, the cost-effectiveness of such cascade screening for BAV has not been fully evaluated. Materials and methods: Using a decision-analytic model, we performed a cost-effectiveness analysis of echocardiographic screening of FDRs of BAV index cases. Data on BAV probabilities and BAV complications among FDRs were derived from our institution's BAV familial cohort and from the relevant literature on population-based BAV cohorts with long-term follow-up. Health gain was measured as quality-adjusted life years (QALYs). Cost inputs were based on list prices and literature data. One-way and probabilistic sensitivity analyses were performed to account for uncertainty in the model's variables. Results and disscusion: Screening of FDRs was found to be the dominant strategy, being more effective and less costly than no screening, with savings of €208 and gains of 1.6 QALYs. Results were sensitive to the full range of reported BAV rates among FDRs across the literature, with the benefit gradually decreasing from the screening age of 55 years, with trend shifting at the age of 69. Conclusions: This economic evaluation model revealed that echocardiographic screening of FDRs of BAV index case is not only clinically important but also highly cost effective and cost-saving. Health gains could be achieved from initiating screening program, along with costs saving. Sensitivity analysis supported the model's robustness, suggesting its generalization. FUNDunding Acknowledgement: Type of funding sources: Public Institution(s). Main funding source(s): Center for Interdisciplinary Data Science Research fellowships grant