Cost-effectiveness of population-wide genomic screening for familial hypercholesterolemia in the United States

J Clin Lipidol. 2022 Jul 30;S1933-2874(22)00220-3. doi: 10.1016/j.jacl.2022.07.014. Online ahead of print.


Background: Population genomic screening for familial hypercholesterolemia (FH) in unselected individuals can prevent premature cardiovascular disease.

Objective: To estimate the clinical and economic outcomes of population-wide FH genomic screening versus no genomic screening.

Methods: We developed a decision tree plus 10-state Markov model evaluating the identification of patients with an FH variant, statin treatment status, LDL-C levels, MI, and stroke to compare the costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness of population-wide FH genomic screening. FH variant prevalence (0.4%) was estimated from the Geisinger MyCode Community Health Initiative (MyCode). Genomic test costs were assumed to be $200. Age and sex-based estimates of MI, recurrent MI, stroke, and recurrent stroke were obtained from Framingham risk equations. Additional outcomes independently associated with FH variants were derived from a retrospective analysis of 26,025 participants screened for FH. Sensitivity and threshold analyses were conducted to evaluate model assumptions and uncertainty.

Results: FH screening was most effective at younger ages; screening unselected 20-year-olds lead to 111 QALYs gained per 100,000 individuals screened at an incremental cost of $20 M. The incremental cost-effectiveness ratio (ICER) for 20-year-olds was $181,000 per QALY, and there was a 38% probability of cost-effectiveness at a $100,000 per QALY willingness-to-pay threshold. If genomic testing cost falls to $100, the ICER would be $91,000 per QALY.

Conclusion: Population FH screening is not cost-effective at current willingness to pay thresholds. However, reducing test costs, testing at younger ages, or including FH within broader multiplex screening panels may improve clinical and economic value.

Keywords: Cost-effectiveness analysis; Cost-utility analysis; Familial hypercholesterolemia; Genomic screening; Population screening.