Presentation (CTI)

OBJECTIVES: Breast cancer (BrCA) screening is a cornerstone of early detection and prevention, yet current guidelines rely primarily on age-based criteria. Incorporating monogenic risks and polygenic risk scores (PRS) provides an opportunity to personalize screening based on genetic risk, improving efficiency and outcomes. However, the cost-effectiveness of PRS-guided strategies remains unclear. A discrete event simulation (DES) model offers a robust framework to evaluate these strategies by capturing individual variability and probabilistic uncertainty.
METHODS: The DES, developed in Microsoft Excel, simulated time-to-event processes, including time to BrCA, ovarian cancer, risk-reducing mastectomy or salpingo-oophorectomy, cancer-specific mortality, and secular mortality. Cancer incidence was predicted by PRS scores in the screening comparator, with 0.5% of women modeled as hereditary breast and ovarian cancer (HBOC) carriers. Each model run simulated 1,000 women, incorporating health state-specific costs and utilities from published literature. A preliminary probabilistic sensitivity analysis (PSA) of 500 simulations was conducted to validate incremental QALYs and life years by comparing results with the six CISNET models under analogous screening strategies.
RESULTS: Preliminary results showed that the 95% credible intervals for incremental QALYs and life years in our PSA closely overlapped with the CISNET deterministic ranges. For biennial screening (ages 40-74), the RIPS PSA model estimated 135 incremental life years (range: 130-140; CISNET: 120-160) and 115 incremental QALYs (range: 110-120; CISNET: 100-140) per 1,000 women. For biennial screening (ages 50-74), RIPS PSA estimates were 95 incremental life years (range: 90-100; CISNET: 80-120) and 82 incremental QALYs (range: 80-85; CISNET: 70-100).
CONCLUSIONS: These preliminary findings validate the utility of a DES framework for PRS-guided BrCA screening strategies. Future work involves refining outcomes and combining this model with colorectal cancer and cardiovascular disease models to assess the cost-effectiveness of PRS testing across multiple conditions.