BackgroundPolygenic risk score for coronary artery disease (CAD-PRS) improves precision in assessing the risk of cardiovascular diseases and is cost-effective in preventing cardiovascular diseases in a health system and may be cost-effective in other settings and prevention programs such as workplace cardiovascular prevention programs. Workplaces provide a conducitve environment for cardiovascular prevention interventions, but the cost-effectiveness of CAD-PRS in a workplace setting remains unknown. This study examined the cost-effectiveness of integrating CAD-PRS in a workplace cardiovascular disease prevention program compared to the standard cardiovascular workplace program without CAD-PRS and no-workplace prevention program.MethodsWe developed a cohort simulation model to project health benefits (quality-adjusted life years gained) and costs over a period of 5 years in a cohort of employees with a mean age of 50 years. The model health states reflected the risk of disease (coronary artery disease and ischemic stroke) and statin prevention therapy side effects (diabetes, hemorrhagic stroke, and myopathy). We considered medical and lost productivity costs. Data were obtained from the literature, and the analysis was performed from a self-insured employer perspective with future costs and quality-adjusted life years discounted at 3% annually. Uncertainty in model parameter inputs was assessed using deterministic and probabilistic sensitivity analyses. Three programs were compared: (1) a workplace cardiovascular program that integrated CAD-PRS with the pooled cohort equation—a standard of care for assessing the risk of cardiovascular diseases (CardioriskSCORE); (2) a workplace cardiovascular prevention program without CAD-PRS (Standard-WHP); and (3) no-workplace health program (No-WHP). The main outcomes were total costs (US $2019), incremental costs, incremental quality-adjusted life years, and incremental cost-effectiveness ratio.ResultsCardioriskSCORE lowered employer costs ($53 and $575) and improved employee quality-adjusted life years (0.001 and 0.005) per employee screened compared to Standard-WHP and No-WHP, respectively. The effectiveness of statin prevention therapy, employees' baseline cardiovascular risk, the proportion of employees that enrolled in the program, and statin adherence had the largest effect size on the incremental net monetary benefit. However, despite the variation in parameter input values, base case results remained robust.ConclusionPolygenic testing in a workplace cardiovascular prevention program improves employees' quality of life and simultaneously lowers health costs and productivity monetary loss for employers.

Clinical implementation of new prediction models requires evaluation of their validity and utility in a broad range of intended use populations. Here we outline the development and validation of multiple ancestry-specific Polygenic Risk Scores (PRSs) for Coronary Artery Disease (CAD) using a dataset comprising 29,389 individuals from multiple cohorts and diverse genetic ancestry groups. We leverage summary statistics from multiple genome-wide association studies comprising over 850,000 individuals to develop calibrated CAD PRSs with an average Odds Ratio per Standard Deviation (ORxSD) of 1.57 (SD = 0.14). Relative to competing scores, across major genetic ancestry groups these PRSs identify between 26 and 184 additional high risk individuals for every 1,000 people screened. We infer ancestry- specific high risk PRS thresholds and apply these to independent test datasets to identify between 12% and 24% of individuals who are at greater than twice the polygenic risk of CAD compared to the rest of the population. Using these PRSs to reclassify borderline or intermediate 10 year Atherosclerotic Cardiovascular Disease (ASCVD) risk in a cohort of 9,691 individuals improved the classification of those at increased risk of both CAD (Net Reclassification Improvement (NRI) = 13.14% (95%CI 9.23-17.06%)) and ASCVD (NRI = 10.70 (95%CI 7.35-14.05)). Our analyses demonstrate that using PRSs as Risk Enhancers can improve clinical 10 year ASCVD risk assessments and provide an approach for utilizing polygenic information to guide ASCVD prevention efforts.

10506 Background: Breast cancer (BC) is the second leading cause of cancer death in women worldwide. Periodic mammography screening has been shown to reduce breast cancer mortality by around 20% in average-risk women and several BC risk models are currently used to identify women at higher risk who can be targeted with increased or earlier screening. However, despite their broad use, these models display only moderate discrimination performance (AUC ranging between 0.51 to 0.68). Here we explore the potential of integrating PRS into a BC risk model using a testing dataset comprising over 175,000 women of diverse ancestry in the UK Biobank and Women Health’s Initiative Cohorts. Methods: We built and validated novel ancestry-specific BC PRSs utilizing novel methodology that leverage multiple Genome Wide Association Studies (GWASs) and linkage disequilibrium maps. We assessed genetic ancestry from 5 continental level ancestry groups across all individuals and applied the PRSs to our testing dataset to assess the clinical implications of integrating genetic information to the Tyrer-Cuzick (TC) breast cancer clinical risk model. Results: The PRSs were well calibrated and displayed high risk stratification (Odds Ratios per Standard Deviation: 1.41 (1.28-1.62) to 1.76 (1.40-2.20; Table) which were comparable or better than benchmarking comparisons with previously published PRS panels. Across genetic ancestries, integrating PRS led to a relative increase of women identified at high lifetime BC risk (20% or higher) from between 1.7 fold (East-Asian ancestry) to around 3 fold (European ancestry). The Net Reclassification Improvement compared to the TC-only model was around 12% for East-Asian (0.001-0.254), European (0.111-0.133), and Admixed American ancestries (0.002-0.228), 17% (0.079-0.266) for South-Asians, and 5% (0.008-0.094) for African ancestry individuals (Table). Conclusions: Our results demonstrate that optimizing PRSs for genetic ancestries and integrating them into BC risk models can lead to a significant improvement in risk stratification. This can enable more targeted use of enhanced screening and prevention strategies improving their cost/benefit ratio.[Table: see text]