Integrating a Polygenic Risk Score for Coronary Artery Disease as a Risk‐Enhancing Factor in the Pooled Cohort Equation: A Cost‐Effectiveness Analysis Study

Abstract

Background Cardiovascular diseases are the leading cause of death in the United States, yet a significant proportion of adults at high risk remain undetected by standard screening practices. Polygenic risk score for coronary artery disease (CAD‐PRS) improves precision in determining the 10‐year risk of atherosclerotic cardiovascular disease but health benefits and health care costs associated with CAD‐PRS are unknown. We examined the cost‐effectiveness of including CAD‐PRS as a risk‐enhancing factor in the pooled cohort equation (PCE)—the standard of care for determining the risk of atherosclerotic cardiovascular disease—versus PCE alone. Methods and Results We applied a Markov model on a cohort of 40‐year‐old individuals with borderline or intermediate 10‐year risk (5% to <20%) for atherosclerotic cardiovascular disease to identify those in the top quintile of the CAD‐PRS distribution who are at high risk and eligible for statin prevention therapy. Health outcomes examined included coronary artery disease (CAD; ie, myocardial infarction) and ischemic stroke. The model projected medical costs (2019 US$) of screening for CAD, statin prevention therapy, treatment, and monitoring patients living with CAD or ischemic stroke and quality‐adjusted life‐years for PCE+CAD‐PRS versus PCE alone. Deterministic and probabilistic sensitivity analyses and scenario analyses were performed to examine uncertainty in parameter inputs. PCE+CAD‐PRS was dominant compared with PCE alone in the 5‐ and 10‐year time horizons. We found that, respectively, PCE+CAD‐PRS had 0.003 and 0.011 higher mean quality‐adjusted life‐years and $40 and $181 lower mean costs per person screened, with 29 and 50 fewer events of CAD and ischemic stroke in a cohort of 10 000 individuals compared with PCE alone. The risk of developing CAD, the effectiveness of statin prevention therapy, and the cost of treating CAD had the largest impact on the cost per quality‐adjusted life‐year gained. However, this cost remained below the $50 000 willingness‐to‐pay threshold except when the annual risk of developing CAD was <0.006 in the 5‐year time horizon. Results from Monte Carlo simulation indicated that PCE+CAD‐PRS would be cost‐effective. with the probability of 94% and 99% at $50 000 willingness‐to‐pay threshold in the 5‐ and 10‐year time horizon, respectively. Conclusions Implementing CAD‐PRS as a risk‐enhancing factor in the PCE to determine the risk of atherosclerotic cardiovascular disease reduced the mean cost per individual, improved quality‐adjusted life‐years, and averted future events of CAD and ischemic stroke when compared with PCE alone.

Reclassification of coronary artery disease risk using genetic risk score among subjects with borderline or intermediate clinical risk

“…It is noted that the performance of various PRSs are similar for CAD and other diseases [8] , [13] . Our results, together with a newly published study demonstrating improved quality-adjusted life-years by implementing PRS [14] , suggest PRS is a cost-effective risk-enhancing factor.…”

Integrating large scale genetic and clinical information to predict cases of heart failure

“…In the grid search for time windows t and embedding dimension d, cosine similarity for 430,579,185 pairs of codes among 29,346 unique codes were calculated for each time window and embedding dimension combination. Ten t time windows (1,2,7,10,14,20,30,40,50, and 60 days) and twelve d embedding dimensions (10,30,50,100,150,200,250,300,350,400,450, and 500) were evaluated. This results in a total of 120 concept-AUC calculated to evaluate the concept derived from NLP in EHR data that are clinically applicable.…”

“…Multiple studies have shown that using a PRSa weighted sum of genetic effects on certain diseases or traits across the human genomecan enhance disease prediction and further improve early prevention 6,18 . Multiple efforts have been made to summarize genetic and clinical information to identify high risk patients, but integrating high-dimensional genome-wide association study (GWAS) and electronic health record (EHR) in heart failure prediction models has not been previously evaluated [19][20][21] .…”

Secure secondary utilization system of genomic data using quantum secure cloud

“…Additionally, personal human genome information is useful for personalized medical treatment not only for monogenic diseases but also for polygenetic diseases, e.g., type two diabetes and cardiovascular artery diseases (CAD). Statin prevention therapy for individuals with high CAD genetic risk (polygenic risk score; PRS) was estimated to reduce the mean cost per individual and improve quality‐adjusted life years and averted future events of CAD 3 . The polygenic risk score is usually calculated by summing the multiplied values of the weight, i.e., the beta value from a genome-wide association study from an independent population matched CAD cohort study, and the genotype, e.g., 0, 1, or 2, in each individual.…”