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.

Keywords: coronary artery disease; cost‐effectiveness; polygenic risk score.

Figure 1. Model structure.

The Markov model structure used in this study is shown with a total of 18 health states. The initial cohort was distributed in 2 groups: high‐risk cohort and nonhigh‐risk cohort. We defined high risk as individuals in the top quintile of the polygenic risk score (PRS) for coronary artery disease (CAD‐PRS) distribution or having other risk‐enhancing factor (eg, family history), while the nonhigh‐risk group included individuals in the bottom 80% of the CAD‐PRS distribution without any risk‐enhancing factor. In the pooled cohort equation (PCE)+CAD‐PRS strategy, all of the high‐risk cohort was initiated on statin preventive therapy to reduce the risk of coronary artery disease (CAD) and stroke, while for the PCE‐alone strategy only a proportion of patients with other risk‐enhancing factors initiated statins. We accounted for statin side effects such as diabetes, myopathy, and hemorrhagic stroke and subsequent risk of ischemic stroke and CAD. In the PCE‐alone strategy, CAD‐PRS was not considered as a risk‐enhancing factor, so only those with other risk‐enhancing factors initiated statins. Health outcomes were not examined for the nonhigh‐risk cohort.

Figure 2. Life expectancy in the US general population compared with the event‐free cohort in the model.

Projected life expectancy of the event‐free cohort in the model with the mean ages of 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, and 90 years. The projected years of survival were comparable to the life expectancy of Americans with the same age.

Figure 3. Life expectancy postmyocardial infarction.

Life expectancy of the cohort after acute myocardial infarction (MI) estimated from the model compared with data from the literature., We found the life expectancy generated by the model to be within 95% CIs of the life‐expectancy values from the literature.

Figure 4. The 5‐year incremental net monetary benefit of pooled cohort equation (PCE) alone vs PCE+polygenic risk score for coronary artery disease (CAD‐PRS), with PCE+CAD‐PRS preferred across all parameter value variations except when the annual risk of developing CAD was <0.006.

CAD indicates coronary artery disease; EV, expected value; and PRS, polygenic risk score.

Figure 5. The 10‐year incremental net monetary benefit of pooled cohort equation (PCE) alone vs PCE+polygenic risk score for coronary artery disease (CAD‐PRS), with PCE+CAD‐PRS preferred across all parameter value variations.

Figures 4 and 5 show the incremental net monetary benefit of pooled cohort equation (PCE) alone vs PCE+polygenic risk score for coronary artery disease (CAD‐PRS), with PCE+CAD‐PRS preferred across all parameter value variations except when the annual risk of developing CAD was <0.006 in the 5‐year time horizon.

Figure 6. Cost‐effectiveness acceptability curves.

Results from the probabilistic sensitivity analysis indicating the probability of the pooled cohort equation (PCE)+polygenic risk score for coronary artery disease (CAD‐PRS) being cost‐effective at different willingness‐to‐pay (WTP) thresholds. Compared with PCE alone, PCE+CAD‐PRS is likely to be cost‐effective with a probability of 94% and 99% at $50 000 WTP threshold and 98% and 99% at $100 000 WTP threshold in 5‐ and 10‐year time horizons, respectively.

Figure 7. Expected value of perfect information (EVPI) at different willingness‐to‐pay (WTP) thresholds.

Population EVPI of ≈$64.3 M and $3.53 M at a $50 000 WTP threshold in the 5‐ and 10‐year time horizons. EVPI decreased with increase in WTP thresholds suggesting high certainty in the cost‐effectiveness analysis results.