Purpose: To conceptualize a personalized decision aid for calculating lifetime outcomes of statin treatment for prevention of coronary artery disease (CAD).
Method: A previously developed and validated Monte Carlo-Markov model based on the Rotterdam Study, a cohort study of 6871 individuals aged 55 years and older with 7 years follow-up, was used. Life courses of 3501 participants with complete risk profiles on statin treatment vs. not-on-statin treatment were simulated using six health states (well, CAD, stroke, both CAD and stroke, cardiovascular death and non-cardiovascular death). Transitions between health states were based on multivariable Cox regression equations. Trends in risk factor levels were modeled by 5-yearly updating of the Cox regression equations using cohort study data. The effect of statin treatment was individualized by applying hazard rate ratios matching the expected absolute reduction in LDL-cholesterol level. Linear mixed models were used to predict lifetime CAD risk, life years, CAD-free life years, and QALYs for the two scenarios in 2357 subjects younger than 80 years without cardiovascular disease and diabetes at baseline. Equations were programmed in a user-friendly interface with Microsoft Excel. The Excel-based decision aid required information on age, sex, smoking, pulse pressure, BMI, waist to hip ratio, family history of myocardial infarction, ankle-brachial index, cholesterol-HDL ratio, glucose level, and use of antihypertensives to calculate outcomes both with and without statin treatment. The lifetime benefit of statin treatment for different age and sex groups was compared to 10-year CAD risk predicted by the Framingham risk score (FRS), which is currently used to decide on statin treatment.
Result: In the 2357 subjects (mean age 66 ± 6.6, 37% men), statin treatment resulted in an average decrease in lifetime CAD risk of 3.2%, and an average gain of 0.84 ± 0.59 life years, 0.58 ± 0.42 QALYs, and 1.08 ± 0.72 CAD-free life years. The benefit of statin treatment increased with FRS 10-year CAD risk, but was negatively associated with age and male sex. For example, women at intermediate 10-year CAD risk (10 -19%) achieved a similar gain in QALYs with statin treatment as men at high 10-year CAD risk (≥20%).
Conclusion: A personalized decision aid for calculating lifetime outcomes could have added value beyond traditional risk scores for initiating statin treatment.
Candidate for the Lee B. Lusted Student Prize Competition
See more of: The 32nd Annual Meeting of the Society for Medical Decision Making