Cost-Effectiveness of ApoB, Non–HDL-C, and LDL-C Goals for Primary Prevention Lipid-Lowering Therapy

Importance: Apolipoprotein B (apoB) is a superior marker of residual atherosclerotic cardiovascular disease risk in patients treated with lipid-lowering therapy (LLT) compared with low-density lipoprotein cholesterol (LDL-C) and non-high-density lipoprotein cholesterol (non-HDL-C). The cost-effectiveness of LDL-C, non-HDL-C, and apoB goals has not been established. Objective: To determine the relative cost-effectiveness of intensifying LLT for primary prevention based on LDL-C, non-HDL-C, and apoB goals. Design, Setting, and Participants: This economic evaluation used a computer simulation model to evaluate the cost-effectiveness of intensifying LLT with high-intensity statins or ezetimibe according to LDL-C, non-HDL-C, or apoB goals. A cohort of 250 000 statin-eligible and atherosclerotic cardiovascular disease-free US adults was constructed from 2005 to 2016 National Health and Nutrition Examination Survey participants (N = 4149). Individuals commenced the simulation after lipid screening and received statin therapy based on 2018 American Heart Association/American College of Cardiology guidelines. Model inputs were derived from national survey data, pooled longitudinal cohort studies, and published literature. Uncertainty was explored with traditional and probabilistic sensitivity analysis. Exposures: Lipid-lowering therapy was intensified if individuals did not achieve treated LDL-C level less than 100 mg/dL, non-HDL-C level less than 118 mg/dL, or apoB level less than 78.7 mg/dL. Main Outcomes and Measures: Lifetime quality-adjusted life-years (QALYs) and costs (in 2025 US dollars), discounted 3.0% annually. The primary outcome was the incremental cost-effectiveness ratio. Strategies were considered cost-effective if they cost less than $120 000 per QALY gained. Results: Compared with an LDL-C goal, 965 QALYs (95% uncertainty interval [UI], -3551 to 5341 QALYs) would be gained with a non-HDL-C goal, alongside a $2.1 million (95% UI, -$94.2 million to $92.0 million) reduction in costs. Compared with a non-HDL-C goal, 1324 QALYs (95% UI, -2602 to 5669 QALYs) would be gained with an apoB goal, alongside a $40.2 million (95% UI, -$43.6 million to $134 million) increase in costs, yielding an incremental cost-effectiveness ratio of $30 300 per QALY gained. At a willingness-to-pay threshold of $120 000 per QALY gained, an apoB goal was optimal in 65% of probabilistic analyses and a non-HDL-C goal was optimal in 25%. The cost of apoB testing was marginal; higher costs reflected longer life expectancy and prolonged preventive treatment. Conclusions and Relevance: The results of this computer simulation study suggest that apoB can be used as a cost-effective marker to guide primary prevention LLT and improve population health.