Abstract
BACKGROUND: Current guidelines for statin use in primary prevention of cardiovascular disease (CVD) predominantly rely on absolute 10-year CVD risk scores. However, this approach may not adequately capture heterogeneity in the potential benefit of low-density lipoprotein cholesterol (LDL-C) reduction. This study compares the absolute risk-based approach with an individualized benefit approach, based on the Causal-Benefit model considering predicted lipid-lowering effects, for statin eligibility in Chinese populations. METHODS AND FINDINGS: We analyzed nationally representative data from the China Health and Retirement Longitudinal Study, including adults aged 40-80 years, free of diabetes and CVD history, with LDL-C levels between 1.8 mmol/L and 4.9 mmol/L, and no prior statin use. Statin eligibility was determined using two strategies: (i) the absolute risk-based approach (10-year CVD risk), and (ii) the individualized benefit approach (using the Causal-Benefit model framework incorporating predicted individual absolute risk reduction [iARR]). We estimated eligible populations, CVD events averted, and number needed to treat (NNT) both at population and individual level (iNNT) over 10 years versus no treatment, assessed discordance, and primarily calibrated the benefit threshold to match event prevention by the risk-based approach for comparison. A total of 7,287 adults were analyzed, forming a cohort reflective of 324.6 million Chinese adults (mean age 57 years; 51.7% women). To prevent a similar number of CVD events (2.19 million vs. 2.16 million), 49.2 million (95% confidence interval [CI]: 45.3,53.0) and 50.3 million (95% CI: 46.0,54.6) adults would be eligible for statins therapy under the individualized benefit and absolute risk-based approaches, respectively. Among 58.9 million adults eligible for either strategy, the concordance was only 68.9%. The benefit approach alone identified 8.6 million people highly benefit from statin therapy, who would not be eligible for statin therapy under the absolute risk-based approach, and this includes 1.3 million people with borderline risk (5% to 7.5%). Conversely, the risk-based approach selected more individuals with low predicted benefit (minimum iARR: 2.5% vs. 3.4%), resulting in a less efficient individual-level targeting profile (maximum iNNT: 41 vs. 29). A key limitation of this study is that benefit was estimated primary from LDL-C reduction, which may neglect other biological mechanisms of statin effects and underestimate the total benefit. CONCLUSIONS: The individualized benefit approach prioritizes individuals most likely to benefit from statin therapy, differing from conventional risk-based selection through its superior individual-level precision. This approach can enhance the capacity to discriminate treatment effects at the individual level, making it particularly valuable for shared decision-making in resource-constrained settings.