# Absolute risk-based versus individualized benefit approaches for determining statin eligibility in primary prevention of cardiovascular diseases in Chinese populations: A modeling study

## TL;DR

This study compares two methods for deciding who should take statins in China, finding that an individualized benefit approach better targets those likely to benefit most from treatment.

## Contribution

The study introduces and evaluates an individualized benefit approach for statin eligibility using the Causal-Benefit model in a Chinese population.

## Key findings

- The individualized benefit approach identified 8.6 million people who would benefit from statins but were not eligible under the absolute risk-based method.
- The two approaches showed only 68.9% concordance in statin eligibility, with the benefit approach offering better individual-level targeting.
- The benefit approach could prevent 65% more cardiovascular events when calibrated to the same prevention level as the risk-based method.

## Abstract

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.

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.

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.

Current guidelines relying on 10-year absolute risk might recommend statins for some high-risk individuals who gain limited benefit specifically from LDL-C reduction, while potentially missing lower-risk individuals who stand to gain substantially more benefit from LDL-C lowering.

An alternative, the individualized benefit approach (conceptually based on the Causal-Benefit model), estimates benefit based on modifiable causal factors like LDL-C and might better identify individuals, including younger people with high LDL-C, who could derive substantial benefit.

The potential population-level implications of the individualized benefit approach needed quantification in settings like China, where non-lipid factors (e.g., hypertension) are key contributors to overall risk.

Using nationally representative Chinese data, we compared statin eligibility recommendations between the conventional absolute risk-based and individualized benefit approaches.

When calibrated to prevent a similar number of cardiovascular events, the groups recommended for treatment overlapped by only about 69%. The group selected by the individualized benefit approach contained a higher concentration of individuals predicted to receive substantial absolute benefit (iARR) from statins, suggesting this method better targets those likely to benefit most.

Applying the Causal-Benefit model’s minimum benefit threshold principle, the benefit approach recommended treatment for more adults (46 million additional) compared to the high-risk approach alone, increasing projected 10-year CVD events prevented by 65% (estimated 3.57 million vs. 2.16 million).

In resource-constrained settings, the individualized benefit approach may better target statins to those likely to benefit most, potentially avoiding overtreatment in some high-risk individuals and allowing earlier treatment for younger populations with high LDL-C.

A key study limitation is the primary focus on LDL-C reduction for benefit estimation, which neglects other biological mechanisms of statin effects that might underestimate the total benefit, especially for individuals deemed high-risk for non-lipid reasons. However, in settings with sufficient resources that allow for shared decision-making, using a minimum benefit threshold (as in the Causal-Benefit model framework) could identify a broader group that potentially benefits from statins through various mechanisms.

Qiuping Liu and her colleagues compare the conventional, absolute, risk-based approach with an individualized, benefit-based approach that considers the effects of lipid-lowering for statin eligibility in Chinese populations.