Abstract P297: Long-Term Benefit Comparison of Absolute Risk Reduction versus Absolute Risk to Prioritize Statin Therapy
Bibliographic record
Abstract
Introduction: Individuals with no established cardiovascular disease (CVD) are currently recommended preventive statin therapy based on 10-year absolute risk (AR) of CVD, and individuals with a 10-year AR ≥7.5% are recommended statins. However, individuals with elevated LDL cholesterol experience greater absolute CVD absolute risk reduction (ARR) from statin therapy compared with those with the same 10-year AR but with lower LDL. A previous study showed that ARR-based statin treatment would prevent more CVD events than AR-based treatment in the 10 years following treatment initiation. Objective: This study aimed to quantify the long-term benefits of treating patients based on ARR rather than AR. Methods: A microsimulation version of the CVD Policy Model, a decision-analytic state transition model, simulated intermediate-strength statin therapy in 40,000 CVD-free US adults (50% female) under a variety of treatment strategies. The model predicts health outcomes for individuals based on their age, sex, and risk factor profile, accounting for the competing risk of non-CVD mortality. Individuals entered the model aged 40 years, and a time horizon of 40 years was employed. Life year gains and CVD events prevented compared to no treatment were estimated for a range of 10-year ARR and AR treatment initiation thresholds. Results: At the same numbers of patient-years of treatment (PYoT), ARR consistently produced more life year gains than AR (Figure). A 10-year ARR threshold of ≥2.62% would lead to approximately the same PYoT as standard of care (10-year AR ≥7.5%) while preventing 60 additional CVD events and producing 421 additional life year gains in the cohort. Conclusion: Treating patients with statins based on ARR would yield significant health gains in the U.S. population compared to standard AR-based treatment strategies. The ARR strategy may also achieve greater adherence and uptake as it focuses on individuals with elevated levels of a modifiable risk factor.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".