Abstract P308: Individualized Statin Benefit for Determining Statin Eligibility in the Primary Prevention of Cardiovascular Disease
Bibliographic record
Abstract
Background: Current guidelines recommend statins in primary prevention of cardiovascular disease based on predicted cardiovascular risk without directly considering the expected benefits of statin therapy based on the available randomized trial (RCT) evidence. Methods and Results: We included 2,134 participants representing 71.8 million American residents potentially eligible for statins in primary prevention from the National Health and Nutrition Examination Survey for years 2005 - 2010. We compared statin eligibilities using three separate approaches: a 10-year risk-based approach, a trials-based approach (i.e. based on inclusion criteria of statin RCTs) and an individualized benefit approach (i.e. based on a predicted absolute risk reduction over 10 years [ARR 10 ] ≥2.5% using RCT data). A risk-based, a trials-based approach or a benefit-based approach led to the eligibility of (in millions of Americans): 15.0 (95% confidence interval 13.3-16.8), 24.7 (22.4-27.0) and 24.0 (21.2-26.7), respectively. The corresponding number needed to treat over 10 years was 19 (range: 9-40), 33 (9-1000) and 23 (9-40). A benefit-based approach identified 8.9 million lower-risk (<7.5% 10 year risk) Americans, not currently eligible for statin treatment, who had the same or greater expected benefit from statins (≥2.5% ARR 10 ) as higher-risk individuals. This lower-risk/acceptable-benefit group includes younger individuals (mean age 55.3 years vs. 62.5 years;p<0.001 for benefit-based vs risk-based) with higher LDL-C (140 mg/dL vs. 133 mg/dL; p=0.01). Conclusions: An individualized statin benefit approach can identify lower-risk individuals who have equal or greater expected benefit from statins in primary prevention than higher-risk individuals. This may help identify individuals who would meaningfully benefit from earlier initiation of statin therapy.
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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 | 0.030 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".