Number of patients needed to prescribe statins in primary cardiovascular prevention: mirage and reality
Bibliographic record
Abstract
Background: Number of patients needed to treat (NNT) with a statin in primary prevention of coronary heart disease (CHD) is often misinterpreted because this single statistic averages results from heterogeneous studies. Objective: To provide estimates of the number of individuals needed to be prescribed a statin to prevent one CHD event accounting for their level of CHD risk and for persistence to treatment. Methods: A post hoc analysis was conducted based on a Cochrane review on statins for the primary prevention of cardiovascular diseases. Five-year NNTs were calculated separately from randomized clinical trials (RCTs), including 'lower' and 'higher' risk populations (CHD mean event rates of 3.7 and 14.4 per 1000 person-years, respectively). NNTs were adjusted for 5-year persistence to treatment using a value of 65%. Results: Persistence-adjusted 5-year NNTs to prevent one CHD for the lower and higher CHD risk categories were 146 [95% confidence interval (CI): 117-211] and 53 (95% CI: 39-88) respectively, values 25% and 15% higher than their unadjusted counterpart (117, 95% CI: 94-167 and 46, 95% CI: 34-78). Conclusions: Five-year NNTs for statins to prevent a first CHD is almost three times higher in those at lower versus higher risk populations. Reporting combined results from RCTs including subjects at different cardiovascular risks should be avoided. Individualizing the risk of CHD should orient family physicians and their patients in their choice of preventive approaches and generate more realistic expectations about compliance and outcomes.
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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 | 0.067 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.015 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".