Longitudinal Control of Lipid Levels in Patients With Premature Coronary Artery Disease
Bibliographic record
Abstract
Background: Lipid-lowering therapy (LLT) is a central aspect of the treatment of patients with coronary artery disease (CAD), and the benefits of LLT accrue over time. However, there are limited real-world data on longitudinal lipid control in patients with premature CAD. Objectives: The purpose of this study was to assess longitudinal attainment of guideline-recommended lipid goals and outcomes in a contemporary cohort of patients with premature CAD. Methods: We enrolled males younger than 50 years and females younger than 55 years with coronary stenosis of >50% and examined achievement of lipid goals, LLT characteristics, and cardiovascular outcomes (major adverse cardiovascular event [MACE]). Results: Of 476 patients who presented with acute coronary syndrome (ST-elevation myocardial infarction, non-ST-segment elevation myocardial infarction, unstable angina) (68%), stable angina (28%), or other symptoms, 73.2% achieved low-density lipoprotein cholesterol (LDL-C) <1.8 mmol/L on at least 1 occasion, but only 27.3% consistently stayed in the target range for 3 years after diagnosis. Although 73.9% of patients received high-intensity LLT at the time of diagnosis, only 43.5% had good adherence over the following 3 years. In multivariable analysis, 1 mmol/L increase in time-weighted average exposure to LDL-C, but not the lowest achieved LDL-C, was associated with a higher risk of MACE, hazard ratio 2.02 (95% CI: 1.48-2.76), when adjusted for sex, age, hypertension, diabetes, and smoking. Conclusions: We found low rates of longitudinal lipid target achievement in patients with premature CAD. Cumulative LDL-C exposure, but not lowest achieved LDL-C, was associated with risk of MACE. This highlights the critical importance of longitudinal control of lipids levels and identifies opportunities to improve LLT and maximize the time-dependent benefits of lipid-lowering.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".