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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".