Abstract 13767: Reaching the LDL-Cholesterol Target Recommended by the Guidelines: Be Ambitious to Get the Maximum Benefit
Bibliographic record
Abstract
Introduction: There is no agreement among international guidelines (GL) on the recommended therapeutic target for LDL cholesterol in secondary prevention (SP). Hypothesis: The aim of this study was to determine the risk of major adverse cardiovascular events (MACE) during follow-up in a real-world SP population according to the achievement of a purely numerical threshold (i.e. LDL<1.8mmol/L [Canadian 2021 GL], group A) or a target that also considers a relative reduction in LDL levels (i.e. LDL reduced ≥50% from baseline and <1.8mmol/L [AHA/ACC 2018 GL] or <1.4 mmol/L [ESC 2019 GL], group B). Methods: We conducted a retrospective analysis of a monocentric observational registry prospectively enrolling patients admitted for STEMI between 2011 and 2019. The combined endpoint of MACE included all-cause death, non-fatal MI, non-fatal stroke, and unplanned revascularization. The lowest LDL value collected between 1 and 12 months after the index event was used to define the achievement of the target. We conducted a Kaplan-Meier analysis comparing patients who achieved different GL-recommended targets. Results: A total of 1199 patients (23% female) were included. Median age was 63 (54-72) years, 56% had hypertension, 17% diabetes, and 38% were smoker. Baseline LDL was 3.2 (2.5-3.8) mmol/L; 83% of patients were treated with statin therapy alone, and 17% with the addition of ezetimibe. Median follow-up was 53 (32-70) months. The Canadian, AHA/ACC and ESC targets were achieved in 842 (70%), 506 (42%) and 308 (26%) patients respectively; MACE-free analysis based on the target achieved is presented in FigA. The net incidence of MACE was 29% in group A vs 15% in group B (HR 1.97; 95%CI 1.36-2.85; P log-rank=0.0003; NNT=7; FigB). Conclusions: Our data from a real-world cohort of secondary prevention patients emphasize the importance of achieving a guideline-recommended target that also considers a relative reduction in LDL levels (i.e. LDL ≤50% from baseline) in order to reduce MACE.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".