Abstract 344: Low Density Lipoprotein Cholesterol and Major Adverse Cardiovascular Events After Percutaneous Coronary Intervention
Bibliographic record
Abstract
Introduction: Lowering low-density lipoprotein cholesterol (LDL-C) reduces the risk of major adverse cardiovascular events (MACE). Few studies have examined LDL-C control and outcomes exclusively after percutaneous coronary intervention (PCI). Furthermore, guidelines provide no formal recommendation on when to check LDL-C after PCI. It is therefore conceivable that LDL-C is not routinely measured after PCI, many patients may have elevated LDL-C levels (≥ 70mg/dL), and that elevated LDL-C levels after PCI are associated with adverse long-term outcomes. Objective: To evaluate LDL-C levels after PCI procedures, and to assess the association between LDL-C and cardiovascular events in a population-based cohort. Methods: All patients who received their first PCI between Oct 2011 and Sep 2014 in Ontario, Canada, and had a cholesterol measurement within 6 months after PCI were included. Multivariable Fine and Gray sub-distribution hazards models were used to assess the association between LDL-C measured after PCI and the incidence of MACE (myocardial infarction, coronary revascularization, stroke and cardiovascular death) through December 31, 2016. Results: There were 47,884 patients who had their first PCI during the study period, and 52% had an LDL-C measurement within 6 months post-procedure (median age 63 years, 27% female). Among them, 57% had LDL-C < 70mg/dL, 28% had LDL-C 70 to < 100mg/dL, and 15% had LDL-C ≥ 100mg/dL. After a median of 3.2 years of follow-up, 19% of patients had a qualifying MACE. After adjustment, the incidence of MACE was significantly higher in patients with higher LDL-C levels (Figure). Conclusions: Only one in two patients had LDL-C measured within 6 months after undergoing PCI and only about half had LDL-C < 70mg/dL. Higher levels of LDL-C after PCI were associated with a significantly higher incidence of MACE. Recommendations for routine LDL-C assessment and optimization may improve patient outcomes after PCI procedures.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| 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".