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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".