In-Hospital Initiation of Lipid-Lowering Therapy After Coronary Intervention as a Predictor of Long-term Utilization
Bibliographic record
Abstract
BACKGROUND: Despite multiple randomized trials demonstrating their efficacy for the secondary prevention of coronary disease, lipid-lowering agents remain underused. Few studies have examined the relationship between predischarge initiation of lipid-lowering therapy and long-term use. METHODS: Using data from patients at 69 centers from the United States and Canada enrolled in the Evaluation in PTCA to Improve Long-term Outcome With Abciximab GP IIb/IIIa Blockade (EPILOG) trial, we performed a retrospective propensity-analyzed cohort study. Patients underwent percutaneous coronary intervention for stable or recently unstable coronary disease and were older than 21 years, were not taking lipid-lowering therapy at the time of admission, and survived to hospital discharge; 175 were discharged taking lipid-lowering therapy and 1951 were not. RESULTS: After 6 months, 77% of patients who started taking lipid-lowering agents before hospital discharge continued taking therapy, compared with only 25% of those discharged without these agents (relative risk, 3.17; 95% confidence interval, 2.88-3.41; P<.001). After restricting the analysis to propensity-matched patients (n = 477) and adjusting for other potential confounders, initiation of a lipid-lowering agent during hospitalization was the strongest independent predictor of use at 6 months (relative risk, 2.50; 95% confidence interval, 2.29-2.65; P<.001). CONCLUSIONS: Inpatient initiation of lipid-lowering therapy is a strong and independent positive predictor of subsequent use, with patients who start taking lipid-lowering therapy before hospital discharge nearly 3 times as likely to be taking these agents 6 months later. Inpatient initiation of lipid-lowering therapy appears to be an effective strategy for bridging the gap between current medical knowledge and practice.
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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 | 0.000 | 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".