Comparison of Preventive Cardiovascular Pharmacotherapy in Surgical vs Percutaneous Coronary Revascularization
Bibliographic record
Abstract
BackgroundData suggest that patients who undergo coronary artery bypass grafting (CABG) have a lower rate of secondary preventive cardiovascular pharmacotherapy use compared with patients who undergo percutaneous coronary intervention (PCI). This study sought to assess the rate of use of preventive pharmacotherapy at discharge in patients who underwent CABG vs PCI post–acute coronary syndrome (ACS).MethodsA prospective cohort study was conducted at St Paul’s Hospital in Vancouver, Canada. Patients aged ≥ 18 years who presented with an ACS and underwent CABG or PCI between January and November 2018 were included. Data on preventive pharmacotherapy use and reasons for justified nonuse (eg, intolerance, contraindication) were collected.ResultsA total of 275 patients were included. Mean age was 65 years, and 83% were male. Overall, 141 patients (51%) underwent CABG and 134 patients (49%) underwent PCI. All patients received acetylsalicylic acid, but more patients who underwent CABG received 325 mg (vs 80-81 mg) compared to PCI (25% vs 1%, P < 0.01). Use of P2Y12 inhibitors was higher in patients who underwent PCI (primarily ticagrelor) compared with patients who underwent CABG (primarily clopidogrel) (99% vs 26%, P < 0.01). All patients who underwent CABG received a β-blocker vs 96% of patients who underwent PCI (P = 0.017). Use of angiotensin-modulating agents was higher in patients who underwent PCI (98% vs 65%, P < 0.01). Statin use was similar between groups (99% vs 99%, P = 0.96), but more patients who underwent PCI received maximum-dose therapy (89% vs 64%, P < 0.01).ConclusionsUse of acetylsalicylic acid, β-blockers, and statins in patients post-ACS was high regardless of revascularization strategy, whereas P2Y12 inhibitors and angiotensin-modulating agents were underused in patients who underwent CABG even after adjusting for justified nonuse.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".