Practice Patterns and Trends in the Use of Medical Therapy in Patients Undergoing Percutaneous Coronary Intervention in Ontario
Bibliographic record
Abstract
BACKGROUND: Clinical guidelines emphasize medical therapy as the initial approach to the management of patients with stable coronary artery disease (CAD). However, the extent to which medical therapy is applied before and after percutaneous coronary intervention (PCI) in contemporary clinical practice is uncertain. We evaluated medication use for patients with stable CAD undergoing PCI, and assessed whether the COURAGE study altered medication use in the Canadian healthcare system. METHODS AND RESULTS: A population-based cohort of 23 680 older patients >65 years old) with stable CAD undergoing PCI in Ontario between 2003 and 2010 was assembled. Optimal medical therapy (OMT) was defined as prescription for a β-blocker, statin, and either angiotensin-converting enzyme inhibitor or angiotensin II receptor blocker in the 90 days before PCI, and the same medications plus thienopyridine 90 days following PCI. Prior to PCI, 8023 (33.9%) patients were receiving OMT, 11 891 (50.2%) were on suboptimal therapy, and 3766 (15.9%) were not prescribed any medications of interest. There was significant improvement in medical therapy following PCI (OMT: 11 149 [47.1%], suboptimal therapy: 11 591 [48.9%], and none: 940 [4.0%], P<0.001). Utilization rate of OMT reduced significantly after the publication of COURAGE (34.9% before versus 32.8% after, P<0.001). Similarly, the rate of OMT following PCI was lower in the period after publication of COURAGE (47.3% before versus 46.9% after, P<0.001). CONCLUSIONS: OMT was prescribed in about 1 in 3 patients prior to PCI and less than half after PCI. In contrast to the anticipated impact of COURAGE, we found lower rates of medication use in PCI patients after its publication.
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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 | 0.003 | 0.002 |
| 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 | 0.001 |
| 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".