Cardiac Medication Use in Patients with Acute Myocardial Infarction and Nonobstructive Coronary Artery Disease
Bibliographic record
Abstract
IMPORTANCE: Patients with acute myocardial infarction (MI) and nonobstructive coronary artery disease (CAD) have an elevated cardiac event rate, suggesting that these patients may benefit from cardiac medication. OBJECTIVE: We evaluated the rates of cardiac medication use 3 months before angiography and 3 months following clinically indicated angiography for MI in patients with no CAD, nonobstructive CAD, and obstructive CAD. We also examined the sex differences in cardiac medication use 3 months following angiography in patients by extent of angiographic CAD. METHODS: We studied patients ≥20 years old with MI undergoing coronary angiography in British Columbia, Canada, from January 1, 2008, to March 31, 2010 (n = 3,841). No CAD, nonobstructive CAD, and obstructive CAD were defined as 0%, 1% to 49%, and ≥50% luminal narrowing in any epicardial coronary artery, respectively. Medication use, 3 months before and 3 months following angiography, was obtained through British Columbia PharmaNet for angiotensin-converting enzyme inhibitors (ACE-Is), angiotensin receptor blockers (ARBs), calcium channel blockers (CCBs), beta-blockers, statins, and antiplatelet agents. Optimal medical therapy (OMT) was defined as filled prescriptions for all three: ACE-Is/ARBs, beta-blockers, and statins. RESULTS: Following angiography, in all medication categories except CCBs, patients with no CAD and nonobstructive CAD had significantly lower rates of prescriptions filled than patients with obstructive CAD (all p < 0.001). After adjusting for age and prior medication use, patients with nonobstructive CAD were still less likely to receive these medications than patients with obstructive CAD, including OMT with an odds ratio = 0.25 (95% confidence interval: 0.18-0.36). There were no significant sex differences in medication use 3 months postangiography. CONCLUSIONS: In post-MI patients, medication use following angiography is significantly lower in nonobstructive CAD than obstructive CAD at 3 months. While sex was not an independent predictor of medication use 3 months post-catheterization, future studies should explore methods of improving medication use in both females and males with nonobstructive CAD post-MI.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".