Use of Renin–Angiotensin System Blockers in Acute Coronary Syndromes
Bibliographic record
Abstract
BACKGROUND: Angiotensin-converting enzyme inhibitors (ACEI) or angiotensin receptor blockers (ARB) initiated after myocardial infarction (MI) reduce mortality and are American College of Cardiology/American Heart Association guideline recommended. Yet the extent to which ACEI/ARB therapy is applied in patients with acute coronary syndrome at hospital discharge is unclear. METHODS AND RESULTS: We performed an observational analysis of 80 241 patients admitted with an acute coronary syndrome and discharged home from 311 U.S. hospitals participating in the Get With the Guidelines-Coronary Artery Disease Program from January 2005 to December 2009. Among the 60,847 patients with an American College of Cardiology/American Heart Association class I indication (left ventricular dysfunction or medical history of heart failure, hypertension, diabetes mellitus, or chronic kidney disease), 49,682 (81.7%) received ACEI/ARB with an increase in the rate of treatment over the study period (76.7%-84.6%; adjusted odds ratio, 1.17; 95% confidence interval, 1.10-1.24; P<0.001, per calendar year). In-hospital coronary artery bypass grafting and renal insufficiency were independently associated with lower use (adjusted odds ratio, 0.55; 95% confidence interval, 0.48-0.63 and adjusted odds ratio, 0.58; 95% confidence interval, 0.52-0.64, respectively). CONCLUSIONS: Results from this large U.S. national registry suggest that 1 in 5 eligible patients hospitalized for acute coronary syndrome failed to receive American College of Cardiology/American Heart Association class I guideline-recommended ACEI/ARB therapy, and the use varies by patient factors. In particular, the low likelihood of ACEI/ARB after coronary artery bypass grafting surgery or in patients with renal insufficiency raises concern. These findings highlight an unmet need in this population and provide an incentive for additional quality improvement efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".