Enalapril Decreases the Incidence of Atrial Fibrillation in Patients With Left Ventricular Dysfunction
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) is frequently encountered in patients with heart failure (HF) and is also a predictor of morbidity and mortality in this population. Recent experimental studies have shown electrical and structural atrial remodeling with increased fibrosis in animals with HF and have suggested a preventive effect of ACE inhibitors (ACEi) on the development of AF. To verify the hypothesis that ACEi prevent the development of AF in patients with HF, we conducted a retrospective analysis of the patients from the Montreal Heart Institute (MHI) included in the Studies Of Left Ventricular Dysfunction (SOLVD). METHODS AND RESULTS: Clinical charts were reviewed and serial ECGs interpreted by a single cardiologist blinded to drug allocation. Patients with AF or flutter on the baseline ECG were excluded. Baseline characteristics were obtained from the SOLVD databases. The mean follow-up was 2.9+/-1.0 years. Of the 391 patients randomly assigned at MHI, 374 were in sinus rhythm at the time of random assignment, with 186 taking enalapril and 188 taking placebo. Baseline characteristics were similar in the two groups except for a higher incidence of previous myocardial infarction in the enalapril group. Fifty-five patients had AF during the follow-up: 10 (5.4%) in the enalapril group and 45 (24%) in the placebo group (P<0.0001). By Cox multivariate analysis, enalapril was the most powerful predictor for risk reduction of AF (hazard ratio, 0.22; 95% CI, 0.11 to 0.44; P<0.0001). CONCLUSIONS: Treatment with the ACEi enalapril markedly reduces the risk of development of atrial fibrillation in patients with left ventricular dysfunction.
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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) | 0.001 | 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) | 0.001 | 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".