Enalapril Reduces the Incidence of Diabetes in Patients With Chronic Heart Failure
Bibliographic record
Abstract
BACKGROUND: Diabetes mellitus is a predictor of morbidity and mortality in patients with heart failure. The effect of angiotensin-converting enzyme (ACE) inhibitors on the prevention of diabetes in patients with left ventricular dysfunction is unknown. The aim of this retrospective study was to assess the effect of the ACE inhibitor enalapril on the incidence of diabetes in the group of patients from the Montreal Heart Institute enrolled in the Studies of Left Ventricular Dysfunction (SOLVD). METHODS AND RESULTS: Clinical charts were evaluated for fasting plasma glucose (FPG) levels by blinded reviewers. A diagnosis of diabetes was made when a FPG > or =126 mg/dL (7 mmol/L) was found at 2 visits (follow-up, 2.9+/-1.0 years). Of the 391 patients enrolled at the Montreal Heart Institute, 291 were not diabetic (FPG <126 mg/dL without a history of diabetes), 153 of these were on enalapril and 138 were on placebo. Baseline characteristics were similar in the 2 groups. Forty patients developed diabetes during follow-up, 9 (5.9%) in the enalapril group and 31 (22.4%) in the placebo group (P<0.0001). By multivariate analysis, enalapril remained the most powerful predictor for risk reduction of developing diabetes (hazard ratio, 0.22; 95% confidence intervals, 0.10 to 0.46; P<0.0001). The effect of enalapril was striking in the subgroup of patients with impaired FPG (110 mg/dL [6.1 mmol/L] < or =FPG <126 mg/dL) at baseline: 1 patient (3.3%) in the enalapril group versus 12 (48.0%) in the placebo group developed diabetes (P<0.0001). CONCLUSIONS: Enalapril significantly reduces the incidence of diabetes in patients with left ventricular dysfunction, especially those with impaired FPG.
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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 | 0.001 | 0.003 |
| 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".