Enalapril prevents clinical proteinuria in diabetic patients with low ejection fraction.
Bibliographic record
Abstract
OBJECTIVE: Clinical proteinuria is a risk factor for both end-stage renal disease and cardiovascular disease. The prevalence of clinical proteinuria, its correlates and predictive value, and the effect of ACE inhibitors in preventing clinical proteinuria in diabetic and nondiabetic patients with left ventricular (LV) dysfunction are unknown. RESEARCH DESIGN AND METHODS: The Studies of Left Ventricular Dysfunction (SOLVD) trials were analyzed to determine the baseline distribution of clinical proteinuria and related cardiovascular risk factors, the effect of baseline proteinuria on the risk of hospitalization for congestive heart failure (CHF) and mortality, and the effect of enalapril in preventing new clinical proteinuria. RESULTS: A total of 5,487 out of 6,797 SOLVD participants (81%) were assessed for proteinuria at baseline. A total of 177 patients (3.2%) had baseline proteinuria. These patients had significantly higher systolic (137 vs. 125 mmHg, P < or = 0.001) and diastolic (83 vs. 77 mmHg, P < or = 0.001) blood pressure levels, a higher prevalence of diabetes (41 vs. 18%, P < or = 0.001), a lower ejection fraction (26.2 vs. 27.3%, P < or = 0.05), and greater degree of CHF (New York Heart Association [NYHA] class III/IV in 22 vs. 10%, P < or = 0.001) than patients without baseline proteinuria. Patients with baseline proteinuria also had higher rates of hospitalization for CHF (relative risk 1.81 [95% CI 1.37-2.41], P = 0.0001) and mortality (1.73 [1.34-2.24], P = 0.0001). Enalapril prevented clinical proteinuria in diabetic patients (0.38 [0.17-0.81], P = 0.0123) but not in nondiabetic patients (1.43 [0.77-2.63], P = 0.2622) without baseline proteinuria. CONCLUSIONS: Clinical proteinuria is an independent predictor of hospitalization for CHF and mortality in diabetic and nondiabetic patients with LV dysfunction. Enalapril significantly reduces the risk of clinical proteinuria in diabetic patients with LV 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 | 0.001 | 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".