Plasma renin activity predicts cardiovascular mortality in the Heart Outcomes Prevention Evaluation (HOPE) study
Bibliographic record
Abstract
AIMS: Plasma renin activity (PRA) has been proposed as an independent predictor of cardiovascular (CV) risk, but there are limited data from large prospective studies, particularly in patients with stable vascular disease and/or diabetes, without heart failure. METHODS AND RESULTS: We evaluated the predictive value of PRA as a marker of CV events and mortality in a large population of patients with stable chronic vascular disease and/or diabetes and one CV risk factor. Baseline PRA was measured in 2913 patients enrolled in the Heart Outcomes Prevention Evaluation (HOPE) study. Subjects were followed for a median of 4.5 years. Compared with the referent lowest fifth, subjects in the highest fifth of the PRA distribution had a hazard ratio (HR) of 1.38 (95% confidence interval, 1.03-1.86; P = 0.03) for the composite of major vascular events, with an HR of 1.89 for CV death. These associations remained statistically significant after full adjustment for clinical characteristics, background use of β-blockers, diuretics, allocation to ramipril, in addition to inflammatory biomarkers, high-sensitivity C-reactive protein, and N-terminal pro-brain natriuretic peptide. CONCLUSION: High PRA is an independent predictor of major vascular events and mortality in a stable population of high-risk patients with atherosclerosis and/or diabetes. Although an increase in PRA could be a marker of more intense antihypertensive therapy, our results suggest that PRA may represent a risk marker and potential target for therapy in high-risk patients with atherosclerosis and/or diabetes.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".