<scp>Angiotensin–neprilysin</scp> inhibition and renal outcomes across the spectrum of ejection fraction in heart failure
Bibliographic record
Abstract
Abstract Aims Patients with heart failure are at higher risk of progression to end‐stage renal disease (ESRD), regardless of ejection fraction (EF). We assessed the renal effects of angiotensin–neprilysin inhibition in a pooled analysis of 13 195 patients with heart failure with reduced and preserved EF. Methods and results We combined data from PARADIGM‐HF (EF ≤40%; n = 8399) and PARAGON‐HF (EF ≥45%; n = 4796) in a pre‐specified pooled analysis. We assessed the effect of treatment (sacubitril/valsartan vs. enalapril or valsartan) on a composite of either ≥50% reduction in estimated glomerular filtration rate (eGFR), ESRD, or death from renal causes, in addition to changes in eGFR slope. We assessed whether baseline renal function or EF modified the effect of therapy on renal outcomes. At randomization, eGFR was 68 ± 20 ml/min/1.73 m 2 in PARADIGM‐HF and 63 ± 19 ml/min/1.73 m 2 in PARAGON‐HF. The composite renal outcome occurred in 70 of 6594 patients (1.1%) in the sacubitril/valsartan group and in 123 of 6601 patients (1.9%) in the valsartan or enalapril group (hazard ratio 0.56, 95% confidence interval [CI] 0.42–0.75; p < 0.001). The mean eGFR change was −1.8 (95% CI −1.9 to −1.7) ml/min/1.73 m 2 /year for the sacubitril/valsartan group, compared with −2.4 (95% CI −2.5 to −2.2) ml/min/1.73 m 2 /year for the valsartan or enalapril group. The treatment effect on the composite renal endpoint was not modified by categories of baseline eGFR ( p ‐interaction = 0.64), but was most pronounced in those with baseline EF between 30% and 60% ( p ‐interaction = 0.001). Conclusions In patients with heart failure, sacubitril/valsartan reduced the risk of serious adverse renal outcomes and slowed decline in eGFR, compared with valsartan or enalapril, independent of baseline renal function.
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 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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".