Sacubitril/Valsartan Versus Valsartan Initiation in Patients Naïve to Renin–angiotensin System Inhibitors: Insights from PARAGLIDE-HF
Bibliographic record
Abstract
Abstract Aims Whether prior treatment with angiotensin-converting enzyme inhibitors (ACEi) or angiotensin receptor blockers (ARBs) modifies efficacy and safety of sacubitril/valsartan (Sac/Val) in patients with heart failure (HF) and ejection fraction (EF) >40% is unclear, thus Sac/Val according to ACEi/ARB status at baseline was assessed. Methods and results This was a pre-specified analysis of Prospective comparison of ARNI with ARB Given following stabiLization In DEcompensated HFpEF (PARAGLIDE-HF), a double-blind, randomized controlled trial of Sac/Val versus valsartan, categorizing patients according to baseline ACEi/ARB status. The primary endpoint was time-averaged proportional change in N-terminal pro-B-type natriuretic peptide (NT-proBNP) from baseline through weeks 4 and 8. Secondary analyses included a win-ratio analysis of the hierarchical outcome of (i) cardiovascular death; (ii) HF hospitalizations; (iii) urgent HF visits; and (iv) time-averaged proportional change in NT-proBNP from baseline to weeks 4 and 8, in addition to safety outcomes. Among 466 patients, 107 (23%) were ACEi/ARB naïve at the time of randomization. NT-proBNP favoured Sac/Val irrespective of ACEi/ARB status (naïve: 0.76, 95% confidence interval [CI] 0.51–1.13; users: 0.88, 95% CI 0.74–1.05; pinteraction = 0.52). The win ratio of the hierarchical outcome was 1.13 (95% CI 0.86–1.49) for ACEi/ARB users and 1.38 (95% CI 0.81–2.37) for ACEi/ARB naïve (pinteraction = 0.51). Safety endpoints showed non-significant interactions by ACEi/ARB status, with odds ratio of 1.79 (95% CI 0.68–4.72) and 0.71 (95% CI 0.29–1.78) for symptomatic hypotension and worsening renal function, respectively for ACEi/ARB naïve. Conclusion In HF with EF >40% stabilized after worsening HF, safety and efficacy were similar irrespective of ACEi/ARB status at baseline, supporting early initiation irrespective of prior ACEi/ARB use.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".