Effects of Sacubitril/Valsartan vs Valsartan in De Novo vs Acute on Chronic HFpEF and HFmrEF
Bibliographic record
Abstract
Decompensated heart failure (HF) can be categorized as de novo or worsening of chronic HF. In PARAGLIDE-HF (Prospective comparison of ARNI with ARB Given following stabiLization In DEcompensated HFpEF), among patients with an ejection fraction >40% that stabilized after worsening HF, sacubitril/valsartan led to a significantly greater reduction in N-terminal pro-B-type natriuretic peptide (NT-proBNP) and was associated with clinical benefit compared to valsartan. This prespecified analysis characterized patients with de novo vs worsening chronic HF in PARAGLIDE-HF and assessed the interaction between HF chronicity and the effect of sacubitril/valsartan. Patients were classified as de novo (first diagnosis of HF) or chronic (known HF prior to the index event). Time-averaged proportional change in NT-proBNP from baseline to weeks 4 and 8 was analyzed using an analysis of covariance model. A win ratio consisting of time to cardiovascular death, number and times of HF hospitalizations during follow-up, number and times of urgent HF visits during follow-up, and time-averaged proportional change in NT-proBNP was assessed for each group. Of the 466 participants, 153 (33%) had de novo HF and 313 (67%) had chronic HF. De novo patients had lower rates of atrial fibrillation/flutter and lower creatinine. There was a nonsignificant reduction in NT-proBNP with sacubitril/valsartan vs valsartan for de novo (0.82; 95% CI: 0.62-1.07) and chronic HF (0.88; 95% CI: 0.73-1.07), interaction P = 0.66. The win ratio was nominally in favor of sacubitril/valsartan for both de novo (1.12; 95% CI: 0.70-1.58) and chronic HF (1.24; 95% CI: 0.89-1.71). There is no interaction between HF chronicity and the effect of sacubitril-valsartan.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".