Effect of sacubitril/valsartan vs. enalapril on changes in heart failure therapies over time: the <scp>PARADIGM‐HF</scp> trial
Bibliographic record
Abstract
AIMS: Sacubitril/valsartan improves morbidity and mortality in patients with heart failure and reduced ejection fraction (HFrEF). Whether initiation of sacubitril/valsartan limits the use and dosing of other elements of guideline-directed medical therapy for HFrEF is unknown. We examined the effects of sacubitril/valsartan, compared with enalapril, on β-blocker and mineralocorticoid receptor antagonist (MRA) use and dosing in a large randomized clinical trial. METHODS AND RESULTS: Patients with full data on medication use were included. We examined β-blocker and MRA use in patients randomized to sacubitril/valsartan vs. enalapril through 12-month follow-up. New initiations and discontinuations of β-blocker and MRA were compared between treatment groups. Overall, 8398 (99.9%) had full medication and dose data at baseline. Baseline use of β-blocker and MRA at any dose was 87% and 56%, respectively. Mean doses of β-blocker and MRA were similar between treatment groups at baseline and at 6-month and 12-month follow-up. New initiations through 12-month follow-up were infrequent and similar in the sacubitril/valsartan and enalapril groups for β-blockers [37 (9.0%) vs. 42 (10.2%), P = 0.56] and MRA [127 (7.6%) vs. 143 (9.2%), P = 0.10]. Among patients on MRA therapy at baseline, there were fewer MRA discontinuations in patients on sacubitril/valsartan as compared with enalapril at 12 months [125 (6.2%) vs. 187 (9.0%), P = 0.001]. Discontinuations of β-blockers were not significantly different between groups in follow-up (2.2% vs. 2.6%, P = 0.26). CONCLUSIONS: Initiation of sacubitril/valsartan, even when titrated to target dose, did not appear to lead to greater discontinuation or dose down-titration of other key guideline-directed medical therapies, and was associated with fewer discontinuations of MRA. Use of sacubitril/valsartan (when compared with enalapril) may promote sustained MRA use in follow-up.
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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) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".