Practical guidance on the use of sacubitril/valsartan for heart failure
Bibliographic record
Abstract
Sacubitril/valsartan is a first-in-class angiotensin receptor-neprilysin inhibitor (ARNI) that has been recommended in clinical practice guidelines to reduce morbidity and mortality in patients with chronic, symptomatic heart failure (HF) with reduced ejection fraction (HFrEF). This review provides an overview of ARNI therapy, proposes strategies to improve the implementation of sacubitril/valsartan in clinical practice, and provides clinicians with evidence-based, practical guidance on the use of sacubitril/valsartan in patients with HFrEF. Despite evidence demonstrating the benefits of ARNI therapy over standard of care, only a fraction of eligible patients takes sacubitril/valsartan. Barriers preventing the prescription of sacubitril/valsartan in eligible patients may include practitioners' unfamiliarity with ARNIs, safety concerns, and payer reimbursement issues. The optimal implementation of sacubitril/valsartan in clinical practice has the potential to reduce the overall burden of HF. Throughout this review, we describe our experience with sacubitril/valsartan, including strategies for the management of adverse events and common patient concerns. In addition, a strategy for the gradual introduction of sacubitril/valsartan using a treatment sequence scheme is proposed.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".