Upstream Therapy for Atrial Fibrillation Prevention: The Role of Sacubitril/Valsartan
Bibliographic record
Abstract
The therapy or prevention of atrial fibrillation (AF) is defined as upstream therapy when conducted with the use of drugs, e.g., angiotensin-converting enzyme inhibitors (ACEIs), angiotensin receptor antagonists, statins, and omega-3 fatty acids, not included in the classes of antiarrhythmic drugs recognized by the Vaughan Williams classification. In our review, we illustrate the rational bases of upstream AF therapy, which encompasses drugs having the property to reduce hemodynamic congestion and cardiac overload, as in the case of ACEIs or angiotensin receptor blockers, as well as drugs able to prevent atrial fibrosis or reduce oxidative stress, such as statins or omega-3 fatty acids, respectively. In this review, randomized controlled trials (RCTs) conducted with the abovementioned drugs are examined. Really, these RCTs have generated mixed results. In the context of the prevention and therapy of AF, our experience is then presented, relating to a patient with heart failure and reduced left ventricular ejection fraction, with a history of relapsing episodes of paroxysmal AF. In this patient, administration of sacubitril/valsartan at appropriate doses allowed recovery of the sinus rhythm. Therefore this case testifies how the upstream therapy of AF might have good results when conducted with sacubitril/valsartan. Thus, RCTs with adequate statistical power are warranted in order to confirm the preliminary encouraging result of our case report, and validate a useful role of sacubitril/valsartan as an upstream therapy of AF.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".