The Pathophysiology and New Advancements in the Pharmacologic and Exercise-Based Management of Heart Failure With Reduced Ejection Fraction: A Narrative Review
Bibliographic record
Abstract
Heart failure with reduced ejection fraction (HFrEF) is a clinical syndrome whose management has significantly evolved based on the pathophysiology and disease process. It is widely prevalent, has a relatively high mortality rate, and is comparatively more common in men than women. In HFrEF, the series of maladaptive processes that occur lead to an inability of the muscle of the left ventricle to pump blood efficiently and effectively, causing cardiac dysfunction. The neurohormonal and hemodynamic adaptations play a significant role in the advancement of the disease and are critical to guiding the treatment and management of HFrEF. The first-line therapy, which includes loop diuretics, β-blockers, angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers, hydralazine/isosorbide-dinitrate, and mineralocorticoid receptor antagonists (MRAs), has been proven to provide symptomatic relief and decrease mortality and complications. The newly recommended drugs for guideline-based therapy, angiotensin receptor/neprilysin inhibitor (ARNI), sodium-glucose cotransporter 2 inhibitors, soluble guanylate cyclase, and myosin activators and modulators have also been shown to improve cardiac function, reverse cardiac remodeling, and reduce mortality rates. Recent studies have demonstrated that exercise-based therapy has resulted in an improved quality of life, exercise capacity, cardiac function, and decreased hospital readmission rates, but it has not had a considerable reduction in mortality rates. Combining multiple therapies alongside holistic advances such as exercise therapy may provide synergistic benefits, ultimately leading to improved outcomes for patients with HFrEF. Although first-line treatment, novel pharmacologic management, and exercise-based therapy have been shown to improve prognosis, the existing literature suggests a need for further studies evaluating the long-term effects of MRA and ARNI.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".