Vericiguat for heart failure with reduced ejection fraction (HFrEF): A review of its potential benefits in Pakistan
Bibliographic record
Abstract
Heart failure (HF) is categorized by left ventricular ejection fraction (LVEF) into three groups. HF with reduced ejection fraction (HFrEF) is one of these groups characterized by the heart’s inability to pump sufficient blood to meet the body’s needs, resulting from the left ventricle’s impaired ability to contract effectively. The Canadian Cardiovascular Society (CCS) guidelines recommend vericiguat for hospitalized patients experiencing worsening symptoms of HFrEF. This article reviews vericiguat’s efficacy and potential benefits in Pakistani patients with HFrEF. A literature search from 2013 to 2024 was conducted using PubMed, ScienceDirect, and Google Scholar, employing keywords such as guidelines, heart failure, Pakistan, and reduced ejection fraction. Soluble guanylate cyclase (sGC) stimulators, like vericiguat, have shown benefits in patients with left ventricular hypertrophy and fibrosis by reducing afterload through vasodilation. Vericiguat (2.5 – 10 mg taken orally once daily) shows promise in reducing cardiovascular mortality and hospitalization in adults with LVEF ≤45%. Vericiguat may alleviate Pakistan’s growing cardiovascular disease burden. Expedited access to this innovative therapy can be achieved through collaborative efforts among policymakers, healthcare authorities, and international research centers, potentially reducing hospitalization rates in Pakistani HFrEF patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".