Is Carvedilol Effective in Preventing and Modulating Concentric Cardiac Remodelling? A Comprehensive Systematic Review and Meta‐Analyses
Bibliographic record
Abstract
Background: Carvedilol, commonly used to treat hypertension and known for its vasodilatory and pleiotropic effects, has been studied in various patient populations. However, its specific impact on diastolic dysfunction and heart failure with preserved ejection fraction (HFpEF) remains unclear. Aim: The aim of the study is to evaluate carvedilol's efficacy in preventing concentric cardiac remodelling in at-risk individuals and modulating it in patients with HFpEF. Methods: In adherence to PRISMA guidelines, we searched PubMed and ScienceDirect up to March 2024 using terms related to carvedilol and HFpEF. We included randomised controlled trials and prospective cohort studies published in English. Outcomes include changes in natriuretic peptides and echocardiography parameters of diastolic function. Exclusion criteria encompassed non-English studies, nonhuman studies and studies not using carvedilol or exclusively involving HFrEF patients. Risk of bias was assessed using the revised Cochrane tool and Newcastle-Ottawa Scale. Data synthesis was performed using a random-effects meta-analysis with sensitivity analyses and a leave-one-out procedure to explore heterogeneity. Results: Eighteen studies involving 2233 participants were included. Various populations were included: those with HFpEF or undergoing cardiotoxic chemotherapy. Meta-analysis did not reveal significant effects of carvedilol on echocardiography parameters such as E/A ratio (mean difference 0.04, 95% CI -0.01 to 0.08), E/e' ratio (mean difference -0.50, 95% CI -1.39 to 0.39) and LVMI (mean difference 0.21, 95% CI -3.13 to 3.55), with substantial heterogeneity observed in LVEF, LVMI and BNP. Conclusion: Carvedilol does not significantly impact diastolic dysfunction across various populations. However, the diversity of study populations and outcomes contributes to the heterogeneity of results.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.036 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".