Efficacy of Beta-Blockers and Angiotensin-Converting Enzyme Inhibitors in Non-Ischemic Dilated Cardiomyopathy: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Non-ischemic dilated cardiomyopathy (NIDCM) is a form of heart failure with a poor prognosis and unclear optimal management. The aim of the study was to systematically review the literature and assess the efficacy and safety of beta-blockers and angiotensin-converting enzyme (ACE) inhibitors in the management of chronic heart failure secondary to NIDCM and explore their putative mechanisms of action. Methods: Studies from 1990 to 2023 were reviewed using PubMed and EMBASE, focusing on their effects on left ventricular ejection fraction (LVEF) in NIDCM patients, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results: Beta-blockers showed a significant beneficial effect on LVEF improvement in NIDCM, with an overall effect size of Cohen¡¯s d = 1.30, 95% confidence interval (CI) (0.76, 1.84), high heterogeneity (Tau 2 = 0.90; Chi 2 = 162.05, df = 13, P < 0.00001; I 2 = 92%), and a significant overall effect (Z = 4.72, P < 0.00001). ACE inhibitors also showed a beneficial role, but with less heterogeneity (Tau 2 = 0.02; Chi 2 = 1.09, df = 1, P = 0.30; I 2 = 8%) and a nonsignificant overall effect (Z = 1.36, P = 0.17), 95% CI (-0.24, 1.31). Conclusions: The study highlights the efficacy of carvedilol in improving LVEF in NIDCM patients over ACE inhibitors, recommends beta-blockers as first-line therapy, and advocates further research on ACE inhibitors. Cardiol Res. 2024;15(4):281-297 doi: https://doi.org/10.14740/cr1653
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.037 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".