Vascular endothelial growth factor gene transfer therapy for coronary artery disease: A systematic review and meta‐analysis
Bibliographic record
Abstract
AIM: It is not clear whether treatment by vascular endothelial growth factor (VEGF) gene transfer can improve myocardial ischemia through a proangiogenesis mechanism and is effective against coronary artery disease (CAD). We aimed to perform a systematic review and meta-analysis of randomized controlled trials (RCTs) that compared VEGF gene therapy and standard treatments in CAD. METHODS: We systematically searched the PubMed, Embase, and Cochrane databases and relevant references for RCTs (published up to May 2018; no language restrictions) and performed meta-analysis using both fixed and random effects models. Our primary outcome measures were mortality and serious cardiac events. The secondary outcome measures were follow-up left ventricular ejection fraction (LVEF), change in LVEF (ΔLVEF), and angina outcomes. The registration number is CRD42017058430. RESULTS: Of 524 identified studies, 14 were eligible and were included in our analysis. At a mean follow-up of 6 months, VEGF gene therapy demonstrated a decreased risk of serious cardiac events (11.7% vs 21.2%, relative risk: 0.56; 95% confidence interval (CI): 0.37, 0.84; P = 0.005) and a slight improvement in follow-up LVEF (weighted mean difference: 1.95; 95%CI: 1.28, 2.62). Furthermore, VEGF gene therapy using adenoviral vectors showed more potential benefit in terms of the risk of serious cardiac events, ΔLVEF, and Canadian Cardiovascular Society angina class. Nevertheless, mortality and angina frequency scores were not different. CONCLUSIONS: Vascular endothelial growth factor gene therapy appears to be safe and effective regarding serious cardiac events, with greater benefit when using adenoviral vectors. This meta-analysis highlights the need for further exploration in these areas.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.029 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".