Vagal Neuromodulation in Chronic Heart Failure With Reduced Ejection Fraction: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objectives: The aim of this study was to evaluate the effects of invasive vagal nerve stimulation (VNS) in patients with chronic heart failure (HF) and reduced ejection fraction (HFrEF). Background: Heart failure is characterized by autonomic nervous system imbalance and electrical events that can lead to sudden death. The effects of parasympathetic (vagal) stimulation in patients with HF are not well-established. Methods: From May 1994 to July 2020, a systematic review was performed using PubMed, Embase, and Cochrane Library for clinical trials, comparing VNS with medical therapy for the management of chronic HFrEF (EF ≤ 40%). A meta-analysis of several outcomes and adverse effects was completed, and GRADE was used to assess the level of evidence. Results: Four randomized controlled trials (RCT) and three prospective studies, totalizing 1,263 patients were identified; 756 treated with VNS and 507 with medical therapy. RCT data were included in the meta-analysis (fixed-effect distribution). Adverse effects related to VNS were observed in only 11% of patients. VNS was associated with significant improvement (GRADE = High) in the New York Heart Association (NYHA) functional class (OR, 2.72, 95% CI: 2.07–3.57, p < 0.0001), quality of life (MD −14.18, 95% CI: −18.09 to −10.28, p < 0.0001), a 6-min walk test (MD, 55.46, 95% CI: 39.11–71.81, p < 0.0001) and NT-proBNP levels (MD −144.25, 95% CI: −238.31 to −50.18, p = 0.003). There was no difference in mortality (OR, 1.24; 95% CI: 0.82–1.89, p = 0.43). Conclusions: A high grade of evidence demonstrated that vagal nerve stimulation improves NYHA functional class, a 6-min walk test, quality of life, and NT-proBNP levels in patients with chronic HFrEF, with no differences in mortality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.003 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".