Updates on Baroreflex Activation Therapy and Vagus Nerve Stimulation for Treatment of Heart Failure With Reduced Ejection Fraction
Bibliographic record
Abstract
In the last decade, neuromodulation via baroreflex activation therapy (BAT) and vagus nerve stimulation (VNS) has emerged as an innovative approach for the treatment of heart failure with reduced ejection fraction (HFrEF). A review of the literature was conducted to examine the latest efficacy and safety data on neuromodulation for the treatment of HFrEF. Two independent researchers searched the PubMed, clinicaltrials.org, and the Cochrane databases for the most recent data on BAT and VNS published between 2013 and 2019. A total of nine studies were identified. BAT and VNS therapy consistently improved subjective heart failure parameters including New York Heart Association class and Minnesota Living with Heart Failure Questionnaire. Improvements in objective cardiac parameters such as left ventricular ejection fraction (LVEF) were less consistently seen; however, where present, ranged from +3% to +6%, in line with improvements seen after other guideline directed therapy such as left ventricular assist device (LVAD). Benefits of BAT showed a predilection for patients without cardiac resynchronization therapy (CRT) and efficacy of VNS therapy varied with device type. The clinical application of BAT and VNS was found to be limited due to low-powered data, inconsistencies in study design, short-term follow-up and lack of diversity in patient recruitment. Well-powered studies with consistent design, longer follow-up and diverse populations are warranted before BAT and VNS can be incorporated into heart failure guidelines and clinical practice.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".