Comparative efficacy of neuromodulation therapies in Lennox–Gastaut syndrome: A systematic review and meta‐analysis of vagus nerve stimulation, deep brain stimulation, and responsive neurostimulation
Bibliographic record
Abstract
OBJECTIVE: Lennox-Gastaut syndrome (LGS) is a childhood onset developmental and epileptic encephalopathy characterized by multiple seizure types that are often refractory to traditional antiseizure medications. Neuromodulation therapies including vagus nerve stimulation (VNS), deep brain stimulation (DBS), and responsive neurostimulation (RNS) have emerged as potential treatment options, but their comparative efficacy remains unclear. METHODS: We conducted a systematic review and meta-analysis of studies reporting outcomes of neuromodulation therapies in patients with LGS. A comprehensive search of electronic databases was performed through July 26, 2024. The primary outcome was the proportion of patients achieving ≥50% seizure reduction. Random-effects models were used to calculate pooled estimates, and meta-regression analyses were performed to identify potential effect modifiers. RESULTS: Fifty-four studies comprising 1350 patients were included in the analysis (VNS: 37 studies, 1242 patients; DBS: 11 studies, 81 patients; RNS: six studies, 27 patients). The overall pooled responder rate was 55.4% (95% confidence interval [CI] = 48.0%-62.8%). DBS showed the highest responder rate (69.7%, 95% CI = 51.3-88.1%), followed by RNS (63.0%, 95% CI = 30.9-95.1%) and VNS (50.6%, 95% CI = 43.0-58.2%). Meta-regression analysis revealed that intervention type was a significant moderator of treatment effect, with VNS showing significantly lower efficacy compared to DBS (p = .0305). In the DBS subgroup, a later onset of epilepsy was a significant positive predictor of response (p = .0051). Twenty studies qualitatively described quality-of-life outcomes, most commonly noting improved alertness and attention, although heterogeneous assessments precluded meta-analysis. Twenty-seven studies reported complications; VNS was linked to stimulation-related side effects, whereas DBS and RNS had higher rates of serious device-related issues. SIGNIFICANCE: This meta-analysis suggests that all three neuromodulation therapies are effective for seizure reduction in LGS, with DBS and RNS demonstrating potentially superior efficacy compared to VNS. These findings may help guide treatment selection for patients with LGS, although prospective comparative studies are needed.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.038 |
| Bibliometrics | 0.006 | 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) | 0.003 | 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".