Gabapentin as an Effective Treatment of Neuropsychiatric Symptoms in Dementia
Bibliographic record
Abstract
BACKGROUND: Neuropsychiatric symptoms (NPS) in dementia are challenging to manage. While Canadian Clinical Practice Guidelines recommend atypical antipsychotics, these often lead to extrapyramidal and gait-related side effects. A 2018 algorithm by Davies et al. suggested that gabapentin, an anti-epileptic drug, has limited evidence for managing agitation and aggression in dementia. This study aimed to evaluate the efficacy of gabapentin in treating NPS. METHODS: Patients in the Virtual Behavioural Medicine (VBM) program from 2022- 2024 (n =250) were treated for NPS in advanced dementia. Demographic, clinical, and treatment data were reviewed. We used the Severity Rating Scale (SRS) to assess NPS severity, where lower scores indicate more severe symptoms (Table 1). Successfully discharged patients (SRS = 7) were categorized into five arms (A-E) (Figure 1). Arm B included patients treated solely with gabapentin, while arm C received at least one drug before successful treatment with gabapentin. Data were analyzed with LASSO and Cox proportional hazards regressions. RESULTS: Gabapentin responders made up 23.6% (n =59, 95% CI: 18-29%) - 48 in arm B and 11 in arm C. Arm B had the youngest median age (73 years, range 47-94), and a male predominance (1.67:1). At baseline, 73% in arm B exhibited physical aggression, 60% verbal aggression, and 52% agitation, while in arm C, 64%, 73% and 18% had similar complaints, respectively. Gabapentin responders were more likely to present with verbal aggression, without delusions or gait disorders. Gabapentin doses ranged from 200-1200 mg/day (arm B, median- 600 mg). Arm B had the shortest median length of stay (9.55 weeks) compared to the other arms (11.7-24.85 weeks), with a significantly shorter stay in arm B than C (p =0.0014). In total, 143 patients received gabapentin. Of these, 13 (9.1%) had side effects leading to discontinuation. Sedation occurred in 10 (7.0%), while confusion (n =3), myoclonus (n =2), and falls (n =1) were also reported. Three patients experienced worsening NPS on gabapentin. CONCLUSIONS: Gabapentin was effective for treating patients with NPS in dementia. It may be an alternative drug for managing NPS in dementia, especially in cases where antipsychotic risks outweigh benefits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".