Efficacy and Safety of Brexpiprazole in Agitation Associated with Dementia in Alzheimer's Disease: A Systematic Review and Meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Agitation is one of the most distressing neuropsychiatric symptoms in patients with dementia due to Alzheimer's disease (AD), significantly impacting patients' quality of life and increasing caregiver burden. Current management options for agitation in AD remain limited, with many treatments associated with modest efficacy and considerable safety concerns. Brexpiprazole, a serotonin-dopamine activity modulator, has recently emerged as a potential therapeutic option for this challenging clinical scenario. This systematic review and meta-analysis aim to evaluate the efficacy and safety of brexpiprazole in managing agitation associated with dementia in AD. METHOD: statistics. A p-value of <0.05 was considered statistically significant. All the calculations were performed using RevMan 5.4. RESULT: This meta-analysis included 4 studies involving 1440 patients (944 vs. 496) suffering from agitation associated with dementia in AD. Brexpiprazole demonstrated a significant reduction in the Cohen-Mansfield Agitation Inventory (CMAI) score (MD: -3.94 [95% CI: -6.21 to -1.67], p <0.001) and Neuropsychiatric Inventory-Nursing Home (NPI-NH) score (MD: -0.67 [95% CI: -1.08 to -0.26], p = 0.002) from baseline, with the greatest improvement observed at the 2-3 mg dose. There was statistically significant change in SAS score (MD: 0.38, [95% CI: 0.18 to 0.58], p = 0.0002) but no difference in Mini-Mental State Examination (MMSE) score (MD: -0.26 [95% CI: -0.53 to 0.01], p = 0.06) and Clinical Global Impression-Severity (CGI-S) score (MD: -0.16 [95% CI: -0.34 to 0.01], p = 0.06) from the baseline with brexpiprazole. There was no difference between two groups regarding serious adverse events (p = 0.57), dizziness (p = 0.49), extrapyramidal effects (p = 0.18) and mortality (p = 0.22). CONCLUSION: Brexpiprazole significantly reduces agitation symptoms in Alzheimer's disease, as shown by improvements in CMAI and NPI-NH scores, with no significant cognitive or safety concerns. While promising, limitations include moderate heterogeneity and short follow-up durations. Future studies should focus on long-term effects, quality of life, and identifying optimal patient subgroups for treatment.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".