Efficacy and Safety of Antidepressants for Treatment of Depression in Alzheimer's Disease: A Metaanalysis
Bibliographic record
Abstract
OBJECTIVE: Depression in patients with Alzheimer's disease (AD) is common (15% to 63%) and is associated with significant morbidity and increased mortality. Our objective was to quantitatively summarize the data on the efficacy and safety of antidepressant treatment for depression complicating AD. METHOD: We performed a metaanalysis of randomized, double-blind, placebo-controlled trials of antidepressants with a database search of the English literature (up to 2006) and a manual search of references in the retrieved articles. We extracted the proportion of subjects who responded and remitted, experienced adverse events (AEs), discontinued treatment due to AEs, or discontinued treatment for any reason. Cognition scores were also extracted. RESULTS: We included 5 studies, which involved 82 subjects treated with antidepressants and 83 subjects who received placebo treatment. Antidepressants were superior to placebo for both treatment response (odds ratio [OR] 2.32; 95% confidence interval [CI], 1.04 to 5.16) and remission of depression (OR 2.75; 95% CI, 1.13 to 6.65). There were no significant differences between the 2 groups for change in cognition (weighted mean difference -0.71, 95% CI, -3.20 to 1.79), overall dropouts (OR 0.70; 95% CI, 0.29 to 1.66) or dropout due to AEs (OR 1.41; 95% CI 0.36 to 5.54). The numbers needed to treat for one additional AD patient to respond to antidepressant treatment were 5 (95% CI, 3 to 59) and 5 (95% CI, 2 to 24) for remission of depression. CONCLUSIONS: Antidepressant treatment for depression in AD is efficacious, with rates of discontinuation that are comparable to placebo. Nonetheless, clinicians must be vigilant regarding the potential side effects of antidepressants in this population.
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 | 0.025 | 0.038 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.055 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".