Efficacy and Tolerability of Combination Treatments for Major Depression: Antidepressants plus Second-Generation Antipsychotics vs. Esketamine vs. Lithium
Bibliographic record
Abstract
BACKGROUND: efficacy and tolerability remain inadequately tested. In particular, the value and safety of increasingly employed second-generation antipsychotics (SGAs) and new esketamine, compared to lithium as antidepressant adjuncts remain unclear. METHODS: We reviewed randomized, placebo-controlled trials and used random-effects meta-analysis to compare odds ratio (OR) versus placebo, as well as numbers-needed-to-treat (NNT) and to-harm (NNH), for adding SGAs, esketamine, or lithium to antidepressants for major depressive episodes. RESULTS: Analyses involved 49 drug-placebo pairs. By NNT, SGAs were more effective than placebo (NNT = 11 [CI: 9-15]); esketamine (7 [5-10]) and lithium (5 [4-10]) were even more effective. Individually, aripiprazole, olanzapine+fluoxetine, risperidone, and ziprasidone all were more effective (all NNT < 10) than quetiapine (NNT = 13), brexpiprazole (16), or cariprazine (16), with overlapping NNT CIs. Risk of adverse effects, as NNH for most-frequently reported effects, among SGAs versus placebo was 5 [4-6] overall, and highest with quetiapine (NNH = 3), lowest with brexpiprazole (19), 5 (4-6) for esketamine, and 9 (5-106) with lithium. The risk/benefit ratio (NNH/NNT) was 1.80 (1.25-10.60) for lithium and much less favorable for esketamine (0.71 [0.60-0.80]) or SGAs (0.45 [0.17-0.77]). CONCLUSIONS: Several modern antipsychotics and esketamine appeared to be useful adjuncts to antidepressants for acute major depressive episodes, but lithium was somewhat more effective and better tolerated. LIMITATIONS: Most trials of adding lithium involved older, mainly tricyclic, antidepressants, and the dosing of adjunctive treatments were not optimized.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".