Moderators of antidepressant augmentation versus switch in the OPTIMUM randomised controlled trial
Bibliographic record
Abstract
Background Older adults with treatment-resistant depression (TRD) benefit more from treatment augmentation than switching. It is useful to identify moderators that influence these treatment strategies for personalised medicine. Aims Our objective was to test whether age, executive dysfunction, comorbid medical burden, comorbid anxiety or the number of previous adequate antidepressant trials could moderate the superiority of augmentation over switching. A significant moderator would influence the differential effect of augmentation versus switching on treatment outcomes. Method We performed a preplanned moderation analysis of data from the Optimizing Outcomes of Treatment-Resistant Depression in Older Adults (OPTIMUM) randomised controlled trial ( N = 742). Participants were 60 years old or older with TRD. Participants were either (a) randomised to antidepressant augmentation with aripiprazole (2.5–15 mg), bupropion (150–450 mg) or lithium (target serum drug level 0.6 mmol/L) or (b) switched to bupropion (150–450 mg) or nortriptyline (target serum drug level 80–120 ng/mL). Treatment duration was 10 weeks. The two main outcomes of this analysis were (a) symptom improvement, defined as change in Montgomery–Asberg Depression Rating Scale (MADRS) scores from baseline to week 10 and (b) remission, defined as MADRS score of 10 or less at week 10. Results Of the 742 participants, 480 were randomised to augmentation and 262 to switching. The number of adequate previous antidepressant trials was a significant moderator of depression symptom improvement ( b = −1.6, t = −2.1, P = 0.033, 95% CI [−3.0, −0.1], where b is the coefficient of the relationship (i.e. effect size), and t is the t -statistic for that coefficient associated with the P -value). The effect was similar across all augmentation strategies. No other putative moderators were significant. Conclusions Augmenting was superior to switching antidepressants only in older patients with fewer than three previous antidepressant trials. This suggests that other intervention strategies should be considered following three or more trials.
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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 | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".