Sleep Quality After Quetiapine Augmentation in Patients With Treatment-Resistant Depression and Personality Disorders
Bibliographic record
Abstract
PURPOSE/BACKGROUND: Quetiapine is a first-line augmenting agent for treatment-resistant depression (TRD) and is used off-label in insomnia. Quetiapine and its active metabolite norquetiapine act mostly on 5-HT2A, 5-HT2C, H1, and D2 as antagonists and on 5-HT1A as partial agonists. Patients with TRD often have comorbid personality disorder (PD), and evidence suggests an association between sleep disturbance and recovery among patients with PD. Here, we aimed to evaluate the effects of quetiapine on sleep in TRD patients with and without PD (PD+/PD-). METHODS/PROCEDURES: We reviewed health records of 38 patients with TRD (20 TRD/PD+) who had been treated with a pharmacotherapy regimen including quetiapine. Clinical outcomes were determined by comparing changes in sleep items of the Hamilton Depression Rating Scale at the beginning (T0) and after 3 months of an unchanged treatment (T3). FINDINGS/RESULTS: Patients with TRD/PD+ and TRD/PD- taking quetiapine showed significant improvement in sleep items from T0 to T3 (P < 0.001, ηp2 ≥ 0.19). There was a significant personality × time interaction for sleep-maintenance insomnia (P = 0.006, ηp2 = 0.23), with TRD/PD+ showing a greater improvement at T3 compared with TRD/PD- (P = 0.01). While exploring other sleep items, no personality × time interaction was found. In the TRD/PD- group, improvement in sleep items was associated with an overall improvement in depressive symptoms (r = 0.55, P = 0.02). IMPLICATIONS/CONCLUSIONS: Quetiapine induced greater improvements in sleep-maintenance insomnia among TRD/PD+ patients than TRD/PD-. These findings suggest quetiapine could have a therapeutic role for insomnia in PD underscoring a distinct underlying neurobiological mechanism of sleep disturbance in people living with PD.
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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 | 0.000 | 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".