Clinical Predictors of Extrapyramidal Symptoms Associated With Aripiprazole Augmentation for the Treatment of Late-Life Depression in a Randomized Controlled Trial
Bibliographic record
Abstract
OBJECTIVE: Augmentation with aripiprazole is an effective pharmacotherapy for treatment-resistant late-life depression (LLD). However, aripiprazole can cause extrapyramidal symptoms (EPS) such as akathisia and parkinsonism; these symptoms are distressing and can contribute to treatment discontinuation. We investigated the clinical trajectories and predictors of akathisia and parkinsonism in older patients receiving aripiprazole augmentation for treatment-resistant LLD. METHODS: Between 2009 and 2013, depressed older adults who did not remit with venlafaxine were randomized to aripiprazole or placebo in a 12-week trial. Participants were 60 years or older and met DSM-IV-TR criteria for major depressive episode with at least moderate symptoms. The presence of akathisia and parkinsonism was measured at each visit using the Barnes Akathisia Scale (BAS) and Simpson-Angus Scale (SAS), respectively. In an exploratory analysis, we examined a broad set of potential clinical predictors and correlates: age, sex, ethnicity, weight, medical comorbidity, baseline anxiety severity, depression severity, concomitant medications including rescue medications, and aripiprazole dosage. RESULTS: Twenty-four (26.7%) of 90 participants randomized to aripiprazole and who had akathisia scores available developed akathisia compared to 11 (12.2%) of 90 randomized to placebo. Greater depression severity was the main predictor of treatment-emergent akathisia. Most participants who developed akathisia improved over time, especially with reductions in dosage. Fifteen (16.5%) of 91 participants taking aripiprazole and who had parkinsonism scores available developed parkinsonism, but no clinical predictors or correlates were identified. CONCLUSIONS: Akathisia is a common side effect of aripiprazole, but it is typically mild and responds to dose reduction. Patients with greater baseline depression may warrant closer monitoring for akathisia. More research is needed to understand the course and predictors of treatment-emergent EPS with antipsychotic augmentation for treatment-resistant LLD. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT00892047.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".