Anxiety, irritability, and agitation as indicators of bipolar mania with depressive symptoms: a post hoc analysis of two clinical trials
Bibliographic record
Abstract
BACKGROUND: Symptoms of anxiety, irritability, and agitation (AIA) are prevalent among patients with bipolar I disorder (BD-I) mania with depressive symptoms, and could potentially be used to aid physicians in the identification of this more severe form of BD-I. Using data from two clinical trials, the aims of this post hoc analysis were to describe the phenomenology of bipolar mania in terms of AIA and depressive symptoms, and to evaluate the influence of these symptoms on the likelihood of remission during treatment. METHODS: Patients with a BD-I manic or mixed episode (Diagnostic and Statistical Manual of Mental Disorders IV criteria) were randomised to 3 weeks of double-blind treatment with asenapine, placebo, or olanzapine (active comparator). Anxiety was defined as a score of ≥3 on the Positive and Negative Syndrome Scale 'anxiety' item, irritability as a score of ≥4 on the Young Mania Rating Scale (YMRS) 'irritability' item, and agitation as a score of ≥3 on the YMRS 'increased motor activity-energy' item. Depressive symptoms were defined as a score of ≥1 on three or more individual Montgomery-Åsberg Depression Rating Scale (MADRS) items, or a MADRS Total score of ≥20. RESULTS: A total of 960 patients with BD-I were analysed, 665 with a manic episode and 295 with a mixed episode. At baseline, 61.4% had anxiety, 62.4% had irritability, 76.4% had agitation, and 34.0% had all three AIA symptoms ('severe AIA'); 47.3% had three or more depressive symptoms, and 13.5% had a MADRS total score of ≥20. Anxiety, irritability, and severe AIA (but not agitation) were statistically significantly more common in patients with depressive symptoms. Patients with anxiety or severe AIA at baseline were statistically significantly less likely to achieve remission (YMRS total <12). In general, remission rates were higher with asenapine and olanzapine than with placebo, irrespective of baseline AIA or depressive symptoms. CONCLUSIONS: Assessment of AIA symptoms in bipolar mania could enable physicians to identify patients with more severe depressive symptoms, allowing for appropriate intervention. Assessment and monitoring of AIA may help physicians to predict which patients may be harder to treat and at risk for self-harm. Trial registration ClinicalTrials.gov NCT00159744, NCT00159796. Registered 8 September 2005 (retrospectively registered).
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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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".