Addition of long-acting injectable antipsychotics during manic episodes in bipolar disorder: A retrospective analysis of rehospitalizations
Bibliographic record
Abstract
INTRODUCTION: Bipolar disorder (BD) often necessitates hospitalization, especially during manic episodes. Long-acting injectable antipsychotics (LAIs) are theorized to enhance treatment adherence and decrease rehospitalization rates compared to oral medications. This study aimed to evaluate the real-world effectiveness of LAIs in reducing rehospitalizations among BD patients admitted for bipolar mania. METHODS: We conducted a retrospective cohort study using data from a tertiary psychiatry hospital in Taiwan spanning January 1st, 2006, to December 31st, 2017. We analyzed 2212 hospitalizations among 945 patients with bipolar mania. A mixed-effects Cox regression model compared rehospitalization hazards between LAIs, mood stabilizer plus oral antipsychotic (MS + OAP), and mood stabilizer only (MS) groups. Sensitivity analyses assessed robustness across various subgroup criteria. RESULTS: LAI treatment significantly reduced the hazard of rehospitalization within one year post-discharge compared to MS + OAP (HR = 2.29, 95 % CI = 1.56-3.36) and MS alone (HR = 2.66, 95 % CI = 1.68-4.21). This effect was consistent across different rehospitalization types-all-cause, bipolar disorder-specific, and bipolar mania-specific. Each additional previous hospitalization was associated with higher hazard of rehospitalization across the three rehospitalization types. Sensitivity analyses suggested LAIs' efficacy in manic episodes with and without psychotic symptoms and for patients with frequent hospitalizations. The LAIs included in the analysis are haloperidol, risperidone, fluphenazine, flupentixol, and zuclopenthixol. CONCLUSION: Our findings suggest that the addition of LAIs for bipolar mania during acute inpatient treatment is associated with reduced rehospitalizations, particularly among patients with recurrent hospitalizations, making it a valuable option. However, the lack of outpatient prescription data limits our ability to further substantiate this concept, warranting future research.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".