Mortality and Lithium-Protective Effects after First-Episode Mania Diagnosis in Bipolar Disorder: A Nationwide Retrospective Cohort Study in Taiwan
Bibliographic record
Abstract
INTRODUCTION: This study aimed to estimate all-cause mortality in patients after a first-episode mania (FEM) and examine whether six guideline-recommended medications can reduce mortality. METHODS: The cohort included population-based FEM samples and matched controls from Taiwan, spanning 2007 to 2018. The primary outcomes assessed were all-cause/suicide-related mortality, while the secondary outcome focused on mortality associated with pharmacological treatments. We compared mortality in post-FEM patients and age-/sex-matched controls without any diagnosed bipolar disorders and patients with and without psychopharmacological treatment using Cox regression analysis, respectively. Statistics were presented with time-to-event adjusted hazard ratios (AHRs) and 95% confidence intervals (CIs). RESULTS: The study included 54,092 post-FEM patients and 270,460 controls, totaling 2,467,417 person-years of follow-up. Post-FEM patients had higher risks of all-cause mortality (AHR 2.38, 95% CI: 2.31-2.45) and suicide death (10.80, 5.88-19.84) than controls. Lithium (0.62, 0.55-0.70), divalproex (0.89, 0.83-0.95), and aripiprazole (0.81, 0.66-1.00) were associated with reduced all-cause mortality compared to non-users. There were no significant all-cause mortality differences for quetiapine (0.95, 0.89-1.01), risperidone (0.92, 0.82-1.02), and paliperidone (1.24, 0.88-1.76) users. When accounting for drug action onset times in sensitivity analyses, only lithium significantly reduced all-cause mortality (AHR range 0.65-0.72). There were 35 and 16 suicide deaths in post-FEM patients and controls, respectively. No drug had a significant effect on suicide deaths (lithium: 6; divalproex: 7; aripiprazole: 0; quetiapine: 10; risperidone: 4; paliperidone: 1). CONCLUSION: Post-FEM patients had a higher risk of all-cause/suicide-related mortality, and lithium treatment might reduce all-cause mortality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".