Incidence and Predictors of Suicide Attempts in Bipolar I and II Disorders: A Five-year Follow-up
Bibliographic record
Abstract
Introduction Although suicidal behavior is very common in bipolar disorder (BD), few long-term studies have investigated incidence and risk factors of suicide attempts (SAs) specifically related to illness phases of BD. Objectives We examined incidence of SAs during different phases of BD in a long-term prospective cohort of bipolar I (BD-I) and II (BD-II) patients and risk factors specifically for SAs during major depressive episodes (MDEs). Methods In the Jorvi bipolar study (JoBS), 191 BD-I and BD-II patients were followed using life-chart methodology. Prospective information on SAs of 177 patients (92.7%) during different illness phases was available up to five years. Incidence of SAs and their predictors were investigated using logistic and Poisson regression models. Analyses of risk factors for SAs occurring during MDEs were conducted using two-level random-intercept logistic regression models. Results During the five-year follow-up, 90 SAs per 718 patient-years occurred. Compared with euthymia the incidence was highest, over 120-fold, during mixed states (765/1000 person-years [95% confidence interval (CI) 461–1269]) and also very high in MDEs, almost 60-fold (354/1000 [95%CI 277–451]). For risk of SAs during MDEs, the duration of MDEs, severity of depression and cluster C personality disorders were significant predictors. Conclusions In this long-term study, the highest incidences of SAs occurred in mixed phases and MDEs. The variations in incidence rates between euthymia and illness phases were remarkably large, suggesting that the question “when” rather than “who” may be more relevant for suicide risk in BD. However, risk during MDEs is likely also influenced by personality factors. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".