Risk Factors for Suicide Attempts in Chinese Patients with Major Depressive Disorder: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND This meta-analysis aimed to identify risk factors for suicide attempts (SA) in patients with depression to inform clinical practice. MATERIAL AND METHODS We searched multiple databases up to January 1, 2025, including MEDLINE and Embase. Case-control and cohort studies reporting risk factors for SA in patients with depression were included. Study quality was assessed using the Newcastle-Ottawa Scale (NOS), and meta-analyses were performed using Rev Man 5.4 software. Results are expressed as odds ratios (OR) and 95% confidence intervals (CI). Heterogeneity was assessed using I² and P values, and publication bias was evaluated using funnel plots and Egger's test. The registration information was deposited in the International Register of Systematic Reviews and Meta-Analyses (PROSPERO) trial registry (CRD420251061401; Retrospective registration). RESULTS Out of a total of 3792 records, 22 case-control studies were included. The overall heterogeneity (I2) ranged from 0% to 91.9%. Significant risk factors for SA included suicidal ideation (OR=4.98, 95% CI 3.21-7.22), previous hospitalizations (OR=1.38, 95% CI 1.18-1.61), family history of suicide (OR=2.59, 95% CI 1.89-3.57), psychotic symptoms (OR=2.77, 95% CI 1.98-3.88), frequent depressive episodes (OR=2.58, 95% CI 1.58-4.22), self-blame (OR=2.43, 95% CI 1.78-3.31), negative life events (OR=3.77, 95% CI 2.85-5.51), and delusion (OR=3.14, 95% CI 1.99-4.96). Publication bias was detected for family history of suicide and suicidal ideation, but OR values remained significant after correction. CONCLUSIONS Our findings highlight the need for comprehensive risk assessments and targeted interventions in clinical practice to prevent suicide attempts in patients with depression. Future research should explore the mechanisms and interactions of these risk factors to refine prevention strategies.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.042 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".