Nomogram for estimating the risk of suicide attempts in major depressive disorder: integrating demographic, clinical and biochemical markers – a cross-sectional study
Bibliographic record
Abstract
Background: Major depressive disorder (MDD) is a significant risk factor for suicide attempts (SA), making early identification of those most at risk crucial for effective clinical intervention. This study aimed to identify demographic, clinical, and biochemical correlates of SA among inpatients with MDD. Methods: In this cross-sectional study, data were collected from 968 inpatients diagnosed with MDD, including 392 with documented suicide attempts (MDD-SA) and 576 without suicidal behaviors (MDD-NSA). Demographic characteristics, clinical history, and peripheral biochemical markers were analyzed using multivariable logistic regression to assess associations with SA. Variable selection was performed using penalized logistic regression with 10-fold cross-validation, and the selected variables were then entered into a binary logistic regression model to illustrate the relative contribution of significant factors. Model performance was evaluated using the area under the Receiver Operating Characteristic (ROC) curve, calibration plots, and decision curve analysis (DCA). Results: Significant differences were found between MDD-S and MDD-N in marital status (42.3% vs. 57.7%, p = 0.004), alcohol consumption (15.82% vs. 8.85%, p < 0.001), tobacco use (20.67% vs. 13.73%, p = 0.004), education level (p < 0.001), life events (79.59% vs. 65.28%, p < 0.001), and folate levels (p < 0.001). MDD-S patients were more likely to undergo modified electroconvulsive therapy (61.73% vs. 39.41%, p < 0.001) and mood stabilizers (26.02% vs. 18.92%, p = 0.009). The nomogram incorporated tobacco use, alcohol consumption, education level, life events, and folate levels, showing good discrimination (C-index = 0.709, bootstrap = 0.703). ROC analysis demonstrated an AUC of 0.709 (95% CI: 0.677-0.742), and DCA confirmed clinical utility. Conclusion: Several routinely available variables, including tobacco use, alcohol consumption, education level, life events, and folate level, were associated with suicide attempts in MDD inpatients. Our findings highlight these important correlates, which may help clinicians in recognizing patients at elevated risk. The nomogram provided in this study should be regarded only as a visualization to better illustrate the contribution of these factors, rather than as a clinical assessment tool. Prospective multicenter studies are needed for further validation.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".