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Development and validation of a nomogram to predict suicidal behavior in female patients with mood disorder
- Source :
- Frontiers in Psychiatry, Vol 14 (2023)
- Publication Year :
- 2023
- Publisher :
- Frontiers Media S.A., 2023.
-
Abstract
- IntroductionThis study aims to explore the risk factors associated with suicidal behavior and establish predictive models in female patients with mood disorders, specifically using a nomogram of the least absolute shrinkage and selection operator (LASSO) regression.MethodsA cross-sectional survey was conducted among 396 female individuals diagnosed with mood disorders (F30-F39) according to the International Classification of Diseases and Related Health Problems 10th Revision (ICD-10). The study utilized the Chi-Squared Test, t-test, and the Wilcoxon Rank-Sum Test to assess differences in demographic information and clinical characteristics between the two groups. Logistic LASSO Regression Analyses were utilized to identify the risk factors associated with suicidal behavior. A nomogram was constructed to develop a prediction model. The accuracy of the prediction model was evaluated using a Receiver Operating Characteristic (ROC) curve.ResultThe LASSO regression analysis showed that psychotic symptoms at first-episode (β = 0.27), social dysfunction (β = 1.82), and somatic disease (β = 1.03) increased the risk of suicidal behavior. Conversely, BMI (β = −0.03), age of onset (β = −0.02), polarity at onset (β = −1.21), and number of hospitalizations (β = −0.18) decreased the risk of suicidal behavior. The area under ROC curve (AUC) of the nomogram predicting SB was 0.778 (95%CI: 0.730–0.827, p
Details
- Language :
- English
- ISSN :
- 16640640
- Volume :
- 14
- Database :
- Directory of Open Access Journals
- Journal :
- Frontiers in Psychiatry
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.91fcc1d67f0342c08123ec79df4d7999
- Document Type :
- article
- Full Text :
- https://doi.org/10.3389/fpsyt.2023.1212579