Development and validation of a nomogram to predict suicidal behavior in female patients with mood disorder

• Published in: Frontiers in psychiatry Vol. 14; p. 1212579
• Main Authors: Liang, Sixiang, Liu, Xinyu, Li, Dan, Zhang, Jinhe, Zhao, Guangwei, Yu, Hongye, Zhao, Xixi, Sha, Sha
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 07.07.2023
• Subjects: mood disorder; nomogram; predictive model; Psychiatry; suicidal behavior; validation; mood disorder; predictive model; nomogram; suicidal behavior; validation
• ISSN: 1664-0640; 1664-0640
• DOI: 10.3389/fpsyt.2023.1212579

This 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. A 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, -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. The LASSO regression analysis showed that psychotic symptoms at first-episode ( = 0.27), social dysfunction ( = 1.82), and somatic disease ( = 1.03) increased the risk of suicidal behavior. Conversely, BMI ( = -0.03), age of onset ( = -0.02), polarity at onset ( = -1.21), and number of hospitalizations ( = -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, < 0.001). The nomogram based on demographic and clinical characteristics can predict suicidal behavior risk in Chinese female patients with mood disorders.