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

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< 0.001).ConclusionThe nomogram based on demographic and clinical characteristics can predict suicidal behavior risk in Chinese female patients with mood disorders.