Citation

Zhang J, Liang S, Liu X, Li D, Zhou F, Xiao L, Liu J, Sha S. Front. Public Health 2023; 11: e1157606.

Copyright

(Copyright © 2023, Frontiers Editorial Office)

DOI

10.3389/fpubh.2023.1157606

PMID

37818303

PMCID

PMC10560740

Abstract

AIM: This study aims to establish a nomogram model to predict the relevance of SA in Chinese female patients with mood disorder (MD).

METHOD: The study included 396 female participants who were diagnosed with MD Diagnostic Group (F30-F39) according to the 10th Edition of Disease and Related Health Problems (ICD-10). Assessing the differences of demographic information and clinical characteristics between the two groups. LASSO Logistic Regression Analyses was used to identify the risk factors of SA. A nomogram was further used to construct a prediction model. Bootstrap re-sampling was used to internally validate the final model. The Receiver Operating Characteristic (ROC) curve and C-index was also used to evaluate the accuracy of the prediction model.

RESULT: LASSO regression analysis showed that five factors led to the occurrence of suicidality, including BMI (β = -0.02, SE = 0.02), social dysfunction (β = 1.72, SE = 0.24), time interval between first onset and first dose (β = 0.03, SE = 0.01), polarity at onset (β = -1.13, SE = 0.25), and times of hospitalization (β = -0.11, SE = 0.06). We assessed the ability of the nomogram model to recognize suicidality, with good results (AUC = 0.76, 95% CI: 0.71-0.80). Indicating that the nomogram had a good consistency (C-index: 0.756, 95% CI: 0.750-0.758). The C-index of bootstrap resampling with 100 replicates for internal validation was 0.740, which further demonstrated the excellent calibration of predicted and observed risks.

CONCLUSION: Five factors, namely BMI, social dysfunction, time interval between first onset and first dose, polarity at onset, and times of hospitalization, were found to be significantly associated with the development of suicidality in patients with MD. By incorporating these factors into a nomogram model, we can accurately predict the risk of suicide in MD patients. It is crucial to closely monitor clinical factors from the beginning and throughout the course of MD in order to prevent suicide attempts.

Language: en

Keywords

Humans; Female; Risk Factors; Suicide, Attempted; *Suicidal Ideation; prediction model; *Nomograms; Chinese population; LASSO Logistic Regression; mood disorder; Mood Disorders/epidemiology; suicidal attempt