Risk prediction model for non-suicidal self-injury behavior among college students based on LASSO regression analysis

Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2025 Sept 28;50(9):1483-1494. doi: 10.11817/j.issn.1672-7347.2025.240256.

ABSTRACT

OBJECTIVES: Non-suicidal self-injury (NSSI) among college students has become a significant public health concern, highlighting the need for effective early identification tools. This study aims to construct a predictive model for NSSI among college students using the least absolute shrinkage and selection operator (LASSO) regression analysis.

METHODS: From April to June 2022, an online questionnaire survey was conducted among college students in 6 provinces: Hunan, Jiangxi, Hubei, Shandong, Guangdong, and Jilin. Sociodemographic information was collected, along with assessments using the Adolescent Non-suicidal Self-injury Assessment Questionnaire, Patient Health Questionnaire-9, Anger Rumination Scale, Multiple Forms of Violence Scale, Childhood Trauma Questionnaire-28 item Short Form, and Community Assessment of Psychic Experiences. LASSO regression analysis was performed to identify predictors of NSSI, construct the predictive model, and develop a nomogram. Calibration curves and receiver operating characteristic (ROC) curves were used to evaluate the calibration and discrimination of the model.

RESULTS: A total of 4 121 college students participated in this study, among whom 650 reported NSSI behaviors, yielding a detection rate of 15.8%. LASSO regression identified 5 predictors of NSSI: Experiences of bullying in primary school, history of alcohol use, depressive symptoms, anger rumination, and psychotic-like experiences. The predictive model was expressed as: Risk of NSSI = (bullying in primary school × 0.41) + (history of alcohol use × 0.76) + (depressive symptoms × 0.08) + (anger rumination × 0.04) + (psychotic-like experiences × 0.05). The area under the curve (AUC) of the predictive model was 0.782 for the training set and 0.769 for the testing set. Calibration curves indicated good agreement between predicted and observed values.

CONCLUSIONS: The predictive model demonstrated strong predictive ability and was visualized using a nomogram. This model can be used to assess the risk of NSSI among college students based on identified risk factors and may assist clinicians and educators in identifying high-risk individuals for early interventions.

PMID:41492733 | DOI:10.11817/j.issn.1672-7347.2025.240256