Liu YuJie, Wu TaiMin, Yan Shu, Zhou Yang, Liu Lianzhong
School of Medicine, Jianghan University, Wuhan, Hubei, China.
Office of Psychosocial Services, Wuhan Mental Health Center, Wuhan, Hubei, China.
Front Psychiatry. 2025 Apr 4;16:1539884. doi: 10.3389/fpsyt.2025.1539884. eCollection 2025.
The issue of psychological maladjustment, particularly Non-Suicidal Self-Injury (NSSI), is prevalent among vocational high school students. Therefore, timely identification of high-risk individuals is important in providing further intervention.
A survey was conducted among 2081 students from a vocational high school in Wuhan, China. The students were divided into two groups: those who had engaged in Non-Suicidal Self-Injury (NSSI) within the past two weeks and those who had not. Lasso regression and logistic regression were employed to identify significant risk factors associated with NSSI. Subsequently, a nomogram was developed to enhance the accuracy and efficiency of identifying individuals at high risk for NSSI. The performance of the model was assessed through various validation methods including Area Under the Curve (AUC), calibration curves, and Decision Curve Analysis (DCA).
The significant predictors of NSSI encompassed gender, problem behavior, depressive mood, and borderline personality tendencies. Based on these predictors, a nomogram was constructed. The model's accuracy was validated using AUC, calibration curves, and DCA, showing high accuracy.
A nomogram prediction tool for NSSI among vocational high school students was constructed, providing an accurate and quick method for predicting adolescent NSSI behavior.
心理适应不良问题，尤其是非自杀性自伤行为（NSSI），在职业高中学生中普遍存在。因此，及时识别高危个体对于提供进一步干预至关重要。
对来自中国武汉一所职业高中的2081名学生进行了一项调查。学生被分为两组：在过去两周内有过非自杀性自伤行为（NSSI）的学生和没有此类行为的学生。采用套索回归和逻辑回归来识别与NSSI相关的显著风险因素。随后，绘制了列线图以提高识别NSSI高危个体的准确性和效率。通过包括曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）在内的各种验证方法对模型性能进行评估。
NSSI的显著预测因素包括性别、问题行为、抑郁情绪和边缘型人格倾向。基于这些预测因素，构建了列线图。使用AUC、校准曲线和DCA对模型的准确性进行验证，结果显示准确性较高。
构建了职业高中学生NSSI的列线图预测工具，为预测青少年NSSI行为提供了一种准确、快速的方法。
