Liu Qi, Ren Yanjuan, Huang Yalin, Xie Ruyi, Li Yinyuan, Dong Zaiquan, Zhang Xiaoxia
West China School of Nursing, Sichuan University/Department of Mental Health Center, West China Hospital, Sichuan University, Chengdu, China.
Mental Health Center, West China Hospital, Sichuan University, Chengdu, China.
Sci Rep. 2025 Apr 30;15(1):15142. doi: 10.1038/s41598-025-00142-y.
Assessing non-suicidal self-injury (NSSI) in adolescents and young adults is a critical yet challenging aspect of psychiatric evaluations for hospitalized patients. This study aimed to develop a detective model for probability of NSSI in adolescents and young adults utilizing a retrospective cross-sectional analysis. Data from 658 hospitalized adolescents and young adults, including demographic characteristics, hormone levels, and violence risk factors, were collected. Age, history of suicide attempts, gender, and psychiatric diagnosis were identified as key detectors through Boruta and LASSO machine learning algorithms, leading to the construction of a nomogram. Model performance was evaluated based on discrimination, calibration, and decision curve analysis (DCA). The model achieved the area under the receiver operating characteristic curve (AUC) values of 0.803 (training set; 95% CI 0.763-0.843) and 0.745 (validation set; 95% CI 0.676-0.814). Calibration plots demonstrated strong alignment between predicted and actual outcomes. The Hosmer-Lemeshow test indicated good model fit, while DCA revealed clinically relevant threshold ranges for the training and validation sets, highlighting the model's potential to inform clinical decision-making. This detective model can support the rational allocation of medical resources and facilitate the early detection and intervention of NSSI behaviors in hospitalized adolescents and young adults.
评估青少年和青年的非自杀性自伤行为（NSSI）是住院患者精神科评估中一个关键但具有挑战性的方面。本研究旨在利用回顾性横断面分析，开发一种用于预测青少年和青年发生NSSI可能性的检测模型。收集了658名住院青少年和青年的数据，包括人口统计学特征、激素水平和暴力风险因素。通过Boruta和LASSO机器学习算法，确定年龄、自杀未遂史、性别和精神科诊断为关键检测指标，从而构建列线图。基于区分度、校准度和决策曲线分析（DCA）对模型性能进行评估。该模型在训练集上的受试者工作特征曲线（ROC）下面积（AUC）值为0.803（95%CI 0.763 - 0.843），在验证集上为0.745（95%CI 0.676 - 0.814）。校准图显示预测结果与实际结果高度一致。Hosmer-Lemeshow检验表明模型拟合良好，而DCA揭示了训练集和验证集具有临床意义的阈值范围，突出了该模型在为临床决策提供参考方面的潜力。这种检测模型可以支持医疗资源的合理分配，并有助于早期发现和干预住院青少年和青年的NSSI行为。
