Beijing Huilonguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China; North China University of Science and Technology, Tangshan 063210, China.
Ganzhou Third People's Hospital No. 10, Jiangbei Avenue, Zhanggong District, Ganzhou, Jiangxi 341000, China.
Asian J Psychiatr. 2024 Jul;97:104088. doi: 10.1016/j.ajp.2024.104088. Epub 2024 May 20.
Suicide attempts (SA) are a significant contributor to suicide deaths, and non-suicidal self-injury (NSSI) can increase the risk of SA. Many adolescents experience both NSSI and SA, which are affected by various factors. This study aimed to identify the risk factors and essential warning signs of SA, establish a predictive model for SA using multiple dimensions and large samples, and provide a multidimensional perspective for clinical diagnosis and intervention.
A total of 9140 participants aged 12-18 years participated in an online survey; 6959 participants were included in the statistical analysis. A multilayer perceptron algorithm was used to establish a prediction model for adolescent SA (with or without); adolescents with NSSI behavior were extracted as a subgroup to establish a prediction model.
Both the prediction model performance of the SA group and the NSSI-SA subgroup were strong, with high accuracy, and AUC values of 0.93 and 0.88, indicating good discrimination. Decision curve analysis (DCA) demonstrated that the clinical intervention value of the prediction results was high and that the clinical intervention benefits of the NSSI-SA subgroup were greater than those of the SA group.
Our study demonstrated that the predictive model has a high degree of accuracy and discrimination, thereby identifying significant factors associated with adolescent SA. As long as adolescents exhibit NSSI behavior, relative suicide interventions should be implemented to prevent future hazards. This study can provide guidance and more nuanced insights for clinical diagnosis as well as a foundation for clinical treatment.
自杀未遂（SA）是自杀死亡的重要原因，非自杀性自伤（NSSI）会增加 SA 的风险。许多青少年同时经历 NSSI 和 SA，这受到多种因素的影响。本研究旨在确定 SA 的风险因素和基本预警信号，使用多个维度和大样本建立 SA 的预测模型，并为临床诊断和干预提供多维视角。
共有 9140 名 12-18 岁的参与者参加了在线调查；其中 6959 名参与者被纳入统计分析。使用多层感知机算法建立青少年 SA（有或无）的预测模型；提取有 NSSI 行为的青少年作为亚组建立预测模型。
SA 组和 NSSI-SA 亚组的预测模型性能均较强，具有较高的准确性和 AUC 值分别为 0.93 和 0.88，表明具有良好的区分度。决策曲线分析（DCA）表明预测结果的临床干预价值较高，且 NSSI-SA 亚组的临床干预获益大于 SA 组。
本研究表明，该预测模型具有较高的准确性和区分度，可识别与青少年 SA 相关的重要因素。只要青少年表现出 NSSI 行为，就应实施相对自杀干预，以预防未来的危害。本研究可为临床诊断提供指导和更细致的见解，并为临床治疗奠定基础。
