摘要
目的:大学生非自杀性自伤(non-suicidal self-injury,NSSI)行为已成为重要的公共卫生问题,需建立有效的早期识别工具。本研究旨在基于最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)回归分析方法构建预测大学生NSSI行为的预测模型。方法:2022年4至6月期间,通过线上平台对湖南、江西、湖北、山东、广东和吉林6个省份在校大学生进行问卷调查。收集大学生的一般社会学人口资料,使用青少年非自杀性自伤行为评定问卷、患者健康问卷-9、愤怒反刍思维量表、多重形式暴力量表、儿童期虐待问卷简版及简式版社区心理体验评估问卷进行调查。通过LASSO回归分析筛选出大学生NSSI行为的预测因素,构建大学生NSSI行为的预测模型并绘制列线图。采用受试者操作特征(receiver operating characteristic,ROC)曲线和校准曲线对预测模型的区分度和校准度进行评估。结果:本研究共4 121名大学生参与,其中650名大学生存在NSSI行为,检出率为15.8%。LASSO回归分析结果显示:小学受欺凌经历、饮酒史、抑郁情绪、愤怒反刍思维和精神病样体验是大学生NSSI行为的预测因素。预测模型显示:大学生NSSI行为的发生风险=小学受欺凌经历×0.41+饮酒史×0.76+抑郁情绪×0.08+愤怒反刍思维×0.04+精神病样体验×0.05。ROC曲线结果表明:预测模型在训练集中的曲线下面积(area under the curve,AUC)为0.782,在测试集中AUC为0.769。校准曲线显示模型的预测值与实际值基本一致。结论:本研究构建的预测模型具有较好的预测能力,并通过列线图实现模型结果可视化呈现。该预测模型能够根据大学生NSSI行为的危险因素评估其风险,帮助临床医师或教育者及时发现高危人群并进行早期干预。
Objective: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 4121 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.Conclusion: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.
作者
唐诗娇
颜楚涵
林晨希
刘小群
TANG Shijiao;YAN Chuhan;LIN Chenxi;LIU Xiaoqun(Department of Maternal and Child Health,Xiangya School of Public Health,Central South University,Changsha 410013,China)
出处
《中南大学学报(医学版)》
北大核心
2025年第9期1483-1494,共12页
Journal of Central South University (Medical Science)
基金
湖南省研究生科研创新项目(CX20230322)
湖南省基础教育教学改革研究项目(Z2024171)。
关键词
非自杀性自伤行为
大学生
预测模型
最小绝对收缩和选择算子回归
心理风险评估
non-suicidal self-injury behavior
college students
predictive model
least absolute shrinkage and selection operator regression
psychological risk assessment
