青少年抑郁障碍患者非自杀性自伤行为发生风险的列线图预测模型构建

Construction of a nomogram prediction model for the risk of non-suicidal self-injury behaviors in adolescents with depressive disorder

中文关键词:  青少年  抑郁障碍  非自杀性自伤  危险因素  预测模型  列线图

英文关键词:Adolescents  Depressive disorder  Non-suicidal self-injury  Risk factors  Prediction model  Nomogram

中文摘要:

      背景 非自杀性自伤（NSSI）已成为广泛关注的公共卫生问题。青少年抑郁障碍患者NSSI行为发生风险较高。既往研究多集中于NSSI行为的相关因素分析，而面向临床快速评估、兼具直观量化特征的可视化模型构建的研究较为有限。目的 考查青少年抑郁障碍患者NSSI行为的危险因素，并构建NSSI行为发生风险的列线图预测模型，以期为临床快速评估此类患者NSSI行为的发生风险提供量化工具。方法 采用横断面调查，纳入2025年2月—12月于兰州大学第二医院心理卫生科就诊、符合《精神障碍诊断与统计手册（第5版）》（DSM-5）抑郁障碍诊断标准的青少年患者448例，按7∶3随机分为建模组（n=313例）和验证组（n=135）。采用抑郁自评量表（SDS）、焦虑自评量表（SAS）、Barratt冲动性量表第11版（BIS-11）、童年创伤问卷简版（CTQ-SF）及匹兹堡睡眠质量指数量表（PSQI）进行评定。采用二元Logistic回归分析筛选建模组NSSI行为的独立预测因子，并构建列线图模型。采用受试者工作特征（ROC）曲线、校准曲线和决策曲线分析（DCA）分别评定该模型的区分度、准确度及临床应用价值。结果 二元Logistic回归分析结果显示，年龄较小、女生、SDS评分较高、BIS-11评分较高、CTQ-SF评分较高、PSQI评分较高是青少年抑郁障碍患者发生NSSI行为的危险因素（OR=0.816、2.671、1.131、1.030、1.064、1.102，P<0.05或0.01）。基于上述6个指标构建的列线图模型，Hosmer-Lemeshow拟合优度检验显示其拟合度较好（χ²=15.144，P=0.056）。内部验证及外部验证结果显示，建模组AUC为0.895，验证组AUC为0.891；校准曲线中的实际值与预测值之间的平均绝对误差分别为0.043和0.023；DCA结果显示，当预测风险阈值大于0.11时，模型具有明确的临床净收益。结论 基于年龄、性别、抑郁症状严重程度、冲动性、童年创伤、睡眠质量6个指标构建的青少年抑郁障碍患者NSSI行为发生风险的列线图预测模型具有较高的区分度、准确度和临床应用价值。

英文摘要:

      Background Non-suicidal self-injury （NSSI） has become a public health concern of wide interest. Adolescents with depressive disorder are at high risk of NSSI behaviors. Previous studies have mostly focused on analyzing factors associated with NSSI behaviors， while research on developing visual models that are intuitive， quantifiable， and suitable for rapid clinical assessment is relatively limited.Objective To investigate the risk factors for NSSI behaviors in adolescents with depressive disorder， and to construct a nomogram prediction model for the risk of NSSI behaviors occurrence， so as to provide a quantitative tool for clinical rapid assessment of the occurrence risk of NSSI behaviors in this population.Methods A cross-sectional study was conducted. A total of 448 adolescent patients diagnosed with depressive disorder who attended the Mental Health Department of The Second Hospital， Lanzhou University from February to December 2025 and met the diagnostic criteria for depressive disorder as specified in the Diagnostic and Statistical Manual of Mental Disorders， fifth edition （DSM-5） were enrolled. All participants were randomly divided into the modeling group （n=313） and the validation group （n=135） at a ratio of 7∶3. Assessments were performed using the Self-rating Depression Scale （SDS）， the Self-rating Anxiety Scale （SAS）， the Barratt Impulsiveness Scale-11 （BIS-11）， the Childhood Trauma Questionnaire-short form （CTQ-SF）， and the Pittsburgh Sleep Quality Index （PSQI）. Binary Logistic regression analysis was applied to screen independent predictors of NSSI behaviors in the modeling group， and a nomogram was constructed accordingly. The receiver operating characteristic （ROC） curve， calibration curve， and decision curve analysis （DCA） were used to evaluate the discrimination， calibration and clinical utility of the model， respectively.Results Binary Logistic regression analysis revealed that younger age， female sex， higher SDS score， higher BIS-11 score， higher CTQ-SF score， and higher PSQI score were risk factors for NSSI behaviors in adolescent patients with depressive disorder （OR=0.816， 2.671， 1.131， 1.030， 1.064， 1.102， P<0.05 or 0.01）. The Hosmer-Lemeshow test of the nomogram prediction model constructed based on these six indicators showed a good fit （χ2=15.144， P=0.056）. Model validation showed that the AUC was 0.895 for the modeling group and 0.891 for the validation group. The calibration curves suggested that the mean absolute errors between the actual values and the predicted values were 0.043 and 0.023， respectively. Decision curve analysis indicated that the model provided a clear net clinical benefit when the risk threshold was greater than 0.11.Conclusion The nomogram model based on six indicators （age， sex， depressive symptom severity， impulsivity， childhood trauma， and sleep quality） for predicting NSSI risk in adolescent patients with depressive disorder exhibits good discrimination， calibration， and clinical application value. ［Funded by National Natural Science Foundation of China （number， 82560281）； Gansu Provincial Science and Technology Program （number， 25JRRA618）； Gansu Provincial Health Industry Research Project （number， GSWSKY2025-13）； The College Students' Innovation and Entrepreneurship Training Program of the Second Clinical Medical College of Lanzhou University （number， 20250050060）］