青少年抑郁障碍患者非自杀性自我伤害诊断性预测模型的建立及验证
Construction and verification of depressive disorder in adolescent diagnostic prediction model in patients with non-suicidal self-injury.
投稿时间:2022-08-11  修订日期:2023-02-24
DOI:
中文关键词:  抑郁障碍  非自杀性自我伤害  青少年  住院患者  诊断性临床预测模型
英文关键词:Depressive disorder  Non-suicidal self-injury  adolescent  inpatients  Diagnostic clinical prediction model
基金项目:深圳市医学重点学科建设经费资助(项目编号:SZXK041)
作者单位地址
李 畅 康宁医院 广东省深圳市罗湖区翠竹街道 康宁医院
张迎黎* 康宁医院 广东省深圳市罗湖区翠竹街道 康宁医院
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中文摘要:
      目的 建立青少年抑郁障碍患者非自杀性自伤(NSSI)行为诊断性预测模型,以期对青少年抑郁障碍患者NSSI行为的早期识别提供参考。方法 回顾性分析2021年1月1日-12月31日在深圳市康宁医院儿少科住院的抑郁障碍患者(n=366)临床资料。根据《精神障碍诊断与统计手册(第5版)》(DSM-5)诊断标准,将患者分为伴NSSI行为组(n=289)和不伴NSSI行为组(n=77)。将366例患者按7:3随机分为(n=258)和验证集(n=108)。对Logistic回归分析筛选出的自变量,建立预测青少年抑郁障碍患者NSSI行为的预测模型。使用受试者工作特征(ROC)曲线下面积(AUC)评估训练集和验证集模型的区分度。使用校准曲线评估训练集和验证集模型的校准度。使用Homser-Lemeshow(HL)拟合优度检验评估模型校准度,使用决策曲线分析(DCA)评价预测模型的临床获益情况。结果 性别(β=1.734,OR=5.561,95% CI:2.678~11.964)、受教育程度(β=0.864,OR=2.737,95% CI:1.174~4.795)、自杀未遂史(β=0.932,OR=2.539,95% CI:1.253~5.144)、独生子女(β=0.745,OR=2.106,95% CI:1.029~4.311)、抑郁严重程度(β=0.056,OR=1.058,95% CI:1.025~1.092)是青少年抑郁障碍患者NSSI行为的独立危险因素(P均<0.05)。ROC曲线训练集AUC=0.808(95% CI:0.746~0.870),验证集AUC=0.722(95% CI:0.581~0.864)。HL检验评估校准图后显示出较好的拟合度(P=0.561)。结论 性别、受教育程度、自杀未遂史、独生子女、抑郁严重程度是青少年抑郁障碍患者NSSI行为的独立危险因素,构建的青少年抑郁障碍患者NSSI行为的诊断性临床预测模型具有良好的敏感性和特异性。
英文摘要:
      Objective To establish a diagnostic prediction model for non-suicidal self-injury (NSSI) in adolescents with depressive disorder, in order to provide ideas for early identification of NSSI in adolescents with depressive disorder. Methods The clinical data of 366 hospitalized patients with depression disorder in the Pediatric Department of Shenzhen Kangning Hospital from January 1, 2021 to December 31, 2021 were retrospectively collected. According to the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) diagnostic criteria, the patients were divided into two groups: the group with NSSI (n=289) and the group without NSSI (n=77). 366 patients were randomly divided into training set and validation set in 7:3 ratio. The independent variables screened by Logistic regression (P<0.05) were used to establish a predictive model for NSSI in adolescents with depressive disorder. Finally, the area under receiver Operating characteristic (ROC) curve (AUC) was used to evaluate the discrimination between the training set and validation set models. Calibration curves were used to evaluate the calibration degree of the training set and verification set models. The Goodness of fit test (HL) was used to evaluate the accuracy of the calibration degree of the model.Decision curve analysis (DCA) was used to evaluate the clinical benefit of the prediction model. Results Finally, gender (β=1.734, OR=5.561, 95% CI: 2.678~11.964), grade (β=0.864, OR=2.737, 95% CI: 1.174~4.795), history of suicide attempts (β=0.932, OR=2.539, 95% CI: 1.253~5.144), only child (β=0.745, OR=2.106, 95% CI: 1.029~4.311), depression severity (β=0.056, OR=1.058, 95% CI: 1.025~1.092) and four independent risk factors related to NSSI behavior in adolescents with depressive disorder (P<0.05). ROC curve training set AUC was 0.808 (95% CI: 0.746~0.870). Validation set AUC was 0.722 (95% CI: 0.581~0.864). HL test evaluated the prediction results of calibration chart showed a good fit (P=0.561). Conclusion Gender, education level, suicide attempt history, only child, and depression severity are independent risk factors for NSSI behavior in adolescents with depressive disorder. The constructed diagnostic clinical prediction model for NSSI behavior in adolescents with depressive disorder has good sensitivity and specificity.
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