Liang Sixiang, Zhang Jinhe, Zhao Qian, Wilson Amanda, Huang Juan, Liu Yuan, Shi Xiaoning, Sha Sha, Wang Yuanyuan, Zhang Ling
Beijing Key Laboratory of Mental Disorders, The National Clinical Research Center for Mental Disorders, The Advanced Innovation Center for Human Brain Protection, Beijing Anding Hospital, Capital Medical University, Beijing, China.
Peking University HuiLongGuan Clinical Medical School, Beijing HuiLongGuan Hospital, Beijing, China.
Front Psychiatry. 2021 Jun 23;12:644038. doi: 10.3389/fpsyt.2021.644038. eCollection 2021.
Major depressive disorder (MDD) is often associated with suicidal attempt (SA). Therefore, predicting the risk factors of SA would improve clinical interventions, research, and treatment for MDD patients. This study aimed to create a nomogram model which predicted correlates of SA in patients with MDD within the Chinese population. A cross-sectional survey among 474 patients was analyzed. All subjects met the diagnostic criteria of MDD according to the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10). Multi-factor logistic regression analysis was used to explore demographic information and clinical characteristics associated with SA. A nomogram was further used to predict the risk of SA. Bootstrap re-sampling was used to internally validate the final model. Integrated Discrimination Improvement (IDI) and Akaike Information Criteria (AIC) were used to evaluate the capability of discrimination and calibration, respectively. Decision Curve Analysis (DCA) and the Receiver Operating Characteristic (ROC) curve was also used to evaluate the accuracy of the prediction model. Multivariable logistic regression analysis showed that being married (OR = 0.473, 95% CI: 0.240 and 0.930) and a higher level of education (OR = 0.603, 95% CI: 0.464 and 0.784) decreased the risk of the SA. The higher number of episodes of depression (OR = 1.854, 95% CI: 1.040 and 3.303) increased the risk of SA in the model. The C-index of the nomogram was 0.715, with the internal (bootstrap) validation sets was 0.703. The Hosmer-Lemeshow test yielded a -value of 0.33, suggesting a good fit of the prediction nomogram in the validation set. Our findings indicate that the demographic information and clinical characteristics of SA can be used in a nomogram to predict the risk of SA in Chinese MDD patients.
重度抑郁症（MDD）常与自杀未遂（SA）相关。因此，预测自杀未遂的风险因素将改善对MDD患者的临床干预、研究和治疗。本研究旨在创建一个列线图模型，以预测中国人群中MDD患者自杀未遂的相关因素。对474例患者进行了横断面调查分析。所有受试者均符合《国际疾病分类及相关健康问题统计分类第十次修订版》（ICD - 10）的MDD诊断标准。采用多因素逻辑回归分析来探索与自杀未遂相关的人口统计学信息和临床特征。进一步使用列线图来预测自杀未遂的风险。采用Bootstrap重抽样对最终模型进行内部验证。分别使用综合判别改善（IDI）和赤池信息准则（AIC）来评估判别能力和校准能力。决策曲线分析（DCA）和受试者工作特征（ROC）曲线也用于评估预测模型的准确性。多变量逻辑回归分析显示，已婚（OR = 0.473，95% CI：0.240和0.930）和较高的教育水平（OR = 0.603，95% CI：0.464和0.784）可降低自杀未遂的风险。在模型中，抑郁发作次数较多（OR = 1.854，9与5% CI：1.040和3.303）会增加自杀未遂的风险。列线图的C指数为0.715，内部（Bootstrap）验证集为0.703。Hosmer - Lemeshow检验的P值为0.33，表明预测列线图在验证集中拟合良好。我们的研究结果表明，自杀未遂的人口统计学信息和临床特征可用于列线图，以预测中国MDD患者自杀未遂的风险。
