Yang Wanqiu, Wang Xiaohong, Kang Chuanyi, Yang Liying, Liu Di, Zhao Na, Zhang Xiangyang
School of Ethnology and Sociology, Yunnan University, Kunming, China; School of Medicine, Yunnan University, Kunming, China.
Department of Psychiatry, The First Affiliated Hospital of Harbin Medical University, Harbin, China.
Asian J Psychiatr. 2023 Oct;88:103732. doi: 10.1016/j.ajp.2023.103732. Epub 2023 Aug 11.
Suicide is common in patients with major depressive disorder (MDD) and has serious consequences for individuals and families. This study aims to establish a risk prediction model for suicide attempts in MDD patients to make the detection of suicide risk more accurate and effective.
A cross-sectional survey, clinical examination, and biochemical indicator tests were performed on 1718 first-episode and drug naïve patients with major depressive disorder. We used Machine Learning to establish a risk prediction model for suicide attempts in FEDN patients with MDD.
Five predictors were identified by LASSO regression analysis from a total of 20 variables studied, namely psychotic symptoms, anxiety symptoms, thyroid peroxidase antibodies (ATPO), total cholesterol (TC), and high-density lipoprotein-cholesterol (HDL-C). The model constructed using the five predictors displayed moderate predictive ability, with an area under the ROC of 0.771 in the training set and 0.720 in the validation set. The DCA curve showed that the nomogram could be applied clinically if the risk threshold was between 22 % and 60 %. The risk threshold was found to be between 20 % and 60 % in external validation.
Introducing psychotic symptoms, anxiety symptoms, ATPO, TC, and HDL-C to the risk nomogram increased its usefulness for predicting suicide risk in patients with MDD. It may be useful in clinical decision-making or in discussions with patients, especially in crisis interventions.
自杀在重度抑郁症（MDD）患者中很常见，对个人和家庭都会造成严重后果。本研究旨在建立一个MDD患者自杀未遂的风险预测模型，以使自杀风险检测更加准确和有效。
对1718例首发且未服用过药物的重度抑郁症患者进行横断面调查、临床检查和生化指标检测。我们使用机器学习为患有MDD的首发抑郁症患者建立自杀未遂的风险预测模型。
通过LASSO回归分析从总共20个研究变量中确定了5个预测因素，即精神病性症状、焦虑症状、甲状腺过氧化物酶抗体（ATPO）、总胆固醇（TC）和高密度脂蛋白胆固醇（HDL-C）。使用这5个预测因素构建的模型显示出中等预测能力，训练集中ROC曲线下面积为0.771，验证集中为0.720。DCA曲线表明，如果风险阈值在22%至60%之间，该列线图可用于临床。在外部验证中发现风险阈值在20%至60%之间。
将精神病性症状、焦虑症状、ATPO、TC和HDL-C纳入风险列线图可提高其对MDD患者自杀风险的预测效用。它可能有助于临床决策或与患者的讨论，特别是在危机干预中。
