重度抑郁障碍患者自杀未遂的预测模型及 EPHX2 的贡献:一项试验性综合机器学习研究

IF 4.7 2区 医学 Q1 PSYCHIATRY Depression and Anxiety Pub Date : 2024-05-09 DOI:10.1155/2024/5538257
Shuqiong Zheng, Weixiong Zeng, Qianyun Wu, Weimin Li, Zilong He, Enze Li, Chong Tang, Xiang Xue, Genggeng Qin, Bin Zhang, Honglei Yin
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引用次数: 0

摘要

自杀是一个重大的公共卫生问题,由各种因素的复杂相互作用造成。重度抑郁障碍(MDD)是与自杀相关的最普遍的精神疾病;因此,必须优先考虑这一人群的自杀预测和预防工作。要提高预测模型的性能,就必须整合来自不同方面的信息,包括人格、认知功能、社会和遗传因素。此外,最近的研究表明,EPHX2/P2X2 在 MDD 的病理生理学中起着关键作用。我们之前的研究发现 EPHX2 和 P2X2 与 MDD 患者自杀有关。本研究旨在:(1)建立综合信息的预测模型,以区分 MDD 和健康志愿者;(2)估计 MDD 的自杀风险;(3)确定 EPHX2/P2X2 的贡献。这项横断面研究的对象是 472 名前瞻性收集的参与者。研究采用了极端梯度提升(XGBoost)分类器的机器学习(ML)技术,以评估提取的特征在识别 MDD 患者和抑郁自杀企图者(DSA)方面的性能和相对重要性。在独立验证集中,包含临床和认知信息的模型可以识别 MDD,其接收者工作特征曲线下面积(AUC)为 0.938(95% 置信区间(CI),0.898-0.977),而遗传信息并没有提高分类性能。与仅有临床信息的模型相比,包含临床、认知和遗传信息的模型在识别DSA方面的AUC明显更高,为0.801(95% CI,0.719-0.884),其中EPHX2的三个单核苷酸多态性显示了重要作用。本研究成功地建立了逐步预测 MDD 患者自杀未遂风险的 ML 模型。我们发现,EPHX2有助于提高自杀预测模型的性能。该试验已在 NCT05575713 上注册。
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Predictive Models for Suicide Attempts in Major Depressive Disorder and the Contribution of EPHX2: A Pilot Integrative Machine Learning Study

Suicide is a major public health problem caused by a complex interaction of various factors. Major depressive disorder (MDD) is the most prevalent psychiatric disorder associated with suicide; therefore, it is essential to prioritize suicide prediction and prevention within this population. Integrated information from different dimensions, including personality, cognitive function, and social and genetic factors, is necessary to improve the performance of predictive models. Besides, recent studies have indicated the critical roles for EPHX2/P2X2 in the pathophysiology of MDD. Our previous studies found an association of EPHX2 and P2X2 with suicide in MDD. This study is aimed at (1) establishing predictive models with integrated information to distinguish MDD from healthy volunteers, (2) estimating the suicide risk of MDD, and (3) determining the contribution of EPHX2/P2X2. This cross-sectional study was conducted on 472 prospectively collected participants. The machine learning (ML) technique using Extreme Gradient Boosting (XGBoost) classifier was employed to evaluate the performance and relative importance of the extracted characteristics in recognising patients with MDD and depressed suicide attempters (DSA). In independent validation set, the model with clinical and cognitive information could recognise MDD with an area under the receiver operating characteristic curve (AUC) of 0.938 (95% confidence interval (CI), 0.898–0.977), and genetic information did not improve classification performance. The model with clinical, cognitive, and genetic information resulted in a significantly higher AUC of 0.801 (95% CI, 0.719–0.884) for identifying DSA than the model with only clinical information, in which the three single nucleotide polymorphisms of EPHX2 showed important roles. This study successfully established step-by-step predictive ML models to estimate the risk of suicide attempts in MDD. We found that EPHX2 can help improve the performance of suicidal predictive models. This trial is registered with NCT05575713.

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来源期刊
Depression and Anxiety
Depression and Anxiety 医学-精神病学
CiteScore
15.00
自引率
1.40%
发文量
81
审稿时长
4-8 weeks
期刊介绍: Depression and Anxiety is a scientific journal that focuses on the study of mood and anxiety disorders, as well as related phenomena in humans. The journal is dedicated to publishing high-quality research and review articles that contribute to the understanding and treatment of these conditions. The journal places a particular emphasis on articles that contribute to the clinical evaluation and care of individuals affected by mood and anxiety disorders. It prioritizes the publication of treatment-related research and review papers, as well as those that present novel findings that can directly impact clinical practice. The journal's goal is to advance the field by disseminating knowledge that can lead to better diagnosis, treatment, and management of these disorders, ultimately improving the quality of life for those who suffer from them.
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