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围产期抑郁症的最佳风险与诊断评估策略:来自Life-ON研究队列的机器学习方法

Optimal risk and diagnosis assessment strategies in perinatal depression: A machine learning approach from the life-ON study cohort.

作者信息

D'Agostino Armando, Garbazza Corrado, Malpetti Daniele, Azzimonti Laura, Mangili Francesca, Stein Hans-Christian, Del Giudice Renata, Cicolin Alessandro, Cirignotta Fabio, Manconi Mauro

机构信息

Department of Health Sciences, Università degli Studi di Milano, Italy; Department of Mental Health and Addiction, ASST Santi Paolo e Carlo, Milan, Italy.

Centre for Chronobiology, University of Basel, Basel, Switzerland; Transfaculty Research Platform Molecular and Cognitive Neurosciences, University of Basel, Basel, Switzerland; Sleep Medicine Unit, Neurocenter of Southern Switzerland, Lugano, Switzerland.

出版信息

Psychiatry Res. 2024 Feb;332:115687. doi: 10.1016/j.psychres.2023.115687. Epub 2023 Dec 24.

DOI:10.1016/j.psychres.2023.115687
PMID:38157709
Abstract

This study aimed to assess the concordance of various psychometric scales in detecting Perinatal Depression (PND) risk and diagnosis. A cohort of 432 women was assessed at 10-15th and 23-25th gestational weeks, 33-40 days and 180-195 days after delivery using the Edinburgh Postnatal Depression Scale (EPDS), Visual Analogue Scale (VAS), Hamilton Depression Rating Scale (HDRS), Montgomery-Åsberg Depression Rating Scale (MADRS), and Mini International Neuropsychiatric Interview (MINI). Spearman's rank correlation coefficient was used to assess agreement across instruments, and multivariable classification models were developed to predict the values of a binary scale using the other scales. Moderate agreement was shown between the EPDS and VAS and between the HDRS and MADRS throughout the perinatal period. However, agreement between the EPDS and HDRS decreased postpartum. A well-performing model for the estimation of current depression risk (EPDS > 9) was obtained with the VAS and MADRS, and a less robust one for the estimation of current major depressive episode (MDE) diagnosis (MINI) with the VAS and HDRS. When the EPDS is not feasible, the VAS may be used for rapid and comprehensive postpartum screening with reliability. However, a thorough structured interview or clinical examination remains necessary to diagnose a MDE.

摘要

本研究旨在评估各种心理测量量表在检测围产期抑郁症(PND)风险和诊断方面的一致性。对432名女性组成的队列在孕10 - 15周、孕23 - 25周、产后33 - 40天以及产后180 - 195天使用爱丁堡产后抑郁量表(EPDS)、视觉模拟量表(VAS)、汉密尔顿抑郁评定量表(HDRS)、蒙哥马利 - 阿斯伯格抑郁评定量表(MADRS)和迷你国际神经精神病学访谈(MINI)进行评估。采用斯皮尔曼等级相关系数评估各工具之间的一致性,并开发多变量分类模型,使用其他量表预测二元量表的值。在整个围产期,EPDS与VAS之间以及HDRS与MADRS之间显示出中度一致性。然而,产后EPDS与HDRS之间的一致性降低。使用VAS和MADRS获得了一个用于估计当前抑郁风险(EPDS > 9)的性能良好的模型,使用VAS和HDRS获得了一个用于估计当前重度抑郁发作(MDE)诊断(MINI)的稳健性较差的模型。当EPDS不可行时,VAS可用于产后快速且全面的筛查,且具有可靠性。然而,诊断MDE仍需要进行全面的结构化访谈或临床检查。

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Optimal risk and diagnosis assessment strategies in perinatal depression: A machine learning approach from the life-ON study cohort.围产期抑郁症的最佳风险与诊断评估策略:来自Life-ON研究队列的机器学习方法
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A method for predicting postpartum depression via an ensemble neural network model.一种通过集成神经网络模型预测产后抑郁症的方法。
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