Antidepressant response trajectories and quantitative electroencephalography (QEEG) biomarkers in major depressive disorder

重度抑郁症的抗抑郁药反应轨迹和定量脑电图(QEEG)生物标志物

阅读:1

Abstract

Individuals with Major Depressive Disorder (MDD) vary regarding the rate, magnitude and stability of symptom changes during antidepressant treatment. Growth mixture modeling (GMM) can be used to identify patterns of change in symptom severity over time. Quantitative electroencephalographic (QEEG) cordance within the first week of treatment has been associated with endpoint clinical outcomes but has not been examined in relation to patterns of symptom change. Ninety-four adults with MDD were randomized to eight weeks of double-blinded treatment with fluoxetine 20mg or venlafaxine 150mg (n=49) or placebo (n=45). An exploratory random effect GMM was applied to Hamilton Depression Rating Scale (Ham-D(17)) scores over 11 timepoints. Linear mixed models examined 48-h, and 1-week changes in QEEG midline-and-right-frontal (MRF) cordance for subjects in the GMM trajectory classes. Among medication subjects an estimated 62% of subjects were classified as responders, 21% as non-responders, and 17% as symptomatically volatile-i.e., showing a course of alternating improvement and worsening. MRF cordance showed a significant class-by-time interaction (F((2,41))=6.82, p=.003); as hypothesized, the responders showed a significantly greater 1-week decrease in cordance as compared to non-responders (mean difference=-.76, Std. Error=.34, df=73, p=.03) but not volatile subjects. Subjects with a volatile course of symptom change may merit special clinical consideration and, from a research perspective, may confound the interpretation of typical binary endpoint outcomes. Statistical methods such as GMM are needed to identify clinically relevant symptom response trajectories.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。