Age as a moderating factor of treatment resistance in depression

年龄作为抑郁症治疗抵抗的调节因素

阅读:1

Abstract

BACKGROUND: Treatment-resistant depression (TRD) is an important clinical challenge and may present differently between age groups. METHODS: A total of 893 depressed patients recruited within the framework of the European research consortium "Group for the Studies of Resistant Depression" were assessed by generalized linear models regarding age effects (both as numerical and factorial predictors) on treatment outcome, number of lifetime depressive episodes, hospitalization time, and duration of the current episode. Effects of age as numerical predictor on the severity of common depressive symptoms, measured with Montgomery-Åsberg Depression Rating Scale (MADRS) for two-time points, were assessed by linear mixed models, respectively, for patients showing TRD and treatment response. A corrected p threshold of 0.001 was applied. RESULTS: Overall symptom load reflected by MADRS (p < 0.0001) and lifetime hospitalization time (p < 0.0001) increased with age in TRD patients but not treatment responders. In TRD, higher age was predicting symptom severity of inner tension, reduced appetite, concentrations difficulties, and lassitude (all p ≤ 0.001). Regarding clinical significance, older TRD patients were more likely to report severe symptoms (item score > 4) for these items both before and after treatment (all p ≤ 0.001). CONCLUSIONS: In this naturalistic sample of severely ill depressed patients, antidepressant treatment protocols were equally effective in addressing TRD in old age. However, specific symptoms such as sadness, appetite, and concentration showed an age-dependent presentation, impacting residual symptoms in severely affected TRD patients and calling for a precision approach by a better integration of age profiles in treatment recommendations.

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。