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本文引用的文献

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Predicting Remission in Late-Life Major Depression: A Clinical Algorithm Based Upon Past Treatment History.预测老年期重度抑郁症缓解:基于既往治疗史的临床算法。
J Clin Psychiatry. 2019 Dec 10;80(6):18m12483. doi: 10.4088/JCP.18m12483.
2
Evaluation of Differences in Individual Treatment Response in Schizophrenia Spectrum Disorders: A Meta-analysis.精神分裂谱系障碍个体治疗反应差异的评估:一项荟萃分析。
JAMA Psychiatry. 2019 Oct 1;76(10):1063-1073. doi: 10.1001/jamapsychiatry.2019.1530.
3
Exploratory analyses of effect modifiers in the antidepressant treatment of major depression: Individual-participant data meta-analysis of 2803 participants in seven placebo-controlled randomized trials.探索性分析在重度抑郁症的抗抑郁治疗中的效应修饰因子:7 项安慰剂对照随机试验中 2803 名参与者的个体参与者数据荟萃分析。
J Affect Disord. 2019 May 1;250:419-424. doi: 10.1016/j.jad.2019.03.031. Epub 2019 Mar 6.
4
Personalized prediction of antidepressant v. placebo response: evidence from the EMBARC study.个体化预测抗抑郁药与安慰剂反应:来自 EMBARC 研究的证据。
Psychol Med. 2019 May;49(7):1118-1127. doi: 10.1017/S0033291718001708. Epub 2018 Jul 2.
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Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis.21 种抗抑郁药治疗成人重度抑郁症的急性治疗的疗效和可接受性比较:系统评价和网络荟萃分析。
Lancet. 2018 Apr 7;391(10128):1357-1366. doi: 10.1016/S0140-6736(17)32802-7. Epub 2018 Feb 21.
6
The potential of predictive analytics to provide clinical decision support in depression treatment planning.预测分析在抑郁症治疗规划中提供临床决策支持的潜力。
Curr Opin Psychiatry. 2018 Jan;31(1):32-39. doi: 10.1097/YCO.0000000000000377.
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Reevaluating the Efficacy and Predictability of Antidepressant Treatments: A Symptom Clustering Approach.重新评估抗抑郁治疗的疗效和可预测性:一种症状聚类方法。
JAMA Psychiatry. 2017 Apr 1;74(4):370-378. doi: 10.1001/jamapsychiatry.2017.0025.
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Comparative efficacy and acceptability of first-generation and second-generation antidepressants in the acute treatment of major depression: protocol for a network meta-analysis.第一代和第二代抗抑郁药在重度抑郁症急性治疗中的疗效和可接受性比较:网状Meta分析方案
BMJ Open. 2016 Jul 8;6(7):e010919. doi: 10.1136/bmjopen-2015-010919.
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Establishing moderators and biosignatures of antidepressant response in clinical care (EMBARC): Rationale and design.在临床护理中确定抗抑郁药反应的调节因素和生物标志物(EMBARC):原理与设计
J Psychiatr Res. 2016 Jul;78:11-23. doi: 10.1016/j.jpsychires.2016.03.001. Epub 2016 Mar 15.
10
Are personalised treatments of adult depression finally within reach?成人抑郁症的个性化治疗终于触手可及了吗?
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抗抑郁药反应的个体差异:安慰剂对照随机临床试验的荟萃分析

Individual Differences in Response to Antidepressants: A Meta-analysis of Placebo-Controlled Randomized Clinical Trials.

作者信息

Maslej Marta M, Furukawa Toshiaki A, Cipriani Andrea, Andrews Paul W, Mulsant Benoit H

机构信息

Centre for Addiction and Mental Health, Toronto, Ontario, Canada.

Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.

出版信息

JAMA Psychiatry. 2020 Jun 1;77(6):607-617. doi: 10.1001/jamapsychiatry.2019.4815.

DOI:10.1001/jamapsychiatry.2019.4815
PMID:32074273
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7042922/
Abstract

IMPORTANCE

Antidepressants are commonly used worldwide to treat major depressive disorder. Symptomatic response to antidepressants can vary depending on differences between individuals; however, this variability may reflect nonspecific or random factors.

OBJECTIVES

To investigate the assumption of systematic variability in symptomatic response to antidepressants and to assess whether this variability is associated with severity of major depressive disorder, antidepressant class, or year of study publication.

DATA SOURCES

Data used were from a recent network meta-analysis of acute treatment with licensed antidepressants in adults with major depressive disorder. The following databases were searched from inception to January 8, 2016: the Cochrane Central Register of Controlled Trials, CINAHL, Embase, LILACS database, MEDLINE, MEDLINE In-Process, and PsycINFO. Additional sources were international trial registries, drug approval agency websites, and key scientific journals.

STUDY SELECTION

Analysis was restricted to double-blind, randomized placebo-controlled trials with available data at the study's end point.

DATA EXTRACTION AND SYNTHESIS

Baseline and end point means, SDs, number of participants in each group, antidepressant class, and publication year were extracted. The data were analyzed between August 14 and November 18, 2019.

MAIN OUTCOMES AND MEASURES

With the use of validated methods, coefficients of variation were derived for antidepressants and placebo, and their ratios were calculated to compare outcome variability between antidepressant and placebo. Ratios were entered into a random-effects model, with the expectation that response to antidepressants would be more variable than response to placebo. Analysis was repeated after stratifying by baseline severity of depression, antidepressant class (selective serotonin reuptake inhibitors: citalopram, escitalopram, fluoxetine, fluvoxamine, paroxetine, sertraline, and vilazodone; serotonin and norepinephrine reuptake inhibitors: desvenlafaxine and venlafaxine; norepinephrine-dopamine reuptake inhibitor: bupropion; noradrenergic agents: amitriptyline and reboxetine; and other antidepressants: agomelatine, mirtazapine, and trazodone), and publication year.

RESULTS

In the 87 eligible randomized placebo-controlled trials (17 540 unique participants), there was significantly more variability in response to antidepressants than to placebo (coefficients of variation ratio, 1.14; 95% CI, 1.11-1.17; P < .001). Baseline severity of depression did not moderate variability in response to antidepressants. Variability in response to selective serotonin reuptake inhibitors was lower than variability in response to noradrenergic agents (coefficients of variation ratio, 0.88; 95% CI, 0.80-0.97; P = .01), as was the variability in response to other antidepressants compared with noradrenergic agents (coefficients of variation ratio, 0.87; 95% CI, 0.79-0.97; P = .001). Variability also tended to be lower in studies that were published more recently, with coefficients of variation changing by a value of 0.005 (95% CI, 0.002-0.008; P = .003) for every year a study is more recent.

CONCLUSIONS AND RELEVANCE

Individual differences may be systematically associated with responses to antidepressants in major depressive disorder beyond placebo effects or statistical factors. This study provides empirical support for identifying moderators and personalizing antidepressant treatment.

摘要

重要性

抗抑郁药在全球范围内广泛用于治疗重度抑郁症。对抗抑郁药的症状反应可能因个体差异而有所不同;然而,这种变异性可能反映了非特异性或随机因素。

目的

研究抗抑郁药症状反应存在系统变异性的假设,并评估这种变异性是否与重度抑郁症的严重程度、抗抑郁药类别或研究发表年份相关。

数据来源

所使用的数据来自最近一项针对成年重度抑郁症患者使用已获许可抗抑郁药进行急性治疗的网络荟萃分析。从数据库创建至2016年1月8日,检索了以下数据库:Cochrane对照试验中央登记册、护理学与健康领域数据库、Embase数据库、拉丁美洲及加勒比卫生科学数据库、医学期刊数据库、医学期刊数据库(正在处理中)和心理学文摘数据库。其他来源包括国际试验注册库、药品审批机构网站和主要科学期刊。

研究选择

分析仅限于在研究终点有可用数据的双盲、随机安慰剂对照试验。

数据提取与合成

提取基线和终点均值、标准差、每组参与者数量、抗抑郁药类别和发表年份。数据于2019年8月14日至11月18日进行分析。

主要结局与测量指标

使用经过验证的方法,得出抗抑郁药和安慰剂的变异系数,并计算其比值以比较抗抑郁药与安慰剂之间的结局变异性。将比值纳入随机效应模型,预期抗抑郁药的反应比安慰剂的反应更具变异性。在按抑郁症基线严重程度、抗抑郁药类别(选择性5-羟色胺再摄取抑制剂:西酞普兰、艾司西酞普兰、氟西汀、氟伏沙明、帕罗西汀、舍曲林和维拉唑酮;5-羟色胺和去甲肾上腺素再摄取抑制剂:度洛西汀和文拉法辛;去甲肾上腺素-多巴胺再摄取抑制剂:安非他酮;去甲肾上腺素能药物:阿米替林和瑞波西汀;以及其他抗抑郁药:阿戈美拉汀、米氮平和曲唑酮)和发表年份进行分层后,重复分析。

结果

在87项符合条件的随机安慰剂对照试验(17540名独立参与者)中,抗抑郁药的反应变异性显著高于安慰剂(变异系数比值为1.14;95%置信区间为1.11 - 1.17;P < 0.001)。抑郁症的基线严重程度并未调节抗抑郁药反应的变异性。选择性5-羟色胺再摄取抑制剂的反应变异性低于去甲肾上腺素能药物(变异系数比值为0.88;95%置信区间为0.80 - 0.97;P = 0.01),其他抗抑郁药与去甲肾上腺素能药物相比的反应变异性也是如此(变异系数比值为0.87;95%置信区间为0.79 - 0.97;P = 0.001)。在较近期发表的研究中,变异性也往往较低,每推迟一年发表研究,变异系数变化值为0.005(95%置信区间为0.002 - 0.008;P = 0.003)。

结论与意义

个体差异可能系统性地与重度抑郁症中抗抑郁药的反应相关,这超出了安慰剂效应或统计因素。本研究为识别调节因素和个性化抗抑郁治疗提供了实证支持。