Literature DB >> 30878654

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.

Hisashi Noma1, Toshi A Furukawa2, Kazushi Maruo3, Hissei Imai4, Kiyomi Shinohara5, Shiro Tanaka6, Kazutaka Ikeda7, Shigeto Yamawaki8, Andrea Cipriani9.   

Abstract

BACKGROUND: It is clinically important to know who are likely to respond more or less to antidepressants. However, meaningful effect modifiers (variables associated with differential response depending on the treatment) are yet to be identified.
METHODS: We conducted individual participant data (IPD) meta-analysis and meta-regression to explore effect modifiers in placebo-controlled antidepressant trials conducted so far in Japan.
RESULTS: We obtained access to IPD from seven placebo-controlled trials comparing bupropion, duloxetine, escitalopram, mirtazapine, paroxetine or venlafaxine with placebo in the acute phase treatment of major depression (total n = 2803). The higher the guilt subscale score of the baseline Hamilton Rating Scale for Depression (HRSD), the greater the difference in reduction in depression severity between the antidepressants and placebo at week 6, while the older the current age or the age at onset, the smaller the difference. At week 8, the guilt subscale score of HRSD and presence of suicidal ideation at baseline predicted greater, and the anhedonia subscale and insomnia subscale scores of HRSD and early response at week 2 predicted smaller, difference in reduction. LIMITATIONS: Different studies measured different sets of baseline variables and we were able to analyze only a limited set of candidate variables for effect modification.
CONCLUSION: Age, age at onset, several HRSD subscales including guilt, anhedonia and insomnia, presence of suicidal ideation at baseline and early response are potential effect modifiers for response to antidepressants in the acute phase antidepressant treatment of major depression. Future trials need to measure these and additional variables in concerted efforts to enable matching of treatments with individual characteristics in depression.
Copyright © 2019 Elsevier B.V. All rights reserved.

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Year:  2019        PMID: 30878654     DOI: 10.1016/j.jad.2019.03.031

Source DB:  PubMed          Journal:  J Affect Disord        ISSN: 0165-0327            Impact factor:   4.839


  9 in total

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

Authors:  Marta M Maslej; Toshiaki A Furukawa; Andrea Cipriani; Paul W Andrews; Benoit H Mulsant
Journal:  JAMA Psychiatry       Date:  2020-06-01       Impact factor: 21.596

2.  A Randomized Clinical Trial Comparing Two Treatment Strategies, Evaluating the Meaningfulness of HAM-D Rating Scale in Patients With Major Depressive Disorder.

Authors:  Junaid Asghar; Madiha Tabasam; Maha M Althobaiti; Amal Adnan Ashour; Mohammed A Aleid; Osamah Ibrahim Khalaf; Theyazn H H Aldhyani
Journal:  Front Psychiatry       Date:  2022-05-27       Impact factor: 5.435

3.  Assessment of a Prediction Model for Antidepressant Treatment Stability Using Supervised Topic Models.

Authors:  Michael C Hughes; Melanie F Pradier; Andrew Slavin Ross; Thomas H McCoy; Roy H Perlis; Finale Doshi-Velez
Journal:  JAMA Netw Open       Date:  2020-05-01

4.  Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables.

Authors:  Giampaolo Perna; Alessandra Alciati; Silvia Daccò; Massimiliano Grassi; Daniela Caldirola
Journal:  Psychiatry Investig       Date:  2020-03-12       Impact factor: 2.505

5.  Analysis of depressive episodes, their recurrence and pharmacologic treatment in primary care patients: A retrospective descriptive study.

Authors:  Shysset Nuggerud-Galeas; Loreto Sáez-Benito Suescun; Nuria Berenguer Torrijo; Ana Sáez-Benito Suescun; Alejandra Aguilar-Latorre; Rosa Magallón Botaya; Bárbara Oliván Blázquez
Journal:  PLoS One       Date:  2020-05-21       Impact factor: 3.240

6.  Role of age, gender and marital status in prognosis for adults with depression: An individual patient data meta-analysis.

Authors:  J E J Buckman; R Saunders; J Stott; L-L Arundell; C O'Driscoll; M R Davies; T C Eley; S D Hollon; T Kendrick; G Ambler; Z D Cohen; E Watkins; S Gilbody; N Wiles; D Kessler; D Richards; S Brabyn; E Littlewood; R J DeRubeis; G Lewis; S Pilling
Journal:  Epidemiol Psychiatr Sci       Date:  2021-06-04       Impact factor: 6.892

7.  Comparing methods for estimating patient-specific treatment effects in individual patient data meta-analysis.

Authors:  Michael Seo; Ian R White; Toshi A Furukawa; Hissei Imai; Marco Valgimigli; Matthias Egger; Marcel Zwahlen; Orestis Efthimiou
Journal:  Stat Med       Date:  2020-12-27       Impact factor: 2.373

8.  The contribution of depressive 'disorder characteristics' to determinations of prognosis for adults with depression: an individual patient data meta-analysis.

Authors:  Joshua E J Buckman; Rob Saunders; Zachary D Cohen; Phoebe Barnett; Katherine Clarke; Gareth Ambler; Robert J DeRubeis; Simon Gilbody; Steven D Hollon; Tony Kendrick; Edward Watkins; Nicola Wiles; David Kessler; David Richards; Deborah Sharp; Sally Brabyn; Elizabeth Littlewood; Chris Salisbury; Ian R White; Glyn Lewis; Stephen Pilling
Journal:  Psychol Med       Date:  2021-04-14       Impact factor: 7.723

9.  The impact of sleep, physical activity and sedentary behaviour on symptoms of depression and anxiety before and during the COVID-19 pandemic in a sample of South African participants.

Authors:  R Lewis; L C Roden; K Scheuermaier; F X Gomez-Olive; D E Rae; S Iacovides; A Bentley; J P Davy; C J Christie; S Zschernack; J Roche; G Lipinska
Journal:  Sci Rep       Date:  2021-12-15       Impact factor: 4.379

  9 in total

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