Literature DB >> 29952277

Predicting antidepressant response by monitoring early improvement of individual symptoms of depression: individual patient data meta-analysis.

Ymkje Anna de Vries1, Annelieke M Roest2, Elisabeth H Bos1, Johannes G M Burgerhof3, Hanna M van Loo4, Peter de Jonge2.   

Abstract

BACKGROUND: Improvement in depression within the first 2 weeks of antidepressant treatment predicts good outcomes, but non-improvers can still respond or remit, whereas improvers often do not.AimsWe aimed to investigate whether early improvement of individual depressive symptoms better predicts response or remission.
METHOD: We obtained individual patient data of 30 trials comprising 2184 placebo-treated and 6058 antidepressant-treated participants. Primary outcome was week 6 response; secondary outcomes were week 6 remission and week 12 response and remission. We compared models that only included improvement in total score by week 2 (total improvement model) with models that also included improvement in individual symptoms.
RESULTS: For week 6 response, the area under the receiver operating characteristic curve and negative and positive predictive values of the total improvement model were 0.73, 0.67 and 0.74 compared with 0.77, 0.70 and 0.71 for the item improvement model. Model performance decreased for week 12 outcomes. Of predicted non-responders, 29% actually did respond by week 6 and 43% by week 12, which was decreased from the baseline (overall) probabilities of 51% by week 6 and 69% by week 12. In post hoc analyses with continuous rather than dichotomous early improvement, including individual items did not enhance model performance.
CONCLUSIONS: Examining individual symptoms adds little to the predictive ability of early improvement. Additionally, early non-improvement does not rule out response or remission, particularly after 12 rather than 6 weeks. Therefore, our findings suggest that routinely adapting pharmacological treatment because of limited early improvement would often be premature.Declaration of interestNone.

Entities:  

Keywords:  Antidepressants; depression; early improvement; individual patient data meta-analysis; treatment response

Mesh:

Substances:

Year:  2018        PMID: 29952277     DOI: 10.1192/bjp.2018.122

Source DB:  PubMed          Journal:  Br J Psychiatry        ISSN: 0007-1250            Impact factor:   9.319


  5 in total

1.  Perceived helpfulness of treatment for specific phobia: Findings from the World Mental Health Surveys.

Authors:  Ymkje Anna de Vries; Meredith G Harris; Daniel Vigo; Wai Tat Chiu; Nancy A Sampson; Ali Al-Hamzawi; Jordi Alonso; Laura H Andrade; Corina Benjet; Ronny Bruffaerts; Brendan Bunting; José Miguel Caldas de Almeida; Giovanni de Girolamo; Silvia Florescu; Oye Gureje; Josep Maria Haro; Chiyi Hu; Elie G Karam; Norito Kawakami; Viviane Kovess-Masfety; Sing Lee; Jacek Moskalewicz; Fernando Navarro-Mateu; Akin Ojagbemi; José Posada-Villa; Kate Scott; Yolanda Torres; Zahari Zarkov; Andrew Nierenberg; Ronald C Kessler; Peter de Jonge
Journal:  J Affect Disord       Date:  2021-04-20       Impact factor: 6.533

Review 2.  Prognosis and improved outcomes in major depression: a review.

Authors:  Christoph Kraus; Bashkim Kadriu; Rupert Lanzenberger; Carlos A Zarate; Siegfried Kasper
Journal:  Transl Psychiatry       Date:  2019-04-03       Impact factor: 6.222

3.  Leveraging Machine Learning Approaches for Predicting Antidepressant Treatment Response Using Electroencephalography (EEG) and Clinical Data.

Authors:  Natalia Jaworska; Sara de la Salle; Mohamed-Hamza Ibrahim; Pierre Blier; Verner Knott
Journal:  Front Psychiatry       Date:  2019-01-14       Impact factor: 4.157

4.  Genetic Markers for Later Remission in Response to Early Improvement of Antidepressants.

Authors:  Hee-Ju Kang; Ki-Tae Kim; Kyung-Hun Yoo; Yoomi Park; Ju-Wan Kim; Sung-Wan Kim; Il-Seon Shin; Ju Han Kim; Jae-Min Kim
Journal:  Int J Mol Sci       Date:  2020-07-10       Impact factor: 5.923

5.  Reading, Conducting, and Developing Systematic Review and Individual Patient Data Meta-Analyses in Psychiatry for Treatment Issues.

Authors:  Nadia Younes; Laurie-Anne Claude; Xavier Paoletti
Journal:  Front Psychiatry       Date:  2021-07-29       Impact factor: 4.157

  5 in total

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