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Predictors of treatment outcome in depression in later life: a systematic review and meta-analysis

Research output: Contribution to journalReview articlepeer-review

Chavit Tunvirachaisakul, Rebecca L. Gould, Mark C. Coulson, Emma V. Ward, Gemma Reynolds, Rebecca L. Gathercole, Hannah Grocott, Thitiporn Supasitthumrong, Athicha Tunvirachaisakul, Kate Kimona, Robert J. Howard

Original languageEnglish
Pages (from-to)164-182
JournalJournal of Affective Disorders
Volume227
Early online date10 Oct 2017
DOIs
Accepted/In press1 Oct 2017
E-pub ahead of print10 Oct 2017

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Abstract

Background
Predictor analyses of late-life depression can be used to identify variables associated with outcomes of treatments, and hence ways of tailoring specific treatments to patients. The aim of this review was to systematically identify, review and meta-analyse predictors of outcomes of any type of treatment for late-life depression.
Methods
Pubmed, Embase, CINAHL, Web of Science and PsycINFO were searched for studies published up to December 2016. Primary and secondary studies reported treatment predictors from randomised controlled trials of any treatment for patients with major depressive disorder aged over 60 were included. Treatment outcomes included response, remission and change in depression score.
Results
Sixty-seven studies met the inclusion criteria. Of 65 identified statistically significant predictors, only 7 were reported in at least 3 studies. Of these, 5 were included in meta-analyses, and only 3 were statistically significant. Most studies were rated as being of moderate to strong quality and satisfied key quality criteria for predictor analyses.
Limitations
The searches were limited to randomised controlled trials and most of the included studies were secondary analyses.
Conclusions
Baseline depression severity, co-morbid anxiety, executive dysfunction, current episode duration, early improvement, physical illnesses and age were reported as statistically significant predictors of treatment outcomes. Only the first three were significant in meta-analyses. Subgroup analyses showed differences in predictor effect between biological and psychosocial treatment. However, high heterogeneity and small study numbers suggest a cautious interpretation of results. These predictors were associated with various mechanisms including brain pathophysiology, perceived social support and proposed distinct types of depressive disorder. Further investigation of the clinical utility of these predictors is suggested.

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