Literature DB >> 29498147

Subtypes of depression and their overlap in a naturalistic inpatient sample of major depressive disorder.

Richard Musil1, Florian Seemüller1,2, Sebastian Meyer1,3,4, Ilja Spellmann1,5, Mazda Adli6, Michael Bauer7, Klaus-Thomas Kronmüller8,9, Peter Brieger10,11, Gerd Laux12, Wolfram Bender11, Isabella Heuser13, Robert Fisher14,15, Wolfgang Gaebel16, Rebecca Schennach1,17, Hans-Jürgen Möller1, Michael Riedel1,18.   

Abstract

Subtyping depression is important in order to further delineate biological causes of depressive syndromes. The aim of this study was to evaluate clinical and outcome characteristics of distinct subtypes of depression and to assess proportion and features of patients fulfilling criteria for more than one subtype. Melancholic, atypical and anxious subtypes of depression were assessed in a naturalistic sample of 833 inpatients using DSM-IV specifiers based on operationalized criteria. Baseline characteristics and outcome criteria at discharge were compared between distinct subtypes and their overlap. A substantial proportion of patients (16%) were classified with more than one subtype of depression, 28% were of the distinct anxious, 7% of the distinct atypical and 5% of the distinct melancholic subtype. Distinct melancholic patients had shortest duration of episode, highest baseline depression severity, but were more often early improvers; distinct anxious patients had higher NEO-Five Factor Inventory (NEO-FFI) neuroticism scores compared with patients with unspecific subtype. Melancholic patients with overlap of anxious features had worse treatment outcome compared to distinct melancholic and distinct anxious subtype. Distinct subtypes differed in only few variables and patients with overlap of depression subtypes may have independent clinical and outcome characteristics. Studies investigating biological causes of subtypes of depression should take influence of features of other subtypes into account.
Copyright © 2017 John Wiley & Sons, Ltd.

Entities:  

Keywords:  DSM; anxiety; inpatients; major depressive disorder; mood disorders

Mesh:

Year:  2017        PMID: 29498147      PMCID: PMC6877097          DOI: 10.1002/mpr.1569

Source DB:  PubMed          Journal:  Int J Methods Psychiatr Res        ISSN: 1049-8931            Impact factor:   4.035


  84 in total

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Authors:  Maurizio Fava; Jonathan E Alpert; Cheryl N Carmin; Stephen R Wisniewski; Madhukar H Trivedi; Melanie M Biggs; Kathy Shores-Wilson; Don Morgan; Terry Schwartz; G K Balasubramani; A John Rush
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Authors:  P T Costa; R R McCrae
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5.  Melancholic versus non-melancholic depression: differences on cognitive function. A longitudinal study protocol.

Authors:  Saray Monzón; Margalida Gili; Margalida Vives; Maria Jesus Serrano; Natalia Bauza; Rosa Molina; Mauro García-Toro; Joan Salvà; Joan Llobera; Miquel Roca
Journal:  BMC Psychiatry       Date:  2010-06-17       Impact factor: 3.630

6.  DSM melancholic features are unreliable predictors of ECT response: a CORE publication.

Authors:  Max Fink; A John Rush; Rebecca Knapp; Keith Rasmussen; Martina Mueller; Teresa A Rummans; Kevin O'Connor; Mustafa Husain; Melanie Biggs; Samuel Bailine; Charles H Kellner
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7.  Difference in treatment outcome in outpatients with anxious versus nonanxious depression: a STAR*D report.

Authors:  Maurizio Fava; A John Rush; Jonathan E Alpert; G K Balasubramani; Stephen R Wisniewski; Cheryl N Carmin; Melanie M Biggs; Sidney Zisook; Andrew Leuchter; Robert Howland; Diane Warden; Madhukar H Trivedi
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Review 8.  Atypical depression. A valid clinical entity?

Authors:  J W Stewart; P J McGrath; J G Rabkin; F M Quitkin
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Journal:  Neuropsychopharmacology       Date:  2009-09-02       Impact factor: 7.853

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  11 in total

1.  Subtypes of depression and their overlap in a naturalistic inpatient sample of major depressive disorder.

Authors:  Richard Musil; Florian Seemüller; Sebastian Meyer; Ilja Spellmann; Mazda Adli; Michael Bauer; Klaus-Thomas Kronmüller; Peter Brieger; Gerd Laux; Wolfram Bender; Isabella Heuser; Robert Fisher; Wolfgang Gaebel; Rebecca Schennach; Hans-Jürgen Möller; Michael Riedel
Journal:  Int J Methods Psychiatr Res       Date:  2017-06-14       Impact factor: 4.035

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8.  Combining machine learning algorithms for prediction of antidepressant treatment response.

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