Format

Send to

Choose Destination
See comment in PubMed Commons below
J Affect Disord. 2014 Jul;163:81-7. doi: 10.1016/j.jad.2014.03.053. Epub 2014 Apr 8.

The predictive power of subgroups: an empirical approach to identify depressive symptom patterns that predict response to treatment.

Author information

1
Department of Psychology, University of Zurich, Zurich, Switzerland. Electronic address: j.buehler@psychologie.uzh.ch.
2
Departement of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany; Department of Psychiatry, Psychotherapy and Psychosomatics, kbo-Lech-Mangfall-Klinik, Garmisch-Partenkirchen, Germany.
3
Department of Psychology, University of Zurich, Zurich, Switzerland.

Abstract

BACKGROUND:

Depression research has been trying to improve the response rates to treatments by identifying a valid set of differential predictor variables. Potential candidates have been proposed, one of which were different subtypes of depression. However, the results on the predictive quality of subtypes on treatment are conflicting.

METHODS:

The analyzed data consisted of Hamilton Depression Rating Scales (HAM-D17) of 879 depressive inpatients, which were recruited in a naturalistic multicenter study. Mean length of stay was 9.9 weeks. In a first step, a Latent Class Analysis (LCA) was conducted to classify the patients into smaller groups. In a second step, the class variable was included in a Linear Mixed Effects model to predict the same patients' response to treatment.

RESULTS:

Five classes were obtained from LCA, showing substantially different symptom profiles. One of the classes, with a symptom profile similar to melancholic depression, showed substantially slower response to treatment (i.e., estimated time to remission; 11.3 weeks) than the remaining classes in the study (6.6-8.6 weeks).

LIMITATIONS:

The applied measurement instrument, the HAM-D17, did not include items for two additional, frequently found subtypes of depression: psychotic and atypical depression. Thus, these subtypes could not emerge in the LCA. Furthermore, there was no systematic variation of treatment in the data. Thus, a differential effect of the classes on treatment could not be measured.

CONCLUSIONS:

The classification of patients according to their symptom profiles seems to be a potent predictor for treatment response. However, the obtained symptom patterns are not completely congruent with the theoretically proposed subgroups. Against the background of the results, dividing melancholic depression in a rather cognitive and vegetative subtype may be promising.

KEYWORDS:

Depression; Efficacy; Subtypes of depression

PMID:
24836092
DOI:
10.1016/j.jad.2014.03.053
[Indexed for MEDLINE]
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Elsevier Science
    Loading ...
    Support Center