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Group-based trajectory modeling a good model to explore sub-groups of recovery during acute bipolar mood episodes

Published online by Cambridge University Press:  16 April 2020

K. M’bailara ,
O. Cosnefroy ,
A. Desage ,
S. Gard ,
L. Zanouy ,
I. Minois  and
C. Henry
K. M’bailara
Affiliation:
University Victor Segalen, Bordeaux, France Hospital Charles Perrens, Bordeaux, France Fondation Fondamental, Créteil, France
O. Cosnefroy
Affiliation:
University Victor Segalen, Bordeaux, France
A. Desage
Affiliation:
Hospital Charles Perrens, Bordeaux, France Fondation Fondamental, Créteil, France
S. Gard
Affiliation:
Hospital Charles Perrens, Bordeaux, France Fondation Fondamental, Créteil, France
L. Zanouy
Affiliation:
Hospital Charles Perrens, Bordeaux, France Fondation Fondamental, Créteil, France
I. Minois
Affiliation:
Hospital Charles Perrens, Bordeaux, France Fondation Fondamental, Créteil, France
C. Henry
Affiliation:
Fondation Fondamental, Créteil, France Hospital Albert Chenevier, Créteil, France

Abstract

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Group-based trajectory modeling (GBTM) is a statistical method created to explore the heterogeneity of clinical groups based on their longitudinal outcomes by identifying distinct trajectories of change. This model can be applied to assess heterogeneity in responses to treatment. This pilot study explored the relevance of the GBTM associated with the dimensional evaluation of mood (MATHYS) to define trajectory of recovery in acute bipolar mood episodes on a short period of time during a naturalistic study.

Method

The sample consisted in 118 bipolar patients and all patients were recruited during an acute phase: 56% had a major depressive episode, 26% a manic or hypomanic episode, and 18% a mixed state using the DSM-IV criteria. Patients were assessed four times with MATHYS during a three weeks follow-up period. It is an observational study and treatment was prescribed as usual. We applied the GBTM method and MATHYS total score to define trajectories of recovery.

Results

This method allows identifying 4 trajectories of recovery. At Baseline, two of them started with a score of inhibition but with quite different evolutive profiles (stable inhibition versus improvement). The two others trajectories started with a score of activation (mild versus moderate) and showed a linear improvement of symptoms but with a more rapid recovery for the patients with the higher activation at baseline.

Conclusion

When considering the diagnosis of patients belonging in each trajectory, there model seems particular relevant to explore the high heterogeneity in response to treatment in bipolar patients during an acute depressive episode.

Type
P01-232
Information
European Psychiatry , Volume 26 , Issue S2: Abstracts of the 19th European Congress of Psychiatry , March 2011 , pp. 233
Copyright
Copyright © European Psychiatric Association 2011
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