Group-based trajectory modeling: A novel approach to examining symptom trajectories in acute bipolar episodes

Background: Pattern analysis can aid understanding of trajectories of symptom evolution. However, most studies focus on relatively homogeneous disorders with a restricted range of outcomes, prescribed a limited number of classes of medication. We explored the utility of pattern analysis in defining short-term outcomes in a heterogeneous clinical sample with acute bipolar disorders. Method: In a naturalistic observational study, we used Group-based trajectory modeling (GBTM) to define trajectories of symptom change in 118 bipolar cases recruited during an acute DSM IV episode: major depression (56%), (hypo)mania (26%), and mixed states (18%). Symptoms were assessed weekly for a month using the MATHYS, which measures symptoms independent of episode polarity. Results: Four trajectories of symptom change were identified: Persistent Inhibition, Transient Inhibition, Transient Activation and Over-activation. However, counter to traditional predictions, we observed that bipolar depression shows a heterogeneous response pattern with cases being distributed approximately equally across trajectories that commenced with inhibition and activation. Limitations: The observational period focuses on acute outcomes and so we cannot use the findings to predict whether the trajectories lead to stable improvement or whether the clinical course for some clusters is cyclical. As in all GBTM, the terms used for each trajectory are subjective, also the modeling programme we used assumes dropouts are random, which is clearly not always the case. Conclusion: This paper highlights the potential importance of techniques such as GBTM in distinguishing the different response trajectories for acutely ill bipolar cases. The use of the MATHYS provides further critical insights, demonstrating that clustering of cases with similar response patterns may be independent of episodes defined by mood state. (C) 2012 Elsevier B.V. All rights reserved.