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Identification and initial validation of empirically derived bipolar symptom states from a large longitudinal dataset: an application of hidden Markov modeling to the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) study

Published online by Cambridge University Press:  29 August 2018

James J. Prisciandaro ,
Bryan K. Tolliver  and
Stacia M. DeSantis
James J. Prisciandaro*
Affiliation:
Department of Psychiatry and Behavioral Sciences, Medical University of SC, Charleston, SC, USA
Bryan K. Tolliver
Affiliation:
Department of Psychiatry and Behavioral Sciences, Medical University of SC, Charleston, SC, USA
Stacia M. DeSantis
Affiliation:
School of Public Health, University of Texas Health Science Center, Houston, TX, USA
*
Author for correspondence: James J. Prisciandaro, E-mail: [email protected]
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Abstract

Background

Although bipolar disorder (BD) is a fundamentally cyclical illness, a divided model of BD that emphasizes polarity over cyclicity has dominated modern psychiatric diagnostic systems since their advent in the 1980s. However, there has been a gradual return to conceptualizations of BD which focus on longitudinal course in the research community due to emerging supportive data. Advances in longitudinal statistical methods promise to further progress the field.

Methods

The current study employed hidden Markov modeling to uncover empirically derived manic and depressive states from longitudinal data [i.e. Young Mania Rating Scale and Montgomery–Asberg Depression Rating Scale responses across five occasions from the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) study], estimate participants’ probabilities of transitioning between these states over time (n = 3918), and evaluate whether clinical variables (e.g. rapid cycling and substance dependence) predict participants’ state transitions (n = 3229).

Results

Analyses identified three empirically derived mood states (‘euthymic,’ ‘depressed,’ and ‘mixed’). Relative to the euthymic and depressed states, the mixed state was less commonly experienced, more temporally unstable, and uniquely associated with rapid cycling, substance use, and psychosis. Individuals assigned to the mixed state at baseline were relatively less likely to be diagnosed with BD-II (v. BD-I), more likely to present with a mixed or (hypo)manic episode, and reported experiencing irritable and elevated mood more frequently.

Conclusions

The results from the current study represent an important step in defining, and characterizing the longitudinal course of, empirically derived mood states that can be used to form the foundation of objective, empirical attempts to define meaningful subtypes of affective illness defined by clinical course.

Keywords

Bipolar disorderhidden Markov modellatent variable modelinglongitudinalmixed episodemixed featuressystematic treatment enhancement program for bipolar disorder
Type
Original Articles
Information
Psychological Medicine , Volume 49 , Issue 7 , May 2019 , pp. 1102 - 1108
Copyright
Copyright © Cambridge University Press 2018 

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