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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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References

Abreu, T and Braganca, M (2015) The bipolarity of light and dark: a review on Bipolar Disorder and circadian cycles. Journal of Affective Disorders 185, 219–229.Google Scholar
Akiskal, HS (1983) Dysthymic and cyclothymic disorders: a paradigm for highrisk research. In Davis, JM and Maas, JW (eds), The Affective Disorders. Washington DC: American Psychiatric Press, pp. 211–232.Google Scholar
American Psychiatric Association (1980) Diagnostic and Statistical Manual of Mental Disorders. Washington, DC: American Psychiatric Association.Google Scholar
American Psychiatric Association (2013) Diagnostic and Statistical Manual of Mental Disorders. Washington, DC: American Psychiatric Association.Google Scholar
Angst, J (1966) Ätiologie und Nosologie endogener depressiver Psychosen. Eine genetische, soziologische und klinische Studie. Berlin, Heidelberg, New York: Springer.Google Scholar
Angst, J, Cui, L, Swendsen, J, Rothen, S, Cravchik, A, Kessler, RC and Merikangas, KR (2010) Major depressive disorder with subthreshold bipolarity in the National Comorbidity Survey Replication. American Journal of Psychiatry 167, 1194–1201.Google Scholar
Angst, J, Azorin, JM, Bowden, CL, Perugi, G, Vieta, E, Gamma, A and Young, AH (2011) Prevalence and characteristics of undiagnosed bipolar disorders in patients with a major depressive episode: the BRIDGE study. Archives of General Psychiatry 68, 791–798.Google Scholar
Carragher, N, Weinstock, LM and Strong, D (2013) Psychometric evaluation of the DSM-IV criterion B mania symptoms in an Australian national sample. Psychological Medicine 43, 433–443.Google Scholar
Cochran, AL, Mcinnis, MG and Forger, DB (2016) Data-driven classification of bipolar I disorder from longitudinal course of mood. Translational Psychiatry 6, e912.Google Scholar
Denicoff, KD, Leverich, GS, Nolen, WA, Rush, AJ, Mcelroy, SL, Keck, PE, Suppes, T, Altshuler, LL, Kupka, R, Frye, MA, Hatef, J, Brotman, MA and Post, RM (2000) Validation of the prospective NIMH-Life-Chart Method (NIMH-LCM-p) for longitudinal assessment of bipolar illness. Psychological Medicine 30, 1391–1397.Google Scholar
Falret, JP (1851) De la folie circulaire ou forme de maladie mentale caracterisée par l'alternative réguliére de la manie et de la mélancolie. Bulletin de l'Académie Nationale de Médecine 19, 382–400.Google Scholar
Fiedorowicz, JG, Endicott, J, Leon, AC, Solomon, DA, Keller, MB and Coryell, WH (2011) Subthreshold hypomanic symptoms in progression from unipolar major depression to bipolar disorder. American Journal of Psychiatry 168, 40–48.Google Scholar
Goldberg, JF, Perlis, RH, Bowden, CL, Thase, ME, Miklowitz, DJ, Marangell, LB, Calabrese, JR, Nierenberg, AA and Sachs, GS (2009) Manic symptoms during depressive episodes in 1380 patients with bipolar disorder: findings from the STEP-BD. American Journal of Psychiatry 166, 173–181.Google Scholar
Goodwin, FK and Jamison, KR (2007) Manic-Depressive Illness: Bipolar Disorders and Recurrent Depression. Oxford, UK: Oxford University Press.Google Scholar
Kraepelin, E (1921) Manic Depressive Insanity and Paranoia. Edinburgh, ES: Livingstone.Google Scholar
Langeheine, R and van de Pol, F (2002) Latent Markov chains. In Hagenaars, JA and Mccutcheon, AL (eds), Applied Latent Class Analysis. Cambridge UK: Cambridge University Press, pp. 304–341.Google Scholar
MacDonald, IL and Zucchini, W (1997) Hidden Markov and Other Models for Discrete-valued Time Series. Boca Raton, Florida: CRC Press.Google Scholar
Marneros, A and Angst, J (2001) Bipolar Disorders: 100 Years after Manic-Depressive Insanity. Netherlands: Springer.Google Scholar
Muthen, LK and Muthen, BO (2011) Mplus User's Guide, 6th Edn. Los Angeles, CA: Muthen & Muthen.Google Scholar
Niitsu, T, Fabbri, C and Serretti, A (2015) Predictors of switch from depression to mania in bipolar disorder. Journal of Psychiatric Research 66–67, 45–53.Google Scholar
Nylund, KL, Asparouhov, T and Muthén, BO (2007) Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo Simulation Study. Structural Equation Modeling: A Multidisciplinary Journal 14, 535–569.Google Scholar
Ostacher, MJ, Perlis, RH, Nierenberg, AA, Calabrese, J, Stange, JP, Salloum, I, Weiss, RD and Sachs, GS (2010) Impact of substance use disorders on recovery from episodes of depression in bipolar disorder patients: prospective data from the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD). American Journal of Psychiatry 167, 289–297.Google Scholar
Pallaskorpi, S, Suominen, K, Ketokivi, M, Valtonen, H, Arvilommi, P, Mantere, O, Leppamaki, S and Isometsa, E (2017) Incidence and predictors of suicide attempts in bipolar I and II disorders: a 5-year follow-up study. Bipolar Disorders 19, 13–22.Google Scholar
Perlis, RH, Miyahara, S, Marangell, LB, Wisniewski, SR, Ostacher, M, Delbello, MP, Bowden, CL, Sachs, GS and Nierenberg, AA (2004) Long-term implications of early onset in bipolar disorder: data from the first 1000 participants in the systematic treatment enhancement program for bipolar disorder (STEP-BD). Biological Psychiatry 55, 875–881.Google Scholar
Perlis, RH, Ostacher, MJ, Goldberg, JF, Miklowitz, DJ, Friedman, E, Calabrese, J, Thase, ME and Sachs, GS (2010) Transition to mania during treatment of bipolar depression. Neuropsychopharmacology 35, 2545–2552.Google Scholar
Perugi, G, Angst, J, Azorin, JM, Bowden, CL, Mosolov, S, Reis, J, Vieta, E and Young, AH (2015) Mixed features in patients with a major depressive episode: the BRIDGE-II-MIX study. Journal of Clinical Psychiatry 76, e351–e358.Google Scholar
Prisciandaro, JJ and Roberts, JE (2005) A taxometric investigation of unipolar depression in the national comorbidity survey. Journal of Abnormal Psychology 114, 718–728.Google Scholar
Prisciandaro, JJ and Roberts, JE (2011) Evidence for the continuous latent structure of mania in the Epidemiologic Catchment Area from multiple latent structure and construct validation methodologies. Psychological Medicine 41, 575–588.Google Scholar
Prisciandaro, JJ and Tolliver, BK (2016) An item response theory evaluation of the young mania rating scale and the Montgomery-Asberg Depression Rating scale in the systematic treatment enhancement program for bipolar disorder (STEP-BD). Journal of Affective Disorders 205, 73–80.Google Scholar
Prisciandaro, JJ, Desantis, SM, Chiuzan, C, Brown, DG, Brady, KT and Tolliver, BK (2012) Impact of depressive symptoms on future alcohol use in patients with co-occurring bipolar disorder and alcohol dependence: a prospective analysis in an 8-week randomized controlled trial of acamprosate. Alcoholism, Clinical and Experimental Research 36, 490–496.Google Scholar
Sachs, GS, Thase, ME, Otto, MW, Bauer, M, Miklowitz, D, Wisniewski, SR, Lavori, P, Lebowitz, B, Rudorfer, M, Frank, E, Nierenberg, AA, Fava, M, Bowden, C, Ketter, T, Marangell, L, Calabrese, J, Kupfer, D and Rosenbaum, JF (2003) Rationale, design, and methods of the systematic treatment enhancement program for bipolar disorder (STEP-BD). Biological Psychiatry 53, 1028–1042.Google Scholar
Schneck, CD, Miklowitz, DJ, Miyahara, S, Araga, M, Wisniewski, S, Gyulai, L, Allen, MH, Thase, ME and Sachs, GS (2008) The prospective course of rapid-cycling bipolar disorder: findings from the STEP-BD. American Journal of Psychiatry 165, 370–377; quiz 410.Google Scholar
Sheehan, DV, Lecrubier, Y, Sheehan, KH, Amorim, P, Janavs, J, Weiller, E, Hergueta, T, Baker, R and Dunbar, GC (1998) The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. Journal of Clinical Psychiatry 59(Suppl. 20), 22–33; quiz 34–57.Google Scholar
Shirley, KE, Small, DS, Lynch, KG, Maisto, SA and Oslin, DW (2010) Hidden Markov models for alcoholism treatment trial data. The Annals of Applied Statistics 4, 366–395.Google Scholar
Wall, MM and Li, R (2009) Multiple indicator hidden Markov model with an application to medical utilization data. Statistics in Medicine 28, 293–310.Google Scholar
Young, RC, Biggs, JT, Ziegler, VE and Meyer, DA (1978) A rating scale for mania: reliability, validity and sensitivity. British Journal of Psychiatry 133, 429–435.Google Scholar
Zimmermann, P, Bruckl, T, Nocon, A, Pfister, H, Lieb, R, Wittchen, HU, Holsboer, F and Angst, J (2009) Heterogeneity of DSM-IV major depressive disorder as a consequence of subthreshold bipolarity. Archives of General Psychiatry 66, 1341–1352.Google Scholar