Hostname: page-component-cb9f654ff-pvkqz Total loading time: 0 Render date: 2025-07-31T19:18:39.319Z Has data issue: false hasContentIssue false
  • English
  • Français

Clinical course predicts long-term outcomes in bipolar disorder

Published online by Cambridge University Press:  28 June 2018

Rudolf Uher ,
Sanna Pallaskorpi ,
Kirsi Suominen ,
Outi Mantere ,
Barbara Pavlova  and
Erkki Isometsä
Rudolf Uher*
Affiliation:
Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada Nova Scotia Health Authority, Halifax, Nova Scotia, Canada
Sanna Pallaskorpi
Affiliation:
Mental Health Unit, National Institute of Health and Welfare, Helsinki, Finland Department of Psychiatry, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland
Kirsi Suominen
Affiliation:
Helsinki City Department of Social Services and Healthcare, Mental Health and Substance Abuse Services, Helsinki, Finland
Outi Mantere
Affiliation:
Department of Psychiatry, McGill University, Montréal, QC, Canada Bipolar Disorders Clinic, Douglas Mental Health University Institute, Montréal, QC, Canada
Barbara Pavlova
Affiliation:
Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada Nova Scotia Health Authority, Halifax, Nova Scotia, Canada
Erkki Isometsä
Affiliation:
Department of Psychiatry, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland
*
Author for correspondence: Rudolf Uher, E-mail: uher@dal.ca
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

The long-term outcomes of bipolar disorder range from lasting remission to chronic course or frequent recurrences requiring admissions. The distinction between bipolar I and II disorders has limited utility in outcome prediction. It is unclear to what extent the clinical course of bipolar disorder predicts long-term outcomes.

Methods

A representative sample of 191 individuals diagnosed with bipolar I or II disorder was recruited and followed for up to 5 years using a life-chart method. We previously described the clinical course over the first 18 months with dimensional course characteristics and latent classes. Now we test if these course characteristics predict long-term outcomes, including time ill (time with any mood symptoms) and hospital admissions over a second non-overlapping follow-up period in 111 individuals with available data from both 18 months and 5 years follow-ups.

Results

Dimensional course characteristics from the first 18 months prospectively predicted outcomes over the following 3.5 years. The proportion of time depressed, the severity of depressive symptoms and the proportion of time manic predicted more time ill. The proportion of time manic, the severity of manic symptoms and depression-to-mania switching predicted a greater likelihood of hospital admissions. All predictions remained significant after controlling for age, sex and bipolar I v. II disorder.

Conclusions

Differential associations with long-term outcomes suggest that course characteristics may facilitate care planning with greater predictive validity than established types of bipolar disorders. A clinical course dominated by depressive symptoms predicts a greater proportion of time ill. A clinical course characterized by manic episodes predicts hospital admissions.

Keywords

Bipolar disorderclinical coursehospital admissionslong-term follow-upoutcome prediction

Information

Type
Original Articles
Information
Psychological Medicine , Volume 49 , Issue 7 , May 2019 , pp. 1109 - 1117
Copyright
Copyright © Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

American Psychiatric Association (2013) Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). Arlington, VA: American Psychiatric Association.Google Scholar
Arvilommi, P, Suominen, K, Mantere, O, Leppamaki, S, Valtonen, HM and Isometsa, E (2010) Maintenance treatment received by patients with bipolar I and II disorders – a naturalistic prospective study. Journal of Affective Disorders 121, 116–126.Google Scholar
Arvilommi, P, Suominen, K, Mantere, O, Valtonen, H, Leppamaki, S and Isometsa, E (2015) Predictors of long-term work disability among patients with type I and II bipolar disorder: a prospective 18-month follow-up study. Bipolar Disorders 17, 821–835.Google Scholar
Austin, PC, Escobar, M and Kopec, JA (2000) The use of the Tobit model for analyzing measures of health status. Quality of Life Research 9, 901–910.Google Scholar
Das Gupta, R and Guest, JF (2002) Annual cost of bipolar disorder to UK society. British Journal of Psychiatry 180, 227–233.Google Scholar
Dunner, DL, Gershon, ES and Goodwin, FK (1976) Heritable factors in the severity of affective illness. Biological Psychiatry 11, 31–42.Google Scholar
First, MB, Spitzer, RL, Gibbon, M and Williams, JBW (2002) Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Patient Edition. (SCID-I/P). New York.Google Scholar
Forte, A, Baldessarini, RJ, Tondo, L, Vazquez, GH, Pompili, M and Girardi, P (2015) Long-term morbidity in bipolar-I, bipolar-II, and unipolar major depressive disorders. Journal of Affective Disorders 178, 71–78.Google Scholar
Goldberg, JF and Harrow, M (2011) A 15-year prospective follow-up of bipolar affective disorders: comparisons with unipolar nonpsychotic depression. Bipolar Disorders 13, 155–163.Google Scholar
Grande, I, Goikolea, JM, de, DC, Gonzalez-Pinto, A, Montes, JM, Saiz-Ruiz, J, Prieto, E and Vieta, E (2013) Occupational disability in bipolar disorder: analysis of predictors of being on severe disablement benefit (PREBIS study data). Acta Psychiatrica Scandinavica 127, 403–411.Google Scholar
Hidalgo-Mazzei, D, Undurraga, J, Reinares, M, Bonnin, CM, Saez, C, Mur, M, Nieto, E and Vieta, E (2015) The real world cost and health resource utilization associated to manic episodes: the MANACOR study. Revista de Psiquiatria y Salud Mental 8, 55–64.Google Scholar
Hong, J, Reed, C, Novick, D, Haro, JM, Windmeijer, F and Knapp, M (2010) The cost of relapse for patients with a manic/mixed episode of bipolar disorder in the EMBLEM study. Pharmacoeconomics 28, 555–566.Google Scholar
Isometsa, E, Suominen, K, Mantere, O, Valtonen, H, Leppamaki, S, Pippingskold, M and Arvilommi, P (2003) The mood disorder questionnaire improves recognition of bipolar disorder in psychiatric care. BMC Psychiatry 3, 8.Google Scholar
Judd, LL, Akiskal, HS, Schettler, PJ, Coryell, W, Endicott, J, Maser, JD, Solomon, DA, Leon, AC and Keller, MB (2003) A prospective investigation of the natural history of the long-term weekly symptomatic status of bipolar II disorder. Archives of General Psychiatry 60, 261–269.Google Scholar
Judd, LL, Akiskal, HS, Schettler, PJ, Endicott, J, Leon, AC, Solomon, DA, Coryell, W, Maser, JD and Keller, MB (2005) Psychosocial disability in the course of bipolar I and II disorders: a prospective, comparative, longitudinal study. Archives of General Psychiatry 62, 1322–1330.Google Scholar
Judd, LL, Akiskal, HS, Schettler, PJ, Endicott, J, Maser, J, Solomon, DA, Leon, AC, Rice, JA and Keller, MB (2002) The long-term natural history of the weekly symptomatic status of bipolar I disorder. Archives of General Psychiatry 59, 530–537.Google Scholar
Jung, CG (1903) On manic mood disorder. In Adler, G and Hull, RFC (eds), Psychiatric Studies, pp. 109–134. Princeton, NJ: Princeton University Press.Google Scholar
Keller, MB, Lavori, PW, Friedman, B, Nielsen, E, Endicott, J, Donald-Scott, P and Andreasen, NC (1987) The longitudinal interval follow-up evaluation. A comprehensive method for assessing outcome in prospective longitudinal studies. Archives of General Psychiatry 44, 540–548.Google Scholar
Kessing, LV, Hansen, HV, Hvenegaard, A, Christensen, EM, Dam, H, Gluud, C and Wetterslev, J (2013) Treatment in a specialised out-patient mood disorder clinic v. standard out-patient treatment in the early course of bipolar disorder: randomised clinical trial. British Journal of Psychiatry 202, 212–219.Google Scholar
Koukopoulos, A and Sani, G (2014) DSM-5 criteria for depression with mixed features: a farewell to mixed depression. Acta Psychiatrica Scandinavica 129, 4–16.Google Scholar
Long, JS (2014) Regression Models for Categorical Dependent Variables Using Stata, 3rd Edn. College Station, TX: Stata Press.Google Scholar
Mantere, O, Suominen, K, Leppamaki, S, Valtonen, H, Arvilommi, P and Isometsa, E (2004) The clinical characteristics of DSM-IV bipolar I and II disorders: baseline findings from the Jorvi Bipolar Study (JoBS). Bipolar Disorders 6, 395–405.Google Scholar
Mantere, O, Suominen, K, Valtonen, HM, Arvilommi, P, Leppamaki, S, Melartin, T and Isometsa, E (2008) Differences in outcome of DSM-IV bipolar I and II disorders. Bipolar Disorders 10, 413–425.Google Scholar
Murru, A, Pacchiarotti, I, Verdolini, N, Reinares, M, Torrent, C, Geoffroy, PA, Bellivier, F, Llorca, PM, Vieta, E and Samalin, L (2017) Modifiable and non-modifiable factors associated with functional impairment during the inter-episodic periods of bipolar disorder. European Archives of Psychiatry and Clinical Neuroscience. doi: 10.1007/s00406-017-0811-0.Google Scholar
Pallaskorpi, S, Suominen, K, Ketokivi, M, Mantere, O, Arvilommi, P, Valtonen, H, Leppamaki, S and Isometsa, E (2015) Five-year outcome of bipolar I and II disorders: findings of the Jorvi Bipolar Study. Bipolar Disorders 17, 363–374.Google Scholar
Prisciandaro, JJ and Roberts, JE (2009) A comparison of the predictive abilities of dimensional and categorical models of unipolar depression in the National Comorbidity Survey. Psychological Medicine 39, 1087–1096.Google Scholar
Tundo, A, Musetti, L, Benedetti, A, Massimetti, E, Pergentini, I, Cambiali, E and Dell'Osso, L (2018) Predictors of recurrence during long-term treatment of bipolar I and II disorders. A 4 year prospective naturalistic study. Journal of Affective Disorders 225, 123–128.Google Scholar
Twisk, J and Rijmen, F (2009) Longitudinal tobit regression: a new approach to analyze outcome variables with floor or ceiling effects. Journal of Clinical Epidemiology 62, 953–958.Google Scholar
Uher, R, Mantere, O, Suominen, K and Isometsa, E (2013) Typology of clinical course in bipolar disorder based on 18-month naturalistic follow-up. Psychological Medicine 43, 789–799.Google Scholar