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The effects of co-morbidity in defining major depression subtypes associated with long-term course and severity

Published online by Cambridge University Press:  17 July 2014

K. J. Wardenaar ,
H. M. van Loo ,
T. Cai ,
M. Fava ,
M. J. Gruber ,
J. Li ,
P. de Jonge ,
A. A. Nierenberg ,
M. V. Petukhova  and
S. Rose
...Show all authors
K. J. Wardenaar*
Affiliation:
Department of Psychiatry, University of Groningen, University Medical Center Groningen, The Netherlands
H. M. van Loo
Affiliation:
Department of Psychiatry, University of Groningen, University Medical Center Groningen, The Netherlands
T. Cai
Affiliation:
Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
M. Fava
Affiliation:
Department of Psychiatry, MGH Clinical Trials Network and Institute, Depression Clinical and Research Program, Massachusetts General Hospital, Boston, MA, USA
M. J. Gruber
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
J. Li
Affiliation:
Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
P. de Jonge
Affiliation:
Department of Psychiatry, University of Groningen, University Medical Center Groningen, The Netherlands
A. A. Nierenberg
Affiliation:
Depression Clinical and Research Program and the Bipolar Clinic and Research Program, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
M. V. Petukhova
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
S. Rose
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
N. A. Sampson
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
R. A. Schoevers
Affiliation:
Department of Psychiatry, University of Groningen, University Medical Center Groningen, The Netherlands
M. A. Wilcox
Affiliation:
Johnson & Johnson Pharmaceutical Research and Development, Titusville, NJ, USA
J. Alonso
Affiliation:
IMIM-Hospital del Mar Research Institute, Parc de Salut Mar, Pompeu Fabra University (UPF), and CIBER en Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
E. J. Bromet
Affiliation:
Department of Psychiatry and Behavioral Science, Stony Brook School of Medicine, State University of New York at Stony Brook, Stony Brook, NY, USA
B. Bunting
Affiliation:
Psychology Research Institute, University of Ulster, Londonderry, UK
S. E. Florescu
Affiliation:
National School of Public Health, Management and Professional Development, Bucharest, Romania
A. Fukao
Affiliation:
Department of Public Health, Yamagata University School of Medicine, Japan
O. Gureje
Affiliation:
University College Hospital, Ibadan, Nigeria
C. Hu
Affiliation:
Shenzhen Institute of Mental Health and Shenzhen Kangning Hospital, Guangdong Province, People's Republic of China
Y. Q. Huang
Affiliation:
Institute of Mental Health, Peking University, Beijing, People's Republic of China
A. N. Karam
Affiliation:
Department of Psychiatry and Clinical Psychology, St George Hospital University Medical Center, Department of Psychiatry and Clinical Psychology, Faculty of Medicine, Balamand University Medical School, and Institute for Development Research Advocacy and Applied Care (IDRAAC), Beirut, Lebanon
D. Levinson
Affiliation:
Research and Planning, Mental Health Services, Ministry of Health, Jerusalem, Israel
M. E. Medina Mora
Affiliation:
National Institute of Psychiatry, Calzada Mexico Xochimilco, Mexico City, Mexico
J. Posada-Villa
Affiliation:
Universidad Colegio Mayor de Cundinamarca, Bogota, Colombia
K. M. Scott
Affiliation:
Department of Psychological Medicine, University of Otago, Dunedin, New Zealand
N. I. Taib
Affiliation:
Mental Health Center-Duhok, Kurdistan Region, Iraq
M. C. Viana
Affiliation:
Department of Social Medicine, Federal University of Espirito Santo, Vitoria, Brazil
M. Xavier
Affiliation:
Department of Mental Health, Universidade Nova de Lisboa, Lisbon, Portugal
Z. Zarkov
Affiliation:
National Center of Public Health and Analyses, Department of Mental Health, Sofia, Bulgaria
R. C. Kessler
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
*
* Address for correspondence: R. C. Kessler, Ph.D., Department of Health Care Policy, Harvard Medical School, Boston, MA, USA. (Email: kessler@hcp.med.harvard.edu)
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Abstract

Background.

Although variation in the long-term course of major depressive disorder (MDD) is not strongly predicted by existing symptom subtype distinctions, recent research suggests that prediction can be improved by using machine learning methods. However, it is not known whether these distinctions can be refined by added information about co-morbid conditions. The current report presents results on this question.

Method.

Data came from 8261 respondents with lifetime DSM-IV MDD in the World Health Organization (WHO) World Mental Health (WMH) Surveys. Outcomes included four retrospectively reported measures of persistence/severity of course (years in episode; years in chronic episodes; hospitalization for MDD; disability due to MDD). Machine learning methods (regression tree analysis; lasso, ridge and elastic net penalized regression) followed by k-means cluster analysis were used to augment previously detected subtypes with information about prior co-morbidity to predict these outcomes.

Results.

Predicted values were strongly correlated across outcomes. Cluster analysis of predicted values found three clusters with consistently high, intermediate or low values. The high-risk cluster (32.4% of cases) accounted for 56.6–72.9% of high persistence, high chronicity, hospitalization and disability. This high-risk cluster had both higher sensitivity and likelihood ratio positive (LR+; relative proportions of cases in the high-risk cluster versus other clusters having the adverse outcomes) than in a parallel analysis that excluded measures of co-morbidity as predictors.

Conclusions.

Although the results using the retrospective data reported here suggest that useful MDD subtyping distinctions can be made with machine learning and clustering across multiple indicators of illness persistence/severity, replication with prospective data is needed to confirm this preliminary conclusion.

Keywords

Co-morbiditydata miningdepression subtypesdepression symptom profileselastic netmachine learningpredictive modelingrisk assessment

Information

Type
Original Articles
Information
Psychological Medicine , Volume 44 , Issue 15 , November 2014 , pp. 3289 - 3302
Copyright
Copyright © Cambridge University Press 2014 

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