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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*
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)

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.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2014 

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References

Alonso, J, Chatterji, S, He, Y (2013). The Burden of Mental Disorders: Global Perspectives from the WHO World Mental Health Surveys. Cambridge University Press: New York, NY.Google Scholar
Altman, D, Machin, D, Bryant, T, Gardner, S (eds) (2000). Statistics with Confidence: Confidence Intervals and Statistical Guidelines, 2nd edn. BMJ Books: London, UK.Google Scholar
Andreescu, C, Mulsant, BH, Houck, PR, Whyte, EM, Mazumdar, S, Dombrovski, AY, Pollock, BG, Reynolds, CF 3rd (2008). Empirically derived decision trees for the treatment of late-life depression. American Journal of Psychiatry 165, 855862.CrossRefGoogle ScholarPubMed
Baumeister, H, Parker, G (2012). Meta-review of depressive subtyping models. Journal of Affective Disorders 139, 126140.Google Scholar
Berk, RA (2003). Regression Analysis: A Constructive Critique. Sage: Newbury Park, CA.Google Scholar
Berk, RA (2008). Statistical Learning from a Regression Perspective. Springer: New York, NY.Google Scholar
Bottomley, C, Nazareth, I, Torres-Gonzalez, F, Svab, I, Maaroos, HI, Geerlings, MI, Xavier, M, Saldivia, S, King, M (2010). Comparison of risk factors for the onset and maintenance of depression. British Journal of Psychiatry 196, 1317.Google Scholar
Candrian, M, Farabaugh, A, Pizzagalli, DA, Baer, L, Fava, M (2007). Perceived stress and cognitive vulnerability mediate the effects of personality disorder comorbidity on treatment outcome in major depressive disorder: a path analysis study. Journal of Nervous and Mental Disease 195, 729737.CrossRefGoogle ScholarPubMed
Carragher, N, Adamson, G, Bunting, B, McCann, S (2009). Subtypes of depression in a nationally representative sample. Journal of Affective Disorders 113, 8899.Google Scholar
Chan, SS, Kyba, M (2013). What is a master regulator? Journal of Stem Cell Research and Therapy 3, e114.Google Scholar
Chang, YJ, Chen, LJ, Chung, KP, Lai, MS (2012). Risk groups defined by Recursive Partitioning Analysis of patients with colorectal adenocarcinoma treated with colorectal resection. BMC Medical Research Methodology 12, 2.CrossRefGoogle ScholarPubMed
Chao, ST, Koyfman, SA, Woody, N, Angelov, L, Soeder, SL, Reddy, CA, Rybicki, LA, Djemil, T, Suh, JH (2012). Recursive partitioning analysis index is predictive for overall survival in patients undergoing spine stereotactic body radiation therapy for spinal metastases. International Journal of Radiation Oncology, Biology, Physics 82, 17381743.CrossRefGoogle ScholarPubMed
Clark, LA, Watson, D (2006). Distress and fear disorders: an alternative empirically based taxonomy of the ‘mood’ and ‘anxiety’ disorders. British Journal of Psychiatry 189, 481483.Google Scholar
Cooper, C, Jones, L, Dunn, E, Forty, L, Haque, S, Oyebode, F, Craddock, N, Jones, I (2007). Clinical presentation of postnatal and non-postnatal depressive episodes. Psychological Medicine 37, 12731280.CrossRefGoogle ScholarPubMed
Cooper, PJ, Murray, L (1995). Course and recurrence of postnatal depression. Evidence for the specificity of the diagnostic concept. British Journal of Psychiatry 166, 191195.Google Scholar
Draper, N, Smith, H (1981). Applied Regression Analysis, 2nd edn. Wiley: New York, NY.Google Scholar
Endicott, J, Andreasen, N, Spitzer, RL (1978). Family History Research Diagnostic Criteria. Biometrics Research, New York State Psychiatric Institute: New York, NY.Google Scholar
Fink, M, Rush, AJ, Knapp, R, Rasmussen, K, Mueller, M, Rummans, TA, O'Connor, K, Husain, M, Biggs, M, Bailine, S, Kellner, CH (2007). DSM melancholic features are unreliable predictors of ECT response: a CORE publication. Journal of ECT 23, 139146.Google Scholar
First, MB, Spitzer, RL, Gibbon, M, Williams, JBW (2002). Structured Clinical Interview for DSM-IV Axis I Disorders, Research Version, Non-Patient Edition (SCID-I/NP). Biometrics Research, New York State Psychiatric Institute: New York, NY.Google Scholar
Friedman, J, Hastie, T, Tibshirani, R (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33, 122.CrossRefGoogle ScholarPubMed
Harkness, J, Pennell, BE, Villar, A, Gebler, N, Aguilar-Gaxiola, S, Bilgen, I (2008). Translation procedures and translation assessment in the World Mental Health Survey Initiative. In The WHO World Mental Health Surveys: Global Perspectives on the Epidemiology of Mental Disorders (ed. Kessler, R. C. and Üstün, T. B.), pp. 91113. Cambridge University Press: New York.Google Scholar
Haro, JM, Arbabzadeh-Bouchez, S, Brugha, TS, de Girolamo, G, Guyer, ME, Jin, R, Lepine, JP, Mazzi, F, Reneses, B, Vilagut, G, Sampson, NA, Kessler, RC (2006). Concordance of the Composite International Diagnostic Interview Version 3.0 (CIDI 3.0) with standardized clinical assessments in the WHO World Mental Health surveys. International Journal of Methods in Psychiatric Research 15, 167180.Google Scholar
Hasler, G, Northoff, G (2011). Discovering imaging endophenotypes for major depression. Molecular Psychiatry 16, 604619.Google Scholar
Hastie, T, Tibshirani, R, Friedman, J (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. Springer: New York, NY.Google Scholar
Heeringa, SG, Wells, EJ, Hubbard, F, Mneimneh, ZN, Chiu, WT, Sampson, NA, Berglund, PA (2008). Sample designs and sampling procedures. In The WHO World Mental Health Surveys: Global Perspectives on the Epidemiology of Mental Disorders (ed. Kessler, R. C. and Üstün, T. B.), pp. 1432. Cambridge University Press: New York, NY.Google Scholar
Ilgen, MA, Downing, K, Zivin, K, Hoggatt, KJ, Kim, HM, Ganoczy, D, Austin, KL, McCarthy, JF, Patel, JM, Valenstein, M (2009). Exploratory data mining analysis identifying subgroups of patients with depression who are at high risk for suicide. Journal of Clinical Psychiatry 70, 14951500.Google Scholar
Jain, FA, Hunter, AM, Brooks, JO 3rd, Leuchter, AF (2013). Predictive socioeconomic and clinical profiles of antidepressant response and remission. Depression and Anxiety 30, 624630.Google Scholar
James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An Introduction to Statistical Learning: With Applications in R. Springer: New York, NY.CrossRefGoogle Scholar
Judd, LL, Schettler, PJ, Coryell, W, Akiskal, HS, Fiedorowicz, JG (2013). Overt irritability/anger in unipolar major depressive episodes: past and current characteristics and implications for long-term course. Journal of the American Medical Association. Psychiatry 70, 11711180.Google Scholar
Kessler, RC, Amminger, GP, Aguilar-Gaxiola, S, Alonso, J, Lee, S, Ustun, TB (2007). Age of onset of mental disorders: a review of recent literature. Current Opinion in Psychiatry 20, 359364.CrossRefGoogle ScholarPubMed
Kessler, RC, Üstün, TB (2004). The World Mental Health (WMH) Survey Initiative Version of the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI). International Journal of Methods in Psychiatric Research 13, 93121.Google Scholar
Kessler, RC, Üstün, TB (eds) (2008). The WHO World Mental Health Surveys: Global Perspectives on the Epidemiology of Mental Disorders. Cambridge University Press: New York, NY.Google Scholar
Knäuper, B, Cannell, CF, Schwarz, N, Bruce, ML, Kessler, RC (1999). Improving accuracy of major depression age-of-onset reports in the US National Comorbidity Survey. International Journal of Methods in Psychiatric Research 8, 3948.CrossRefGoogle Scholar
Lamers, F, Burstein, M, He, JP, Avenevoli, S, Angst, J, Merikangas, KR (2012). Structure of major depressive disorder in adolescents and adults in the US general population. British Journal of Psychiatry 201, 143150.CrossRefGoogle ScholarPubMed
Lumley, T (2004). Analysis of complex survey samples. Journal of Statistical Software 9, 119.Google Scholar
Musil, R, Zill, P, Seemuller, F, Bondy, B, Meyer, S, Spellmann, I, Bender, W, Adli, M, Heuser, I, Fisher, R, Gaebel, W, Maier, W, Rietschel, M, Rujescu, D, Schennach, R, Moller, HJ, Riedel, M (2013). Genetics of emergent suicidality during antidepressive treatment – data from a naturalistic study on a large sample of inpatients with a major depressive episode. European Neuropsychopharmacology 23, 663674.Google Scholar
Nelson, JC, Zhang, Q, Deberdt, W, Marangell, LB, Karamustafalioglu, O, Lipkovich, IA (2012). Predictors of remission with placebo using an integrated study database from patients with major depressive disorder. Current Medical Research and Opinion 28, 325334.Google Scholar
Nock, MK, Borges, G, Ono, Y (2012). Suicide: Global Perspectives from the WHO World Mental Health Surveys. Cambridge University Press: New York, NY.Google Scholar
Patten, SB, Wang, JL, Williams, JV, Lavorato, DH, Khaled, SM, Bulloch, AG (2010). Predictors of the longitudinal course of major depression in a Canadian population sample. Canadian Journal of Psychiatry 55, 669676.Google Scholar
Pennell, B-E, Mneimneh, Z, Bowers, A, Chardoul, S, Wells, JE, Viana, MC, Dinkelmann, K, Gebler, N, Florescu, S, He, Y, Huang, Y, Tomov, T, Vilagut, G (2008). Implementation of the World Mental Health Surveys. In The WHO World Mental Health Surveys: Global Perspectives on the Epidemiology of Mental Disorders (ed. Kessler, R. C. and Üstün, T. B.), pp. 3357. Cambridge University Press: New York, NY.Google Scholar
Penninx, BW, Beekman, AT, Smit, JH, Zitman, FG, Nolen, WA, Spinhoven, P, Cuijpers, P, De Jong, PJ, Van Marwijk, HW, Assendelft, WJ, Van Der Meer, K, Verhaak, P, Wensing, M, De Graaf, R, Hoogendijk, WJ, Ormel, J, Van Dyck, R (2008). The Netherlands Study of Depression and Anxiety (NESDA): rationale, objectives and methods. International Journal of Methods in Psychiatric Research 17, 121140.CrossRefGoogle ScholarPubMed
Pizzagalli, DA (2011). Frontocingulate dysfunction in depression: toward biomarkers of treatment response. Neuropsychopharmacology 36, 183206.Google Scholar
R Core Team (2013). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing: Vienna, Austria.Google Scholar
Rabinoff, M, Kitchen, CM, Cook, IA, Leuchter, AF (2011). Evaluation of quantitative EEG by classification and regression trees to characterize responders to antidepressant and placebo treatment. Open Medical Informatics Journal 5, 18.Google Scholar
Rabinowitz, JD, Silhavy, TJ (2013). Systems biology: metabolite turns master regulator. Nature 500, 283284.Google Scholar
Rayner, BS, Figtree, GA, Sabaretnam, T, Shang, P, Mazhar, J, Weaver, JC, Lay, WN, Witting, PK, Hunyor, SN, Grieve, SM, Khachigian, LM, Bhindi, R (2013). Selective inhibition of the master regulator transcription factor egr-1 with catalytic oligonucleotides reduces myocardial injury and improves left ventricular systolic function in a preclinical model of myocardial infarction. Journal of the American Heart Association 2, e000023.Google Scholar
Rhebergen, D, Lamers, F, Spijker, J, de Graaf, R, Beekman, AT, Penninx, BW (2012). Course trajectories of unipolar depressive disorders identified by latent class growth analysis. Psychological Medicine 42, 13831396.Google Scholar
Riedel, M, Moller, HJ, Obermeier, M, Adli, M, Bauer, M, Kronmuller, K, Brieger, P, Laux, G, Bender, W, Heuser, I, Zeiler, J, Gaebel, W, Schennach-Wolff, R, Henkel, V, Seemuller, F (2011). Clinical predictors of response and remission in inpatients with depressive syndromes. Journal of Affective Disorders 133, 137149.Google Scholar
Ryu, B, Kim, DS, Deluca, AM, Alani, RM (2007). Comprehensive expression profiling of tumor cell lines identifies molecular signatures of melanoma progression. PloS One 2, e594.Google Scholar
Seemuller, F, Riedel, M, Obermeier, M, Bauer, M, Adli, M, Mundt, C, Holsboer, F, Brieger, P, Laux, G, Bender, W, Heuser, I, Zeiler, J, Gaebel, W, Jager, M, Henkel, V, Moller, HJ (2009). The controversial link between antidepressants and suicidality risks in adults: data from a naturalistic study on a large sample of in-patients with a major depressive episode. International Journal of Neuropsychopharmacology 12, 181189.Google Scholar
Steinert, C, Hofmann, M, Kruse, J, Leichsenring, F (2014). The prospective long-term course of adult depression in general practice and the community. A systematic literature review. Journal of Affective Disorders 152–154, 6575.CrossRefGoogle ScholarPubMed
Strobl, C, Malley, J, Tutz, G (2009). An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychological Methods 14, 323348.Google Scholar
Thernau, T, Atkinson, B, Ripley, B (2012). Rpart: Recursive Partitioning. R Package 4.1–0. (http://crantastic.org/packages/rpart)Google Scholar
Uher, R, Dernovsek, MZ, Mors, O, Hauser, J, Souery, D, Zobel, A, Maier, W, Henigsberg, N, Kalember, P, Rietschel, M, Placentino, A, Mendlewicz, J, Aitchison, KJ, McGuffin, P, Farmer, A (2011). Melancholic, atypical and anxious depression subtypes and outcome of treatment with escitalopram and nortriptyline. Journal of Affective Disorders 132, 112120.Google Scholar
van der Laan, MJ, Polley, EC, Hubbard, AE (2007). Super learner. Statistical Applications in Genetics and Molecular Biology 6, Article 25.Google Scholar
van der Laan, MJ, Rose, S (2011). Targeted Learning: Causal Inference for Observational and Experimental Data. Springer: New York, NY.Google Scholar
van Loo, HM, Cai, T, Gruber, MJ, Li, J, de Jonge, P, Petukhova, M, Rose, S, Sampson, NA, Schoevers, RA, Wardenaar, KJ, Wilcox, MA, Al-Hamzawi, A, Andrade, LH, Bromet, EJ, Bunting, B, Fayyad, J, Florescu, SE, Gureje, O, Hu, C, Huang, Y, Levinson, D, Medina-Mora, ME, Nakane, Y, Posada-Villa, J, Scott, KM, Xavier, M, Zarkov, Z, Kessler, RC (2014). Major depressive disorder subtypes to predict long-term course. Depression and Anxiety. Published online: 14 January 2014 . doi: 10.1002/da.22233.Google Scholar
van Loo, HM, de Jonge, P, Romeijn, JW, Kessler, RC, Schoevers, RA (2012). Data-driven subtypes of major depressive disorder: a systematic review. BMC Medicine 10, 156.Google Scholar
Von Korff, MR, Scott, KM, Gureje, O (2009). Global Perspectives on Mental-Physical Comorbidity in the WHO World Mental Health Surveys. Cambridge University Press: New York, NY.CrossRefGoogle Scholar
Vrieze, E, Demyttenaere, K, Bruffaerts, R, Hermans, D, Pizzagalli, DA, Sienaert, P, Hompes, T, de Boer, P, Schmidt, M, Claes, S (2014). Dimensions in major depressive disorder and their relevance for treatment outcome. Journal of Affective Disorders 155, 3541.Google Scholar
Wolter, KM (1985). Introduction to Variance Estimation. Springer-Verlag: New York, NY.Google Scholar
World Bank (2009). Data and Statistics: Country Groups by Income. (http://go.worldbank.org/D7SN0B8YU0)Google Scholar
Zhang, H, Singer, BH (2010). Recursive Partitioning and Applications, 2nd edn. Springer: New York, NY.Google Scholar
Zou, H, Hastie, T (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67, 301320.Google Scholar