MAJOR DEPRESSIVE DISORDER SUBTYPES TO PREDICT LONG-TERM COURSE

Autor(es):Mostrar menos -

van Loo, Hanna M. ^[1] ; Cai, Tianxi ^[2] ; Gruber, Michael J. ^[3] ; Li, Junlong ^[2] ; de Jonge, Peter ^[1] ; Petukhova, Maria ^[3] ; Rose, Sherri ^[3] ; Sampson, Nancy A. ^[3] ; Schoevers, Robert A. ^[1] ; Wardenaar, Klaas J. ^[1] ; Wilcox, Marsha A. ^[4] ; Al-Hamzawi, Ali Obaid ^[5] ; Andrade, Laura Helena ^[6] ; Bromet, Evelyn J. ^[7] ; Bunting, Brendan ^[8] ; Fayyad, John ^[9] ; Florescu, Silvia E. ^[10] ; Gureje, Oye ^[11] ; Hu, Chiyi ^[12] ; Huang, Yueqin ^[13] ; Levinson, Daphna ^[14] ; Medina-Mora, Maria Elena ^[15] ; Nakane, Yoshibumi ^[16] ; Posada-Villa, Jose ^[17] ; Scott, Kate M. ^[18] ; Xavier, Miguel ^[19] ; Zarkov, Zahari ^[20] ; Kessler, Ronald C. ^[3]

Afiliação do(s) autor(es):Mostrar menos -

^[1] Univ Groningen, Univ Med Ctr Groningen, Dept Psychiat, Groningen - Netherlands

^[2] Harvard Univ, Sch Med, Dept Biostat, Boston, MA 02115 - USA

^[3] Harvard Univ, Sch Med, Dept Hlth Care Policy, Boston, MA 02115 - USA

^[4] Johnson & Johnson Pharmaceut Res & Dev, Titusville, NJ - USA

^[5] Al Qadisia Univ, Dept Psychiat, Coll Med, Diwania - Iraq

^[6] Univ Sao Paulo, Sch Med, Sect Psychiat Epidemiol LIM 23, Dept & Inst Psychiat, Sao Paulo - Brazil

^[7] SUNY Stony Brook, Stony Brook, NY 11794 - USA

^[8] Univ Ulster, Psychol Res Inst, Coleraine BT52 1SA, Londonderry - North Ireland

^[9] St George Hosp Univ Med Ctr, Inst Dev Res Advocacy & Appl Care, Beirut - Lebanon

^[10] Natl Sch Publ Hlth Management & Profess Dev, Bucharest - Romania

^[11] Univ Coll Ibadan Hosp, Dept Psychiat, Ibadan - Nigeria

^[12] Shenzhen Kangning Hosp, Shenzhen Inst Mental Hlth, Shenzhen, Guangdong - Peoples R China

^[13] Peking Univ, Inst Mental Hlth, Beijing 100871 - Peoples R China

^[14] Minist Hlth, Mental Hlth Serv, Dept Res & Planning, Jerusalem - Israel

^[15] Inst Nacl Psiquiatria Ramon de la Fuente, Mexico City, DF - Mexico

^[16] Nagasaki Int Univ, Fac Human Sociol, Dept Social Work, Nagasaki - Japan

^[17] Univ Colegio Mayor Cundinamarca, Bogota - Colombia

^[18] Univ Otago, Dept Psychol Med, Dunedin - New Zealand

^[19] Univ Nova Lisboa, Dept Mental Hlth, P-1200 Lisbon - Portugal

^[20] Natl Ctr Publ Hlth & Anal, Dept Mental Hlth, Sofia - Bulgaria

• Tipo de documento: Artigo Científico
• Fonte: DEPRESSION AND ANXIETY; v. 31, n. 9, p. 765-777, SEP 2014.
• Citações Web of Science: 19

Resumo

BackgroundVariation in the course of major depressive disorder (MDD) is not strongly predicted by existing subtype distinctions. A new subtyping approach is considered here. MethodsTwo data mining techniques, ensemble recursive partitioning and Lasso generalized linear models (GLMs), followed by k-means cluster analysis are used to search for subtypes based on index episode symptoms predicting subsequent MDD course in the World Mental Health (WMH) surveys. The WMH surveys are community surveys in 16 countries. Lifetime DSM-IV MDD was reported by 8,261 respondents. Retrospectively reported outcomes included measures of persistence (number of years with an episode, number of years with an episode lasting most of the year) and severity (hospitalization for MDD, disability due to MDD). ResultsRecursive partitioning found significant clusters defined by the conjunctions of early onset, suicidality, and anxiety (irritability, panic, nervousness-worry-anxiety) during the index episode. GLMs found additional associations involving a number of individual symptoms. Predicted values of the four outcomes were strongly correlated. Cluster analysis of these predicted values found three clusters having consistently high, intermediate, or low predicted scores across all outcomes. The high-risk cluster (30.0% of respondents) accounted for 52.9-69.7% of high persistence and severity, and it was most strongly predicted by index episode severe dysphoria, suicidality, anxiety, and early onset. A total symptom count, in comparison, was not a significant predictor. ConclusionsDespite being based on retrospective reports, results suggest that useful MDD subtyping distinctions can be made using data mining methods. Further studies are needed to test and expand these results with prospective data. (C) 2014 Wiley Periodicals, Inc. (AU)

• Processo FAPESP: 03/00204-3 - Estudo epidemiológico dos transtornos psiquiátricos na região metropolitana de São Paulo: prevalências, fatores de risco e sobrecarga social e econômica
• Beneficiário: Laura Helena Silveira Guerra de Andrade
• Modalidade de apoio: Auxílio à Pesquisa - Temático