Premium
MAJOR DEPRESSIVE DISORDER SUBTYPES TO PREDICT LONG‐TERM COURSE
Author(s) -
Loo Hanna M.,
Cai Tianxi,
Gruber Michael J.,
Li Junlong,
Jonge Peter,
Petukhova Maria,
Rose Sherri,
Sampson Nancy A.,
Schoevers Robert A.,
Wardenaar Klaas J.,
Wilcox Marsha A.,
AlHamzawi Ali Obaid,
Andrade Laura Helena,
Bromet Evelyn J.,
Bunting Brendan,
Fayyad John,
Florescu Silvia E.,
Gureje Oye,
Hu Chiyi,
Huang Yueqin,
Levinson Daphna,
MedinaMora Maria Elena,
Nakane Yoshibumi,
PosadaVilla Jose,
Scott Kate M.,
Xavier Miguel,
Zarkov Zahari,
Kessler Ronald C.
Publication year - 2014
Publication title -
depression and anxiety
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.634
H-Index - 129
eISSN - 1520-6394
pISSN - 1091-4269
DOI - 10.1002/da.22233
Subject(s) - panic disorder , anxiety , major depressive disorder , psychology , subtyping , generalized anxiety disorder , clinical psychology , dysphoria , suicidal ideation , worry , irritability , psychiatry , persistence (discontinuity) , anxiety disorder , cluster (spacecraft) , medicine , poison control , injury prevention , mood , geotechnical engineering , environmental health , computer science , engineering , programming language
Background Variation in the course of major depressive disorder (MDD) is not strongly predicted by existing subtype distinctions. A new subtyping approach is considered here. Methods Two 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). Results Recursive 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. Conclusions Despite 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.