Course trajectories of unipolar depressive disorders identified by latent class growth analysis
SourcePsychological Medicine, 42, 7, (2012), pp. 1383-1396
Article / Letter to editor
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SW OZ BSI KLP
SubjectExperimental Psychopathology and Treatment
Background. Current classification of unipolar depression reflects the idea that prognosis is essential. However, do DSM categories of major depressive disorder (MDD), dysthymic disorder (Dysth) and double depression (DD=MDD+Dysth) indeed adequately represent clinically relevant course trajectories of unipolar depression ? Our aim was to test DSM categories (MDD, Dysth and DD) in comparison with empirically derived prognostic categories, using a prospectively followed cohort of depressed patients. Method. A large sample (n=804) of out-patients with unipolar depression were derived from a prospective cohort study, the Netherlands Study of Depression and Anxiety (NESDA). Using latent class growth analysis (LCGA), empirically derived 2-year course trajectories were constructed. These were compared with DSM diagnoses and a wider set of putative predictors for class membership. Results. Five course trajectories were identified, ranging from mild severity and rapid remission to high severity and chronic course trajectory. Contrary to expectations, more than 50% of Dysth and DD were allocated to classes with favorable course trajectories, suggesting that current DSM categories do not adequately represent course trajectories. The class with the most favorable course trajectory differed on several characteristics from other classes (younger age, more females, less childhood adversity, less somatic illnesses, lower neuroticism, higher extraversion). Older age, earlier age of onset and lower extraversion predicted poorest course trajectory. Conclusions. MDD, Dysth and DD did not adequately match empirically derived course trajectories for unipolar depression. For the future classification of unipolar depression, it may be wise to retain the larger, heterogeneous category of unipolar depression, adopting cross-cutting dimensions of severity and duration to further characterize patients.
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