ArticleDepression and anxiety2014
Major depressive disorder subtypes to predict long-term course.
Article in Depression and anxiety, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction models for longitudinal trajectories of depression and anxiety: a systematic review.Journal of affective disorders · 2026Pooled it
- Depression Subtypes in Systolic Heart Failure: A Secondary Analysis From a Randomized Controlled Trial.Journal of the Academy of Consultation-Liaison PsychiatryTrial
- Depression as a Disease of White Matter Network Disruption: Learning From Multiple Sclerosis.Biological psychiatry · 2026Review
- Effective targets of intervention on generalized anxiety symptoms across whole adolescence and emerging adulthood: network analysis and insilico intervention.European child & adolescent psychiatry · 2025Article
- Degree centrality values in the left calcarine as a potential imaging biomarker for anxious major depressive disorder.World journal of psychiatry · 2025Article
- Using machine learning to derive neurobiological subtypes of general psychopathology in late childhood.Journal of psychopathology and clinical science · 2024Article
- Predicting non-response to multimodal day clinic treatment in severely impaired depressed patients: a machine learning approach.Scientific reports · 2022Article
- How well do clinical and demographic characteristics predict Patient Health Questionnaire-9 scores among patients with treatment-resistant major depressive disorder in a real-world setting?Brain and behavior · 2021Article
- Investigation of early and lifetime clinical features and comorbidities for the risk of developing treatment-resistant depression in a 13-year nationwide cohort study.BMC psychiatry · 2020Article
- Approaches to Defining Common and Dissociable Neurobiological Deficits Associated With Psychopathology in Youth.Biological psychiatry · 2020Review
- A methylation study of long-term depression risk.Molecular psychiatry · 2020Article
- Distinct Symptom-Specific Treatment Targets for Circuit-Based Neuromodulation.The American journal of psychiatry · 2020Article
- Toward a New Model of Understanding, Preventing, and Treating Adolescent Depression Focusing on Exhaustion and Stress.Frontiers in psychiatry · 2020Article
- The Predictive Validity of Machine Learning Models in the Classification and Treatment of Major Depressive Disorder: State of the Art and Future Directions.Frontiers in psychiatry · 2020Article
- Progress in psychoradiology, the clinical application of psychiatric neuroimaging.The British journal of radiology · 2019Review
- Clinical factors predicting treatment resistant depression: affirmative results from the European multicenter study.Acta psychiatrica Scandinavica · 2019Article
- Finding factors that predict treatment-resistant depression: Results of a cohort study.Depression and anxiety · 2018Article
- Transdiagnostic Symptom Clusters and Associations With Brain, Behavior, and Daily Function in Mood, Anxiety, and Trauma Disorders.JAMA psychiatry · 2018Article
- The centrality of DSM and non-DSM depressive symptoms in Han Chinese women with major depression.Journal of affective disorders · 2018Article
- Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder.Epidemiology and psychiatric sciences · 2017Review
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Authors and funding
28 authors.
Funding
Abstract
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.
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