ArticlePsychological medicine2014
The effects of co-morbidity in defining major depression subtypes associated with long-term course and severity.
Article in Psychological medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- Prediction models for longitudinal trajectories of depression and anxiety: a systematic review.Journal of affective disorders · 2026Pooled it
- Predicting non-response to multimodal day clinic treatment in severely impaired depressed patients: a machine learning approach.Scientific reports · 2022Article
- A methylation study of long-term depression risk.Molecular 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
- Treatment response classes in major depressive disorder identified by model-based clustering and validated by clinical prediction models.Translational psychiatry · 2019Article
- Robust symptom networks in recurrent major depression across different levels of genetic and environmental risk.Journal of affective disorders · 2018Article
- Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder.Epidemiology and psychiatric sciences · 2017Review
- Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports.Molecular psychiatry · 2016Article
- Concordance between Composite International Diagnostic Interview and self-reports of depressive symptoms: a re-analysis.International journal of methods in psychiatric research · 2015Article
- Multiple risk factors predict recurrence of major depressive disorder in women.Journal of affective disorders · 2015Article
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Authors and funding
31 authors at 11 institutions in 7 countries.
Funding
Abstract
backgroundAlthough 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.
methodData 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.
resultsPredicted 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.
conclusionsAlthough 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.
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