Major Depressive Disorder Subtypes to Predict Long-Term Course

Abstract

Background Variation in 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 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 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.

The effects of co-morbidity in defining major depression subtypes associated with long-term course and severity

“…As in the earlier van Loo analysis (van Loo et al ., 2014), inspection of mean percentile scores for solutions in the range between three and eight clusters showed that all solutions defined one class that had the highest mean scores on all outcomes, a second class that had the lowest mean scores on all outcomes, and between one and six other classes that had intermediate mean scores on all outcomes. (Figure 1a–1f) Based on this observation, alternative three-cluster solutions were constructed from the original four-cluster through eight-cluster solutions by collapsing the intermediate clusters in each solution.…”

“…(More detailed information on the descriptive characteristics of the sample is presented by van Loo et al . (van Loo et al ., 2014). …”

“…We followed van Loo and colleagues (van Loo et al ., 2014) in predicting persistence and chronicity in the subsamples of respondents where AAI-AOO was either 10+ years (chronicity) or 15+ years (persistence) based on preliminary inspection carried out by van Loo showing that outcome scores stabilized after these cut-points. The chronicity and persistence models both used a Poisson link function.…”

“…In the second step, a separate dummy variable for each temporally primary comorbid disorder, a dummy predictor for each of the terminal interactions found in 10% or more of trees, and an offset term for the predicted values of the outcome from the models estimated in the earlier van Loo analysis (van Loo et al ., 2014) were used to predict each outcome in multiple regression analyses. The central difficulty in estimating second-step models was that the predictors were highly inter-correlated, leading to coefficient instability when all predictors were included in a single equation.…”

What are 'good' depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depression in a network analysis

“…A host of studies have documented the important role of anxiety in depressed patients, which predicts reduced treatment efficacy (Fava et al, 2008;Gollan et al, 2012) as well as chronicity of MD, hospitalization, and disability (van Loo et al, 2014). High comorbidity rates between mood and anxiety disorders (Kessler et al, 2005) are well established and traditionally understood as a patients having two distinct diseases.…”