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

Abstract

Background Although variation in 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 comorbid conditions. The current report presents results on this question. Methods Data come from 8,261 respondents with lifetime DSM-IV MDD in the WHO World Mental Health (WMH) Surveys. Outcomes include 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 comorbidity to predict these outcomes. Results Predicted values were strongly correlated across outcomes. Cluster analysis of predicted values found 3 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 (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 comorbidity as predictors. Conclusions Although 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 is need with prospective data to confirm this preliminary conclusion.

Multiple risk factors predict recurrence of major depressive disorder in women

“…Therefore, we expect the actual performance of the model to lie somewhere in between the estimates in the training and test set, i.e., an AUC within the range of 0.61-0.79. These estimates are similar to AUC's of prediction models, identified with similar statistical methods, for different aspects of course of illness of MD in a large general population study (AUC's 0.62-0.73 in training data) (Wardenaar, et al, 2014) and somewhat lower than AUC estimates in the most comparable study, specifically for the test data (AUC's of 0.72-0.75 in test and training data) (Wang, et al, 2014). We will compare our studies in further detail below.…”

“…Some of the predictors – a large number of episodes, residual symptoms, traumas including childhood adversity and abuse – have been consistently found by previous studies to recurrence of depression (Fava, et al, 2007; Hardeveld, et al, 2010; Hardeveld, et al, 2013; Nanni, et al, 2012; Patten, et al, 2010). Also comorbid anxiety symptoms and a history of (early) anxiety were associated with a severe course of depression with frequent and long-lasting episodes (RW.ERROR - Unable to find reference:590; van Loo, et al, 2014; Wardenaar, et al, 2014). The contribution of other predictors, however, is less consistent in previous studies: associations were significant in some studies, but insignificant in others (such as age of onset, marital status) (Hardeveld, et al, 2010).…”

“…Several recent studies have investigated multivariate prediction algorithms for MD onset (King, et al, 2008; Wang, et al, 2013), MD treatment resistance (Perils, 2013), suicide (Kessler, et al, 2015; Tran, et al, 2014), and persistence and severity of course of MD (van Loo, et al, 2014; Wardenaar, et al, 2014). One study developed a multivariate prediction model for recurrence of MD, which resulted in an algorithm with 19 unique factors and a C-statistics of 0.72 in independent test data (Wang, et al, 2014).…”

Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder

“…Initial exploratory analyses based on unsupervised clustering found patterns suggesting that significant associations existed between retrospective reports about incident episode symptoms and subsequent illness course in the NCS-R data. These results were sufficiently promising that subsequent supervised machine learning analyses were carried out to maximize the prediction of MDD persistence-severity from retrospectively-reported information on incident episode symptoms in the much larger WMH series, where there were 8,261 respondents with lifetime DSM-IV MDD (van Loo et al, 2014; Wardenaar et al, 2014). …”

Concordance between Composite International Diagnostic Interview and self-reports of depressive symptoms: a re-analysis

“…A relevant unanswered question then becomes: how good questionnaire‐to‐interview concordance could be achieved, were one to maximize it using all the information from the individual items? We answer to this question using computationally intensive classification techniques, because the “data mining” algorithms of explorative statistics (Hastie et al ., ; Kuhn and Johnson, ) can provide additional information that would not be readily available from classical confirmatory statistics and psychiatric intuition (Baca‐García et al ., ; Wardenaar et al ., ). These results are compared with a previous classification study that used the same data and more classic methods (Aalto et al ., ).…”