Diagnostic heterogeneity in psychiatry: towards an empirical solution

Klaas J Wardenaar,Peter De Jonge

doi:10.1186/1741-7015-11-201

Klaas J Wardenaar, Peter De Jonge

Open Access

https://doi.org/10.1186/1741-7015-11-201

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Abstract

The launch of the 5th version of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) has sparked a debate about the current approach to psychiatric classification. The most basic and enduring problem of the DSM is that its classifications are heterogeneous clinical descriptions rather than valid diagnoses, which hampers scientific progress. Therefore, more homogeneous evidence-based diagnostic entities should be developed. To this end, data-driven techniques, such as latent class- and factor analyses, have already been widely applied. However, these techniques are insufficient to account for all relevant levels of heterogeneity, among real-life individuals. There is heterogeneity across persons (p:for example, subgroups), across symptoms (s:for example, symptom dimensions) and over time (t:for example, course-trajectories) and these cannot be regarded separately. Psychiatry should upgrade to techniques that can analyze multi-mode (p-by-s-by-t) data and can incorporate all of these levels at the same time to identify optimal homogeneous subgroups (for example, groups with similar profiles/connectivity of symptomatology and similar course). For these purposes, Multimode Principal Component Analysis and (Mixture)-Graphical Modeling may be promising techniques.

Highlights

With the launch of the fifth version of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), the debate about current psychiatric diagnostics has come into the limelight again, focusing on specific alterations in the DSM-5, such as the deletion of pervasive developmental disorder not otherwise specified (PDD-NOS) and Asperger’s Disorder [1,2] and the inclusion of mourning in major depressive disorder (MDD)
We argue that the development of more valid psychiatric classifications is important in
The problem of diagnostic heterogeneity Current psychopathological concepts are heterogeneous by default whichrestricts their usefulness for research [6,7]

Summary

Introduction

With the launch of the fifth version of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), the debate about current psychiatric diagnostics has come into the limelight again, focusing on specific alterations in the DSM-5, such as the deletion of pervasive developmental disorder not otherwise specified (PDD-NOS) and Asperger’s Disorder [1,2] and the inclusion of mourning in major depressive disorder (MDD). For instance, subtypes have been identified with latent class analyses (LCA) [8,9], symptom-dimensions with factor analyses (FA) [10,11] and course-trajectory groups with mixture growth analyses (MGA) [12,13]. These studies tackle only one aspect of heterogeneity at a time. Longitudinal studies of heterogeneity (for example, MGA) apply to the p-by-t slice, modeling classes-based temporal trajectories on one or more variables (Figure 1D). Incomplete, this summary shows that none of these models incorporate all three sources of variation. It is a fully developed technique that can be used to explore threedimensional psychopathology data for more homogeneous diagnostic entities

D Growth Mixture Analysis

Conclusions

Wakefield JC

11. Shafer AB

14. Cattell RB