The importance of transdiagnostic symptom level assessment to understanding prognosis for depressed adults: analysis of data from six randomised control trials

C O’Driscoll,R J Derubeis,S Pilling,E Watkins,N Wiles,R Saunders,T Kendrick,G Lewis,D Kessler,S Gilbody,E I Fried,J E J Buckman,G Ambler,S D Hollon,Z D Cohen

doi:10.1186/s12916-021-01971-0

Abstract

BackgroundDepression is commonly perceived as a single underlying disease with a number of potential treatment options. However, patients with major depression differ dramatically in their symptom presentation and comorbidities, e.g. with anxiety disorders. There are also large variations in treatment outcomes and associations of some anxiety comorbidities with poorer prognoses, but limited understanding as to why, and little information to inform the clinical management of depression. There is a need to improve our understanding of depression, incorporating anxiety comorbidity, and consider the association of a wide range of symptoms with treatment outcomes.MethodIndividual patient data from six RCTs of depressed patients (total n = 2858) were used to estimate the differential impact symptoms have on outcomes at three post intervention time points using individual items and sum scores. Symptom networks (graphical Gaussian model) were estimated to explore the functional relations among symptoms of depression and anxiety and compare networks for treatment remitters and those with persistent symptoms to identify potential prognostic indicators.ResultsItem-level prediction performed similarly to sum scores when predicting outcomes at 3 to 4 months and 6 to 8 months, but outperformed sum scores for 9 to 12 months. Pessimism emerged as the most important predictive symptom (relative to all other symptoms), across these time points. In the network structure at study entry, symptoms clustered into physical symptoms, cognitive symptoms, and anxiety symptoms. Sadness, pessimism, and indecision acted as bridges between communities, with sadness and failure/worthlessness being the most central (i.e. interconnected) symptoms. Connectivity of networks at study entry did not differ for future remitters vs. those with persistent symptoms.ConclusionThe relative importance of specific symptoms in association with outcomes and the interactions within the network highlight the value of transdiagnostic assessment and formulation of symptoms to both treatment and prognosis. We discuss the potential for complementary statistical approaches to improve our understanding of psychopathology.

Highlights

Depression is commonly perceived as a single underlying disease with a number of potential treatment options
Pessimism, and indecision acted as bridges between communities, with sadness and failure/worthlessness being the most central symptoms
We aimed to address topological overlap using the ‘goldbricker’ function in R [51] with a threshold of 25%, accepting minimal correlation of 0.5.The respective pair of items were combined into a single variable using principal component analysis (PCA) if reasonable to combine from a clinical perspective

Summary

Introduction

Depression is commonly perceived as a single underlying disease with a number of potential treatment options. Patients with major depression differ dramatically in their symptom presentation and comorbidities, e.g. with anxiety disorders. There is a need to improve our understanding of depression, incorporating anxiety comorbidity, and consider the association of a wide range of symptoms with treatment outcomes. The risk of one disorder can increase the risk of another [11], and the same end state may be achieved via many different paths (equifinality) [12, 13]. These disorders are not discrete entities and, as such, neglecting the symptomatic heterogeneity discards potential insights [14]

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