Depression 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.Method
Individual 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.Results
Item-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.Conclusion
The 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.
Bibliographical noteFunding Information:
1. COBALT: The National Institute for Health Research Health Technology Assessment (NIHR HTA) programme (project number 06/404/02).
2. GENPOD: Medical Research Council and supported by the Mental Health Research Network.
This work was supported by the Wellcome Trust through a Clinical Research Fellowship to JEJB (201292/Z/16/Z), MQ Foundation (for ZDC: MQDS16/72), the Higher Education Funding Council for England, the National Institute of Health Research (NIHR), NIHR University College London Hospitals Biomedical Research Centre (RS and SP), NIHR Biomedical Research Centre at the University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol (NW and DK), University College London (GA, GL), University of Pennsylvania (RDR), Vanderbilt University (SDH), University of Southampton (TK), University of Exeter (EW), and University of York (SG). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.
© 2021, The Author(s).
- item level analysis
- network modelling