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

BMC Medicine

卷:19

N. Wiles¹  D. Kessler²  S. Pilling³  J. E. J. Buckman³  C. O’Driscoll³  R. Saunders³  E. I. Fried⁴  S. Gilbody⁵  Z. D. Cohen⁶  E. Watkins⁷  S. D. Hollon⁸  G. Lewis⁹  T. Kendrick¹⁰  R. J. DeRubeis¹¹  G. Ambler¹² 

[1] Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol;

[2] Centre for Academic Primary Care, Population Health Sciences, Bristol Medical School, University of Bristol;

[3] Centre for Outcomes Research and Effectiveness (CORE), Research Department of Clinical, Educational & Health Psychology, University College London;

[4] Department of Clinical Psychology, Leiden University;

[5] Department of Health Sciences, University of York;

[6] Department of Psychiatry, University of California, Los Angeles;

[7] Department of Psychology, University of Exeter;

[8] Department of Psychology, Vanderbilt University;

[9] Division of Psychiatry, University College London;

[10] Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton, Aldermoor Health Centre;

[11] School of Arts and Sciences, Department of Psychology;

[12] Statistical Science, University College London;

关键词: Item level analysis;    Network modelling;    Comorbidity;    Depression;    Anxiety;   

DOI  :  10.1186/s12916-021-01971-0

来源: DOAJ

【 摘 要 】

Abstract Background 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.

【 授权许可】

Unknown