Moriarty, A.S. orcid.org/0000-0003-0770-3262, Paton, L.W. orcid.org/0000-0002-3328-5634, Snell, K.I.E. et al. (14 more authors) (2024) Development and validation of a prognostic model to predict relapse in adults with remitted depression in primary care: secondary analysis of pooled individual participant data from multiple studies. BMJ Mental Health, 27. e301226. ISSN 1362-0347
Abstract
Background Relapse of depression is common and contributes to the overall associated morbidity and burden. We lack evidence-based tools to estimate an individual’s risk of relapse after treatment in primary care, which may help us more effectively target relapse prevention.
Objective The objective was to develop and validate a prognostic model to predict risk of relapse of depression in primary care.
Methods Multilevel logistic regression models were developed, using individual participant data from seven primary care-based studies (n=1244), to predict relapse of depression. The model was internally validated using bootstrapping, and generalisability was explored using internal–external cross-validation.
Findings Residual depressive symptoms (OR: 1.13 (95% CI: 1.07 to 1.20), p<0.001) and baseline depression severity (OR: 1.07 (1.04 to 1.11), p<0.001) were associated with relapse. The validated model had low discrimination (C-statistic 0.60 (0.55–0.65)) and miscalibration concerns (calibration slope 0.81 (0.31–1.31)). On secondary analysis, being in a relationship was associated with reduced risk of relapse (OR: 0.43 (0.28–0.67), p<0.001); this remained statistically significant after correction for multiple significance testing.
Conclusions We could not predict risk of depression relapse with sufficient accuracy in primary care data, using routinely recorded measures. Relationship status warrants further research to explore its role as a prognostic factor for relapse.
Clinical implications Until we can accurately stratify patients according to risk of relapse, a universal approach to relapse prevention may be most beneficial, either during acute-phase treatment or post remission. Where possible, this could be guided by the presence or absence of known prognostic factors (eg, residual depressive symptoms) and targeted towards these.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
Keywords: | Adult psychiatry; Data Interpretation, Statistical; Depression; Humans; Primary Health Care; Female; Recurrence; Male; Prognosis; Middle Aged; Adult; Depression; Aged; Secondary Prevention; Depressive Disorder |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > Department of Psychology (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 12 Nov 2024 11:59 |
Last Modified: | 12 Nov 2024 11:59 |
Published Version: | http://dx.doi.org/10.1136/bmjment-2024-301226 |
Status: | Published |
Publisher: | BMJ |
Refereed: | Yes |
Identification Number: | 10.1136/bmjment-2024-301226 |
Related URLs: | |
Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:219487 |