Moriarty, AS, Meader, N, Snell, KIE ORCID: https://orcid.org/0000-0001-9373-6591, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Paton, LW, Dawson, S, Hendon, J, Chew-Graham, CA ORCID: https://orcid.org/0000-0002-9722-9981, Gilbody, S, Churchill, R, Phillips, RS, Ali, S and McMillan, D (2022) Predicting relapse or recurrence of depression: systematic review of prognostic models. British Journal of Psychiatry.

[img] Text
Prognostic models for depression for BJPsych Reviewer Response (Clean).docx - Accepted Version
Restricted to Repository staff only until 11 July 2022.

Download (192kB)

Abstract

Background Relapse and recurrence of depression are common, contributing to the overall burden of depression globally. Accurate prediction of relapse or recurrence while patients are well would allow the identification of high-risk individuals and may effectively guide the allocation of interventions to prevent relapse and recurrence. Aims To review prognostic models developed to predict the risk of relapse, recurrence, sustained remission, or recovery in adults with remitted major depressive disorder. Method We searched the Cochrane Library (current issue); Ovid MEDLINE (1946 onwards); Ovid Embase (1980 onwards); Ovid PsycINFO (1806 onwards); and Web of Science (1900 onwards) up to May 2021. We included development and external validation studies of multivariable prognostic models. We assessed risk of bias of included studies using the Prediction model risk of bias assessment tool (PROBAST). Results We identified 12 eligible prognostic model studies (11 unique prognostic models): 8 model development-only studies, 3 model development and external validation studies and 1 external validation-only study. Multiple estimates of performance measures were not available and meta-analysis was therefore not necessary. Eleven out of the 12 included studies were assessed as being at high overall risk of bias and none examined clinical utility. Conclusions Due to high risk of bias of the included studies, poor predictive performance and limited external validation of the models identified, presently available clinical prediction models for relapse and recurrence of depression are not yet sufficiently developed for deploying in clinical settings. There is a need for improved prognosis research in this clinical area and future studies should conform to best practice methodological and reporting guidelines.

Item Type: Article
Additional Information: The final version of this article and all relevant information related to it, including copyrights, can be found on the publisher website at; https://www.cambridge.org/core/journals/the-british-journal-of-psychiatry/article/abs/predicting-relapse-or-recurrence-of-depression-systematic-review-of-prognostic-models/64E2A0298FBD0B70E360729993F6371B
Uncontrolled Keywords: Depressive disorders; epidemiology; statistical methodology; risk assessment; primary care
Subjects: B Philosophy. Psychology. Religion > BF Psychology
R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Related URLs:
Depositing User: Symplectic
Date Deposited: 27 Jan 2022 15:12
Last Modified: 21 Feb 2022 14:42
URI: https://eprints.keele.ac.uk/id/eprint/10545

Actions (login required)

View Item View Item