Predicting relapse or recurrence of depression: systematic review of prognostic models. (August 2022)

• Record Type: Journal Article
• Authors: Moriarty, Andrew S.; Meader, Nicholas; Snell, Kym I. E.; Riley, Richard D.; Paton, Lewis W.; Dawson, Sarah; Hendon, Jessica; Chew-Graham, Carolyn A.; Gilbody, Simon; Churchill, Rachel; Phillips, Robert S.; Ali, Shehzad; McMillan, Dean
• Is Part Of: British journal of psychiatry. Volume 221:Number 2(2022)
• Page Start: 448
• Page End: 458
• Publication Date: 2022-08
• Subjects: Depressive disorders -- epidemiology -- statistical methodology -- risk assessment -- primary care; Psychiatry -- Periodicals; Psychology, Pathological -- Periodicals; 616.89005
• DOI: 10.1192/bjp.2021.218
• Languages: English
• ISSNs: 0007-1250

Abstract

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.