Background Relapse of depression is common and contributes to the overall associated morbidity and burden. We lack evidence-based tools to estimate an individuals risk of relapse after treatment in primary care, which may help us more effectively target relapse prevention. Objective 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 [Odds ratio (OR): 1.13 (95% CI: 1.07-1.20), p<0.001] and baseline depression severity [OR: 1.07 (1.04-1.11), 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 cannot currently predict risk of depression relapse with sufficient accuracy in a primary care setting, 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 (e.g. residual depressive symptoms) and targeted towards these.

The authors have declared no competing interest.

https://doi.org/10.1186/s41512-021-00101-x

This report is independent research supported by the National Institute for Health and Care Research (NIHR Doctoral Research Fellowship, Dr Andrew Moriarty, DRF-2018-11-ST2-044). KIES, RDR and LA are supported by funding from the NIHR Birmingham Biomedical Research Centre (BRC). RDR, SG, DAR and CS are NIHR Senior Investigators. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care.

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