The 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
Authors: Moriarty Andrew S , Paton Lewis W , Snell Kym IE , Archer Lucinda , Riley Richard D , Buckman Joshua EJ , Chew-Graham Carolyn A , Gilbody Simon , Ali Shehzad , Pilling Stephen , Meader Nick , Phillips Bob , Coventry Peter A , Delgadillo Jaime , Richards David A , Salisbury Chris , McMillan Dean 
Published: June 2024

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

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.

What is already known on this topic

Relapse contributes to the morbidity and burden associated with depression and, while there is robust research confirming predictors of relapse, individualised risk prediction is a challenge.

What this study adds

We found that it is not possible to accurately predict individualised risk of relapse using prognostic factors that are routinely collected and available in primary care. We found evidence to suggest that relationship status (not being in a relationship) is associated with increased risk of relapse and warrants confirmatory prognostic factor research.

How this study might affect research, practice or policy

Future prognosis research in this area should focus on exploring the feasibility of routinely measuring and documenting additional prognostic factors in primary care (for example, adverse childhood events, relationship status and social support) and including these in prognostic models. Until we can more accurately identify individuals at increased risk of relapse, commonly used acute-phase treatments could be optimised to better prepare for and mitigate the risk of relapse and there is a need for brief, scalable relapse prevention interventions that could be provided more widely.