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.
Trial registration: number NCT04666662.
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.
Trial registration: number NCT04666662.
| Original language | English |
|---|---|
| Article number | e301226 |
| Number of pages | 8 |
| Journal | BMJ Mental Health |
| Volume | 27 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 28 Oct 2024 |
Keywords
- Data Interpretation, Statistical
- Depression
- Adult psychiatry