A Prognostic Model to PREDICT Relapse of Depression in Primary Care

Overview

The investigators aim to develop a prognostic model to predict the risk of relapse within 6-8 months of patients entering remission. The long-term objective is to facilitate more efficient targeting of evidence-based relapse prevention strategies to these patients.

Most patients with depression are treated in primary care by general practitioners (GPs). Relapse of depression is common and leads to considerable morbidity and decreased quality of life for patients. Estimates suggest that at least 50% of patients treated for depression will relapse after a single episode. The majority of these will relapse within 6 months and the risk of relapse increases for each successive episode of depression. GPs see a largely undifferentiated case-mix of patients and, once patients with depression reach remission, there is limited guidance and no validated tools to help GPs stratify patients according to risk of relapse.This study will potentially derive a statistical model to predict relapse of depression in remitted depressed patients in primary care. The investigators have created a longitudinal cohort of patients drawn from seven randomised controlled trials (RCTs) of non-pharmacological primary care-based interventions for depression and one longitudinal cohort study. The investigators will use logistic regression to predict the outcome of relapse of depression within 6-8 months. The investigators plan to include the following well-evidenced relapse predictors in the model: residual depressive symptoms; number of previous episodes of depression; co-morbid anxiety; and severity of the index episode. They will also control for RCT intervention received by participants. If sample size and availability of predictor information allows, the investigators will include the following predictors in an exploratory analysis: age; relationship status; multi-morbidity; employment status; gender; and ethnicity. Generalisability will be assessed through internal-external cross-validation and net benefit will be explored.

Conditions

Eligibility

Locations (1)

Outcomes