Predicting Relapse of Depressive Episodes During Maintenance Treatment: The Canadian Biomarker Integration Network in Depression (CAN-BIND) Wellness Monitoring in Major Depressive Disorder Study: Prédire la rechute d’épisodes dépressifs pendant le tr

BackgroundRelapse rates in major depressive disorder (MDD) remain high even after treatment to remission. Identifying predictors of relapse is, therefore, crucial for improving maintenance strategies and preventing future episodes. Remote data collection and sensing technologies may allow for more comprehensive and longitudinal assessment of potential predictors.MethodsThe Canadian Biomarker Integration Network in Depression Wellness Monitoring for MDD (CBN-WELL) study was a prospective, multicentre observational study with an aim to identify biomarkers associated with relapse in patients on maintenance treatment for MDD. Participants had a DSM-5-TR diagnosis of MDD in remission and a Montgomery-Åsberg Depression Rating Scale (MADRS) score ≤14. Participants remained on their baseline medication regimens and were followed bimonthly for up to 2 years. Relapse criteria included MADRS > 22 for 2 consecutive weeks, suicidality or hospitalization, and initiation or change in medication for worsening symptoms. Data collection included clinical assessments, self-report questionnaires, and remote monitoring using wrist-worn actigraphs and smartphones.ResultsA total of 96 participants had follow-up data. Of these, 28.9% experienced a depressive relapse during the study period, with an average time to relapse of 211 days. Baseline depressive severity, as measured by MADRS, was higher in participants who relapsed compared to those who did not, but few other baseline clinical measures differentiated these groups.ConclusionsIndividuals with MDD in remission continued to have high relapse rates despite maintenance treatment. The paucity of clinical factors that predict relapse underscores the need for biomarkers. The CBN-WELL database can be used for future research to integrate multiple predictive factors and to identify objective measures to predict relapse in individuals.