The Development and Internal Evaluation of a Predictive Model to Identify for Whom Mindfulness-Based Cognitive Therapy Offers Superior Relapse Prevention for Recurrent Depression Versus Maintenance Antidepressant Medication
Zachary D. Cohen, Robert J. DeRubeis, Rachel Hayes, Edward Watkins, Glyn Lewis, Richard Byng, Sarah Byford, Catherine Crane, Willem Kuyken, Tim Dalgleish, Susanne Schweizer
Abstract
Depression is highly recurrent, even following successful pharmacological and/or psychological intervention. We aimed to develop clinical prediction models to inform adults with recurrent depression choosing between antidepressant medication (ADM) maintenance or switching to mindfulness-based cognitive therapy (MBCT). Using previously published data ( N = 424), we constructed prognostic models using elastic-net regression that combined demographic, clinical, and psychological factors to predict relapse at 24 months under ADM or MBCT. Only the ADM model (discrimination performance: area under the curve [AUC] = .68) predicted relapse better than baseline depression severity (AUC = .54; one-tailed DeLong’s test: z = 2.8, p = .003). Individuals with the poorest ADM prognoses who switched to MBCT had better outcomes compared with individuals who maintained ADM (48% vs. 70% relapse, respectively; superior survival times, z = −2.7, p = .008). For individuals with moderate to good ADM prognoses, both treatments resulted in similar likelihood of relapse. If replicated, the results suggest that predictive modeling can inform clinical decision-making around relapse prevention in recurrent depression.