The development and internal evaluation of a predictive model to identify for whom Mindfulness-Based Cognitive Therapy (MBCT) offers superior relapse prevention for recurrent depression versus maintenance antidepressant medication

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 data from the PREVENT trial (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: 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 to those who maintained ADM (48% vs. 70% relapse, respectively; superior survival times [z=-2.7, p=.008]). For individuals with moderate-to-good ADM prognosis, 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.

Fig. 1.

Schematic of cross-validation procedure for producing antidepressant medication (ADM) predictions for the full analysis sample. Ten key steps in the procedure are indicated by circled numbers. Step 1 (10-fold cross-validation [CV]): The main analysis sample was separated into ADM and mindfulness-based cognitive therapy (MBCT) samples, each of which was then split into 10 subgroups, balanced on outcomes. Step 2: The ADM sample was separated into its first train-test samples, and the first of the 10 subgroups was held out as ADM test sample (1); the other nine subgroups constituted ADM training sample (1). Steps 3 and 4: ADM training sample (1) was then itself split into 10 subgroups, and parameter tuning was performed using internal 10-fold CV; this entire process was repeated three times using different random permutations of the internal 10-fold CV of ADM training sample (1). Step 5 (hyperparameter optimization): The optimal alpha (a) and lambda (λ) were selected and used in Step 6 (model specification), in which elastic-net regularized regression (ENRR) was applied to the entire ADM training sample (1) to derive the ADM training sample (1) Model. Step 7^a: This model was then used to generate factual predictions for the held-out ADM test sample (1) and to generate counterfactual predictions (Step 7^b) for the entire MBCT sample. Step 8: Steps 2 through 7 were then repeated nine more times to complete the 10-fold CV. Step 9^a: The resulting set of (protected) factual predictions for the entire ADM sample (likelihood of relapse in ADM) were then evaluated using the area under the receiver operating characteristic curve. Step 9^b: The set of 10 (protected) counterfactual predictions for each individual in the MBCT sample (likelihood of relapse if they had received ADM) were averaged, which resulted in a set of averaged “ensemble” counterfactual predictions for the MBCT sample. Step 10: The ADM and MBCT samples and their ADM predictions were then recombined, which resulted in protected prognoses under ADM for the full analysis sample.

Fig. 2.

Probability of relapse in the ADM model. The graph in (a) shows the area under the receiver-operating-characteristic (ROC) curve (AUC), which delineates the relative sensitivity (true-positive rate) and specificity (false-positive rate) of the prognostic multivariable antidepressant medication (ADM) elastic-net model. The AUC (red line) is plotted against the straight gray line, which represents the threshold at which the model has no predictive utility. The gray line indicates the likelihood that someone above and below that threshold on the prognostic index has an equal likelihood of relapse. That is, the larger (farther away from the gray line) the AUC, the greater a model’s predictive utility. The graph in (b) plots the predicted survival curves for time (measured in days) to depressive relapse over the 2-year follow-up period for each ADM-prognosis group (poor, moderate, good) as a function of the treatment they received (mindfulness-based cognitive therapy [MBCT] or ADM). The graph in (c) shows the observed relapse rates over the 2-year follow-up as a function of the ADM relapse risk, separately by treatment received.