Data-driven personalized medicine approaches to cognitive-behavioral therapy allocation in a large sample: A reanalysis of the ENRICHED study.

BACKGROUND: Although effective treatments for common mental health problems are available, individual responses to treatments are difficult to predict. Treatment efficacy can be optimized by targeting interventions using individual predictions of treatment outcomes. The aim of this study was to develop a prediction algorithm using data from one of the largest randomized clinical trials on psychological interventions for common mental health problems.

METHODS: This is a secondary analysis of the Enhancing Recovery in Coronary Heart Disease study investigating the effectiveness of cognitive behavioral therapy (CBT) and care as usual (CAU) for depression and low perceived social support following acute myocardial infarction. 2481 participants were randomly assigned to CBT and CAU. Baseline social-demographics, depression characteristics, comorbid symptoms, and stress and adversity measures were used to build an algorithm predicting post-treatment depression severity using elastic net regularization. Performance and generalizability of this algorithm were determined in a hold-out sample (n = 1203).

RESULTS: Treatment matching based on predictions in the hold-out sample resulted in inconsistent and small effects (d = 0.15), that were more pronounced for individuals matched to CBT (d = 0.22). We identified a small subgroup of individuals for which CBT did not appear more efficacious than CAU.

LIMITATIONS: Limitations are a poorly defined CAU condition, a low-severity sample, specific exclusion criteria and unavailability of certain baseline variables.

CONCLUSIONS: Small matching effects are likely a realistic representation of the performance and generalizability of multivariable prediction algorithms based on clinical measures. Results indicate that future work and new approaches are needed.