Targeted Prescription of Cognitive-Behavioral Therapy Versus Person-Centered Counseling for Depression Using a Machine Learning Approach

Objective: Depression is a highly common mental disorder and a major cause of disability worldwide. Several psychological interventions are available, but there is a lack of evidence to decide which treatment works best for whom. This study aimed to identify subgroups of patients who respond differentially to cognitive-behavioral therapy (CBT) or person-centered counseling for depression (CD). Method: This was a retrospective analysis of archival routine practice data for 1,435 patients who received either CBT (N = 1,104) or CfD (N = 331) in primary care. The main outcome was posttreatment reliable and clinically significant improvement (RCSI) in the PHQ-9 depression measure. A targeted prescription algorithm was developed in a training sample (N = 1,085) using a supervised machine learning approach (elastic net with optimal scaling). The clinical utility of the algorithm was examined in a statistically independent test sample (N = 350) using chi-square analysis and odds ratios. Results: Cases in the test sample that received their model-indicated optimal treatment had a significantly higher RCSI rate (62.5%) compared to those who received the suboptimal treatment (41.7%); chi(2) (df = 1) = 4.79, p = .03, OR = 2.33 (95% CI [1.09, 5.02]). Conclusion: Targeted prescription has the potential to make best use of currently available evidence-based treatments, improving outcomes for patients at no additional cost to psychological services.