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 behavioural therapy (CBT) or person-centred counselling for depression (CfD).

Methods: This was a retrospective analysis of archival routine practice data for 1435 patients who received either CBT (N=1104) or CfD (N=331) in primary care. The main outcome was post-treatment reliable and clinically significant improvement (RCSI) in the PHQ-9 depression measure. A targeted prescription algorithm was developed in a training sample (N=1085) 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%); x2 (DF = 1) = 4.79, p = .03, OR = 2.33 (95% CI = 1.09, 5.02).

Conclusions: 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.