Background: Persistent depressive disorder is prevalent, disabling, and often difficult to treat. The cognitive-behavioral analysis system of psychotherapy (CBASP) is the only psychotherapy specifically developed for its treatment. However, we do not know which of CBASP, antidepressant pharmacotherapy, or their combination is the most efficacious and for which types of patients. This study aims to present personalized prediction models to facilitate shared decision-making in treatment choices to match patients’ characteristics and preferences based on individual participant data network metaregression. Methods: We conducted a comprehensive search for randomized controlled trials comparing any two of CBASP, pharmacotherapy, or their combination and sought individual participant data from identified trials. The primary outcomes were reduction in depressive symptom severity for efficacy and dropouts due to any reason for treatment acceptability. Results: All 3 identified studies (1,036 participants) were included in the present analyses. On average, the combination therapy showed significant superiority over both monotherapies in terms of efficacy and acceptability, while the latter 2 treatments showed essentially similar results. Baseline depression, anxiety, prior pharmacotherapy, age, and depression subtypes moderated their relative efficacy, which indicated that for certain subgroups of patients either drug therapy or CBASP alone was a recommendable treatment option that is less costly, may have fewer adverse effects and match an individual patient’s preferences. An interactive web app (https://kokoro.med.kyoto-u.ac.jp/CBASP/prediction/) shows the predicted disease course for all possible combinations of patient characteristics. Conclusions: Individual participant data network metaregression enables treatment recommendations based on individual patient characteristics....

Cognitive-Behavioral Analysis System of Psychotherapy, Drug, or Their Combination for Persistent Depressive Disorder: Personalizing the Treatment Choice Using Individual Participant Data Network Metaregression

10.1159/000489227