Cognitive-behavioral analysis system of psychotherapy (CBASP), drug, or their combination for persistent depressive disorder: Personalizing the treatment choice using individual participant data network meta-regression

Background: Persistent depressive disorder is prevalent, disabling and often difficult to treat. 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 meta-regression. Methods: We have conducted 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 three 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 two 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 less adverse effects and match 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 meta-regression enables treatment recommendations based on individual patient characteristics.