Cognitive-Behavioral Analysis System of Psychotherapy, Drug, or Their Combination for Persistent Depressive Disorder: Personalizing the Treatment Choice Using Individual Participant Data Network Metaregression.
- Furukawa TA Department of Health Promotion and Human Behavior, Kyoto University Graduate School of Medicine/School of Public Health, Kyoto, Japan.
- Efthimiou O Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
- Weitz ES Department of Clinical, Neuro- and Developmental Psychology, and EMGO Institute for Health and Care Research, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
- Cipriani A Department of Psychiatry, University of Oxford, Oxford, United Kingdom.
- Keller MB Department of Psychiatry and Human Behavior, Brown University, Providence, Rhode Island, USA.
- Kocsis JH Department of Psychiatry, Weill Cornell Medical College, New York, New York, USA.
- Klein DN Department of Psychology, Stony Brook University, Stony Brook, New York, USA.
- Michalak J Department of Psychology and Psychotherapy, Witten/Herdecke University, Witten, Germany.
- Salanti G Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
- Cuijpers P Department of Clinical, Neuro- and Developmental Psychology, and EMGO Institute for Health and Care Research, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
- Schramm E Department of Psychiatry and Psychotherapy, University Medical Center Freiburg, Freiburg, Germany.
- 2018-05-31
Published in:
- Psychotherapy and psychosomatics. - 2018
Cognitive-behavioral analysis system of psychotherapy
Persistent depressive disorder
Pharmacotherapy
Adult
Antidepressive Agents
Combined Modality Therapy
Depressive Disorder
Female
Humans
Male
Middle Aged
Outcome Assessment, Health Care
Psychotherapy
English
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.
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.
- Language
-
- English
- Open access status
- green
- Identifiers
-
- DOI 10.1159/000489227
- PMID 29847831
- Persistent URL
- https://sonar.ch/global/documents/278771
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