Abstract
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.
© 2018 S. Karger AG, Basel.
KEYWORDS:
Cognitive-behavioral analysis system of psychotherapy; Persistent depressive disorder; Pharmacotherapy
Original language | English |
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Pages (from-to) | 140-153 |
Number of pages | 14 |
Journal | Psychotherapy and Psychosomatics |
Volume | 87 |
Issue number | 3 |
Early online date | 30 May 2018 |
DOIs | |
Publication status | Published - Jun 2018 |
Funding
This study has been supported in part by JSPS KAKENHI (Grant-in-Aid for Scientific Research) Grant No. 26670314 and 17K19808, and Comprehensive Research on Disability (Grant No. 17dk0307072) from the Japan Agency for Medical Research and Development to T.A.F. G.S. is a Marie Skłodowska-Curie fellow (MSCA-IF-703254). T.A.F. has received lecture fees from Eli Lilly, Janssen, Meiji, Mitsubishi-Tanabe, MSD, and Pfizer. He has received grant or research support from Mitsubishi-Tanabe and Mochida. J.H.K. has received research grants and contracts from AHRQ, NIMH, Burroughs Wellcome Trust, Pritzker Consortium, Rockefeller Treatment Development Fund, and Elan. He has a patent No. 8,853,279 “Method for Determining Sensitivity or Resistance to Compounds That Activate the Brain Serotonin System.” E.S. received book royalties and honoraria for workshops and presentations relating to CBASP. P.C. received grants from the European Union, ZonMw, and PFGV, and he receives expense allowances for his membership of the board of directors of the Dutch Foundation for Mental Health (Fonds Psychische Gezondheid) and a national telephone helpline (called “Korrelatie”) and for serving as chair of the Science Committee of the Council for Care and Research (RZO) of the Dutch Ministry of Defense. All the other authors report no competing interests to declare.
Funders | Funder number |
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Council for Care and Research | |
Dutch Foundation for Mental Health | |
Dutch Ministry of Defense | |
RZO | |
Rockefeller Treatment Development Fund | |
National Institute of Mental Health | |
Agency for Healthcare Research and Quality | |
Burroughs Wellcome Fund | |
Japan Agency for Medical Research and Development | |
H2020 Marie Skłodowska-Curie Actions | MSCA-IF-703254 |
European Commission | |
Japan Society for the Promotion of Science | 17dk0307072, 26670314, 17K19808 |
ZonMw |
Keywords
- Cognitive-behavioral analysis system of psychotherapy
- Persistent depressive disorder
- Pharmacotherapy
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Supplementary Material for: 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, T. A. (Contributor), Efthimiou, O. (Contributor), Weitz, E. S. (Contributor), Cipriani, A. (Contributor), Keller, M. B. (Contributor), Kocsis, J. H. (Contributor), Klein, D. N. (Contributor), Michalak, J. (Contributor), Salanti, G. (Contributor), Cuijpers, P. (Contributor) & Schramm, E. (Contributor), Unknown Publisher, 1 Jun 2018
DOI: 10.6084/m9.figshare.6391226.v2, https://karger.figshare.com/articles/Supplementary_Material_for_Cognitive-Behavioral_Analysis_System_of_Psychotherapy_Drug_or_Their_Combination_for_Persistent_Depressive_Disorder_Personalizing_the_Treatment_Choice_Using_Individual_Participant_Data_Network_Metaregression/6391226/2
Dataset / Software: Dataset
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Supplementary Material for: 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, T. A. (Contributor), Efthimiou, O. (Contributor), Weitz, E. S. (Contributor), Cipriani, A. (Contributor), Keller, M. B. (Contributor), Kocsis, J. H. (Contributor), Klein, D. N. (Contributor), Michalak, J. (Contributor), Salanti, G. (Contributor), Cuijpers, P. (Contributor) & Schramm, E. (Contributor), Unknown Publisher, 1 Jun 2018
DOI: 10.6084/m9.figshare.6391226.v1, https://karger.figshare.com/articles/Supplementary_Material_for_Cognitive-Behavioral_Analysis_System_of_Psychotherapy_Drug_or_Their_Combination_for_Persistent_Depressive_Disorder_Personalizing_the_Treatment_Choice_Using_Individual_Participant_Data_Network_Metaregression/6391226/1
Dataset / Software: Dataset