Cognitive-Behavioral Analysis System of Psychotherapy, Drug, or Their Combination for Persistent Depressive Disorder: Personalizing the Treatment Choice Using Individual Participant Data Network Metaregression
Cognitive-Behavioral Analysis System of Psychotherapy, Drug, or Their Combination for Persistent Depressive Disorder: Personalizing the Treatment Choice Using Individual Participant Data Network Metaregression
复制标题
DOI：
10.1159/000489227
复制
发表时间：
2018-01-01
影响因子：
22.8
通讯作者：
Schramm, Elisabeth
中科院分区：
文献类型：
--
作者：
Furukawa, Toshi A.;Efthimiou, Orestis;Schramm, Elisabeth
背景：持续性抑郁症普遍存在，致残且往往难以治疗。心理治疗的认知行为分析系统（CBASP）是唯一专门针对其治疗而开发的心理治疗方法。然而，我们不知道 CBASP、抗抑郁药物疗法或它们的组合中哪一种最有效以及适用于哪些类型的患者。本研究旨在提出个性化预测模型，以促进治疗选择中的共同决策，以基于个体参与者数据网络元回归来匹配患者的特征和偏好。方法：我们对比较 CBASP、药物疗法或其组合中的任意两种疗法或其组合的随机对照试验进行了全面检索，并从已确定的试验中寻找个体参与者数据。主要结局是降低抑郁症状的严重程度以提高疗效以及由于治疗可接受性的任何原因而退出。结果：所有 3 项已确定的研究（1,036 名受试者）均纳入本分析。平均而言，联合疗法在疗效和可接受性方面均显着优于两种单一疗法，而后两种疗法显示出基本相似的结果。基线抑郁、焦虑、既往药物治疗、年龄和抑郁亚型调节了它们的相对疗效，这表明对于某些亚组患者来说，药物治疗或单独 CBASP 是值得推荐的治疗选择，成本较低，副作用可能较少，并且符合患者个体的偏好。交互式网络应用程序 (https://kokoro.med.kyoto-u.ac.jp/CBASP/prediction/) 显示患者特征所有可能组合的预测病程。结论：个体参与者数据网络元回归可以根据个体患者特征提出治疗建议。 (C) 2018 S. Karger AG，巴塞尔
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. (C) 2018 S. Karger AG, Basel