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

<b><i>Background:</i></b> 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. <b><i>Methods:</i></b> 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. <b><i>Results:</i></b> 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. <b><i>Conclusions:</i></b> Individual participant data network metaregression enables treatment recommendations based on individual patient characteristics.

<b><i>研究背景：</i></b> 持续性抑郁障碍（Persistent Depressive Disorder）患病率高、致残性强，且往往难以治疗。认知行为分析系统心理治疗（Cognitive-Behavioral Analysis System of Psychotherapy，CBASP）是目前唯一专为该病症治疗研发的心理治疗手段。然而，目前尚不明确CBASP、抗抑郁药物治疗及其联合治疗中哪种方案疗效最优，也不清楚其适配的患者群体类型。本研究旨在基于个体参与者数据网络元回归（Individual Participant Data Network Meta-Regression）构建个性化预测模型，以助力治疗选择中的共同决策过程，实现治疗方案与患者特征及偏好的匹配。<b><i>研究方法：</i></b> 本研究全面检索了对比CBASP、药物治疗及其联合治疗中任意两种方案的随机对照试验（Randomized Controlled Trial，RCT），并从已纳入的试验中收集个体参与者数据。本研究的主要结局指标包括：疗效方面以抑郁症状严重程度的减分作为评估标准，治疗可接受性方面以任何原因导致的脱落率作为评估标准。<b><i>研究结果：</i></b> 本次分析共纳入3项已识别的研究，累计纳入1036名参与者。整体而言，联合治疗方案在疗效与治疗可接受性上均显著优于两种单一治疗方案，而两种单一治疗方案的疗效与可接受性基本相当。基线抑郁水平、焦虑水平、既往药物治疗史、年龄及抑郁亚型会对两种单一治疗方案的相对疗效产生调节作用，这意味着对于特定亚组患者，单独使用药物治疗或单独使用CBASP均为值得推荐的治疗选择——此类方案成本更低、不良反应可能更少，且更贴合患者个体偏好。本研究开发了一款交互式网页应用（https://kokoro.med.kyoto-u.ac.jp/CBASP/prediction/），可针对患者特征的所有可能组合展示预测的疾病病程。<b><i>研究结论：</i></b> 个体参与者数据网络元回归可实现基于患者个体特征的治疗方案推荐。