Aifred Health, Montreal, Quebec, Canada.
University of Cambridge, Cambridge, UK.
J Affect Disord. 2022 Nov 15;317:307-318. doi: 10.1016/j.jad.2022.08.041. Epub 2022 Aug 24.
Psychological therapies are effective for treating major depressive disorder, but current clinical guidelines do not provide guidance on the personalization of treatment choice. Established predictors of psychotherapy treatment response could help inform machine learning models aimed at predicting individual patient responses to different therapy options. Here we sought to comprehensively identify known predictors.
EMBASE, Medline, PubMed, PsycINFO were searched for systematic reviews with or without meta-analysis published until June 2020 to identify individual patient-level predictors of response to psychological treatments. 3113 abstracts were identified and 300 articles assessed. We qualitatively synthesized our findings by predictor category (sociodemographic; symptom profile; social support; personality features; affective, cognitive, and behavioural; comorbidities; neuroimaging; genetics) and treatment type. We used the AMSTAR 2 to evaluate the quality of included reviews.
Following screening and full-text assessment, 27 systematic reviews including 12 meta-analyses were eligible for inclusion. 74 predictors emerged for various psychological treatments, primarily cognitive behavioural therapy, interpersonal therapy, and mindfulness-based cognitive therapy.
A paucity of studies examining predictors of psychological treatment outcome, as well as methodological heterogeneities and publication biases limit the strength of the identified predictors.
The synthesized predictors could be used to supplement clinical decision-making in selecting psychological therapies based on individual patient characteristics. These predictors could also be used as a priori input features for machine learning models aimed at predicting a given patient's likelihood of response to different treatment options for depression, and may contribute toward the development of patient-specific treatment recommendations in clinical guidelines.
心理疗法对治疗重度抑郁症有效，但目前的临床指南并未提供治疗选择个性化的指导。已确立的心理治疗反应预测因子有助于为旨在预测不同治疗选择对个体患者反应的机器学习模型提供信息。在这里，我们试图全面识别已知的预测因子。
检索 EMBASE、Medline、PubMed 和 PsycINFO，以查找截至 2020 年 6 月发表的有或没有荟萃分析的系统评价，以确定对心理治疗有反应的个体患者水平预测因子。确定了 3113 篇摘要，并评估了 300 篇文章。我们通过预测因子类别（社会人口统计学；症状特征；社会支持；人格特征；情感、认知和行为；合并症；神经影像学；遗传学）和治疗类型对我们的发现进行了定性综合。我们使用 AMSTAR 2 评估了纳入的综述的质量。
经过筛选和全文评估，有 27 项系统评价（包括 12 项荟萃分析）符合纳入标准。针对各种心理治疗方法，主要是认知行为疗法、人际治疗和正念认知疗法，出现了 74 个预测因子。
研究预测心理治疗结果的预测因子的研究很少，以及方法学的异质性和发表偏倚限制了所确定的预测因子的强度。
综合预测因子可用于根据个体患者特征补充选择心理治疗的临床决策。这些预测因子还可以作为机器学习模型的先验输入特征，旨在预测特定患者对不同抑郁治疗选择的反应可能性，并可能有助于在临床指南中制定针对患者的治疗建议。
