Applying precision methods to treatment selection for moderate/severe depression in person-centered experiential therapy or cognitive behavioral therapy.

IF 2.6 1区 心理学 Q2 PSYCHOLOGY, CLINICAL Psychotherapy Research Pub Date : 2024-11-01 Epub Date: 2023-11-02 DOI:10.1080/10503307.2023.2269297
Danilo Moggia, David Saxon, Wolfgang Lutz, Gillian E Hardy, Michael Barkham
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Abstract

Objective: To develop two prediction algorithms recommending person-centered experiential therapy (PCET) or cognitive-behavioral therapy (CBT) for patients with depression: (1) a full data model using multiple trial-based and routine variables, and (2) a routine data model using only variables available in the English NHS Talking Therapies program.

Method: Data was used from the PRaCTICED trial comparing PCET vs. CBT for 255 patients meeting a diagnosis of moderate or severe depression. Separate full and routine data models were derived and the latter tested in an external data sample.

Results: The full data model provided the better prediction, yielding a significant difference in outcome between patients receiving their optimal vs. non-optimal treatment at 6- (Cohen's d = .65 [.40, .91]) and 12 months (d = .85 [.59, 1.10]) post-randomization. The routine data model performed similarly in the training and test samples with non-significant effect sizes, d = .19 [-.05, .44] and d = .21 [-.00, .43], respectively. For patients with the strongest treatment matching (d ≥ 0.3), the resulting effect size was significant, d = .38 [.11, 64].

Conclusion: A treatment selection algorithm might be used to recommend PCET or CBT. Although the overall effects were small, targeted matching yielded somewhat larger effects.

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应用精确方法选择以人为中心的体验疗法或认知行为疗法治疗中重度抑郁症。
目的:开发两种预测算法,为抑郁症患者推荐以人为中心的体验疗法(PCET)或认知行为疗法(CBT):(1)使用多个基于试验和常规变量的完整数据模型,以及(2)仅使用英国NHS Talking Therapies计划中可用变量的常规数据模型。方法:使用来自PRaCTICED试验的数据,比较255名诊断为中度或重度抑郁症的患者的PCET与CBT。导出了单独的完整数据模型和常规数据模型,并在外部数据样本中对后者进行了测试。结果:全数据模型提供了更好的预测,在6岁时接受最佳治疗与非最佳治疗的患者之间产生了显著的结果差异(Cohen’s d = .65[40,.91])和12个月(d = .85[.59,1.10])。在具有非显著效应大小的训练和测试样本中类似地执行的常规数据模型,d = .19[-.05,.44]和d = .21[-.00,.43]。对于具有最强治疗匹配(d ≥ 0.3),得到的效应大小是显著的,d = .38[11,64]。结论:治疗选择算法可用于推荐PCET或CBT。尽管总体效果很小,但有针对性的匹配产生了更大的效果。
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来源期刊
Psychotherapy Research
Psychotherapy Research PSYCHOLOGY, CLINICAL-
CiteScore
7.80
自引率
10.30%
发文量
68
期刊介绍: Psychotherapy Research seeks to enhance the development, scientific quality, and social relevance of psychotherapy research and to foster the use of research findings in practice, education, and policy formulation. The Journal publishes reports of original research on all aspects of psychotherapy, including its outcomes, its processes, education of practitioners, and delivery of services. It also publishes methodological, theoretical, and review articles of direct relevance to psychotherapy research. The Journal is addressed to an international, interdisciplinary audience and welcomes submissions dealing with diverse theoretical orientations, treatment modalities.
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