Data from: Predicting optimal outcomes in cognitive therapy or interpersonal psychotherapy for depressed individuals using the Personalized Advantage Index approach

Introduction: Although psychotherapies for depression produce equivalent outcomes, individual patients respond differently to different therapies. Predictors of outcome have been identified in the context of randomized trials, but this information has not been used to predict which treatment works best for the depressed individual. In this paper, we aim to replicate a recently developed treatment selection method, using data from an RCT comparing the effects of cognitive therapy (CT) and interpersonal psychotherapy (IPT). Methods: 134 depressed patients completed the pre- and post-treatment BDI-II assessment. First, we identified baseline predictors and moderators. Secondly, individual treatment recommendations were generated by combining the identified predictors and moderators in an algorithm that produces the Personalized Advantage Index (PAI), a measure of the predicted advantage in one therapy compared to the other, using standard regression analyses and the leave-one-out cross-validation approach. Results: We found five predictors (gender, employment status, anxiety, personality disorder and quality of life) and six moderators (somatic complaints, cognitive problems, paranoid symptoms, interpersonal self-sacrificing, attributional style and number of life events) of treatment outcome. The mean average PAI value was 8.9 BDI points, and 63% of the sample was predicted to have a clinically meaningful advantage in one of the therapies. Those who were randomized to their predicted optimal treatment (either CT or IPT) had an observed mean end-BDI of 11.8, while those who received their predicted non-optimal treatment had an end-BDI of 17.8 (effect size for the difference = 0.51). Discussion: Depressed patients who were randomized to their predicted optimal treatment fared much better than those randomized to their predicted non-optimal treatment. The PAI provides a great opportunity for formal decision-making to improve individual patient outcomes in depression. Although the utility of the PAI approach will need to be evaluated in prospective research, this study promotes the development of a treatment selection approach that can be used in regular mental health care, advancing the goals of personalized medicine.

引言：尽管针对抑郁症的各类心理治疗均可达到等效的临床疗效，但不同患者对不同治疗方案的响应存在显著个体差异。目前已有随机对照试验（Randomized Controlled Trial, RCT）背景下确立的疗效预测因子，但此类信息尚未被用于预测哪一种治疗方案对特定抑郁症患者最为适配。本研究旨在复现一项近年提出的治疗选择方法，所用数据来自一项比较认知治疗（CT）与人际心理治疗（IPT）疗效的随机对照试验。
方法：本研究共纳入134名抑郁症患者，所有患者均完成了治疗前后的贝克抑郁量表第二版（Beck Depression Inventory-II, BDI-II）评估。首先，我们明确了基线水平的疗效预测因子与调节因子；其次，通过将已识别的预测因子与调节因子整合至算法中，生成个体化治疗推荐方案——该算法可计算个体化优势指数（Personalized Advantage Index, PAI），即两种治疗方案间的预测疗效优势量化指标，分析过程采用标准回归分析与留一交叉验证法。
结果：本研究共识别出5个抑郁症治疗疗效的预测因子（性别、就业状态、焦虑症状、人格障碍与生活质量）以及6个调节因子（躯体主诉、认知功能问题、偏执症状、人际自我牺牲倾向、归因风格与生活事件数量）。个体化优势指数的平均得分为8.9个BDI量表分，且63%的研究样本被预测可从某一种治疗方案中获得具有临床意义的疗效优势。被分配至匹配其预测最优治疗方案（CT或IPT）的患者，其治疗结束时的BDI平均得分为11.8；而被分配至非预测最优治疗方案的患者，其治疗结束时的BDI平均得分为17.8（两组差异的效应量为0.51）。
讨论：被分配至匹配其预测最优治疗方案的抑郁症患者，其临床转归显著优于被分配至非预测最优治疗方案的患者。个体化优势指数为抑郁症患者的规范化治疗决策以改善个体疗效提供了重要契机。尽管个体化优势指数方法的临床效用尚需在前瞻性研究中进一步验证，但本研究推动了可应用于常规精神卫生诊疗的治疗选择方案的发展，助力精准医学目标的实现。