Although effective treatments for common mental health problems are available, individual responses to treatments are difficult to predict. Treatment efficacy could be optimized by targeting interventions using individual predictions of treatment outcomes. The aim of this study was to develop a prediction algorithm using data from one of the largest randomized controlled trials on psychological interventions for common mental health problems. This is a secondary analysis of the Enhancing Recovery in Coronary Heart Disease study investigating the effectiveness of cognitive behavioral therapy (CBT) and care as usual (CAU) for depression and low perceived social support following acute myocardial infarction. 2481 participants were randomly assigned to CBT and CAU. Baseline social-demographics, depression characteristics, comorbid symptoms, and stress and adversity measures were used to build an algorithm predicting post-treatment depression severity using elastic net regularization. Performance and generalizability of this algorithm were determined in a hold-out sample ( = 1203). Treatment matching based on predictions in the hold-out sample resulted in inconsistent and small effects ( = 0.15), that were more pronounced for individuals matched to CBT ( = 0.22). We identified a small subgroup of individuals for which CBT did not appear more efficacious than CAU. Limitations are a poorly defined CAU condition, a low-severity sample, specific exclusion criteria and unavailability of certain baseline variables. Small matching effects are likely a realistic representation of the performance and generalizability of multivariable prediction algorithms based on clinical measures. Results indicate that future work and new approaches are needed.

中文翻译：

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数据驱动的个性化医学方法在大样本中分配认知行为治疗：ENRICHED 研究的重新分析

尽管可以有效治疗常见的心理健康问题，但个体对治疗的反应很难预测。可以通过使用治疗结果的个体预测进行针对性干预来优化治疗效果。本研究的目的是利用来自一项关于常见心理健康问题的心理干预的最大随机对照试验的数据来开发一种预测算法。这是对“增强冠心病康复”研究的二次分析，该研究调查了认知行为疗法（CBT）和照常护理（CAU）对急性心肌梗塞后抑郁症和低感知社会支持的有效性。 2481 名参与者被随机分配到 CBT 和 CAU。使用基线社会人口统计学、抑郁症特征、共病症状以及压力和逆境测量来构建使用弹性网络正则化预测治疗后抑郁症严重程度的算法。该算法的性能和通用性是在保留样本 (= 1203) 中确定的。基于保留样本预测的治疗匹配会产生不一致且较小的影响 (= 0.15)，对于与 CBT 匹配的个体 (= 0.22) 而言，这种影响更为明显。我们确定了一小部分人，对于这些人来说，CBT 似乎并不比 CAU 更有效。局限性是 CAU 状况定义不明确、样本严重程度低、特定的排除标准以及某些基线变量不可用。小匹配效应可能是基于临床测量的多变量预测算法的性能和普遍性的现实表示。结果表明，未来的工作和新方法是必要的。