Cross-trial prediction of treatment outcome in depression: a machine learning approach
Cross-trial prediction of treatment outcome in depression: a machine learning approach
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DOI：
10.1016/s2215-0366(15)00471-x
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发表时间：
2016-03-01
影响因子：
64.3
通讯作者：
Corlett, Philip Robert
中科院分区：
文献类型：
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作者：
Chekroud, Adam Mourad;Zotti, Ryan Joseph;Corlett, Philip Robert
背景抗抑郁药物的治疗效果很低，但通过将患者与干预措施相匹配可能会有所改善。目前，临床医生还没有经过经验验证的机制来评估抑郁症患者是否会对特定的抗抑郁药物产生反应。我们的目标是开发一种算法来评估患者在12周的西酞普兰疗程后是否会获得症状缓解。方法我们使用患者报告的来自抑郁症患者(n=4041,1949名患者)的数据，这些数据来自缓解抑郁的排序替代疗法(STAR*D；ClinicalTrials.gov，编号NCT00021528)的第一级，以确定最能预测治疗结果的变量，并使用这些变量训练机器学习模型来预测临床缓解。我们在埃西妥普兰治疗组(n=151)的独立临床试验(联合药物改善抑郁结果[ComEd]；ClinicalTrials.gov，编号NCT00590863)中外部验证了该模型。结果我们从164个患者报告的变量中确定了25个最能预测治疗结果的变量，并使用这些变量来训练模型。该模型经过内部交叉验证，在STAR*D队列中预测结果的准确率显著高于概率(64.6%[SD 3.2]；p
Background Antidepressant treatment efficacy is low, but might be improved by matching patients to interventions. At present, clinicians have no empirically validated mechanisms to assess whether a patient with depression will respond to a specific antidepressant. We aimed to develop an algorithm to assess whether patients will achieve symptomatic remission from a 12-week course of citalopram.Methods We used patient-reported data from patients with depression (n=4041, with 1949 completers) from level 1 of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D; ClinicalTrials.gov, number NCT00021528) to identify variables that were most predictive of treatment outcome, and used these variables to train a machine-learning model to predict clinical remission. We externally validated the model in the escitalopram treatment group (n=151) of an independent clinical trial (Combining Medications to Enhance Depression Outcomes [COMED]; ClinicalTrials.gov, number NCT00590863).Findings We identified 25 variables that were most predictive of treatment outcome from 164 patient-reportable variables, and used these to train the model. The model was internally cross-validated, and predicted outcomes in the STAR*D cohort with accuracy significantly above chance (64.6% [SD 3.2]; p