Treatment Selection in Depression
Treatment Selection in Depression
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DOI：
10.1146/annurev-clinpsy-050817-084746
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发表时间：
2018-01-01
期刊：
影响因子：
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通讯作者：
DeRubeis, Robert J.
中科院分区：
文献类型：
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作者：
Cohen, Zachary D.;DeRubeis, Robert J.
长期以来，心理健康研究人员和临床医生一直在寻找“什么对谁有效？“精准医学的目标是为这个问题提供基于证据的答案。抑郁症的治疗选择旨在帮助每个人接受治疗，在可用的选择中，最有可能为他们带来积极的结果。虽然已经确定了预测治疗反应的患者变量，但这些知识尚未转化为现实世界的治疗建议。个性化优势指数（派）和相关方法将治疗开始前获得的联合收割机信息结合到多变量预测模型中，该模型可以生成个性化预测，以帮助临床医生和患者选择正确的治疗。随着先进的统计建模方法以及新的预测变量和大数据的日益可用，治疗选择模型有望有助于改善抑郁症的预后。
Mental health researchers and clinicians have long sought answers to the question "What works for whom?" The goal of precision medicine is to provide evidence-based answers to this question. Treatment selection in depression aims to help each individual receive the treatment, among the available options, that is most likely to lead to a positive outcome for them. Although patient variables that are predictive of response to treatment have been identified, this knowledge has not yet translated into real-world treatment recommendations. The Personalized Advantage Index (PAI) and related approaches combine information obtained prior to the initiation of treatment into multivariable prediction models that can generate individualized predictions to help clinicians and patients select the right treatment. With increasing availability of advanced statistical modeling approaches, as well as novel predictive variables and big data, treatment selection models promise to contribute to improved outcomes in depression.