Lancet:交叉试验预测抑郁结局:机器学习法

2016-01-28 MedSci MedSci原创

Lancet:交叉试验预测抑郁结局:机械学习法

抗抑郁药物疗效很低,但通过对病人进行干预匹配可提高疗效。目前,临床医生没有经验性机制来评估是否抑郁症患者会对特定的抗抑郁药产生反应。我们旨在开发一个算法来评估患者在使用12周西酞普兰后能否达到症状缓解。

我们使用抑郁症患者自我报告的数据(n = 4041,1949名完成者),从1级测序替代治疗到抑郁缓解(STAR * D)来确定最有预测治疗结局价值的变量,并使用这些变量来训练一个机器学习模型,预测临床缓解。在独立的临床试验中 (联合药物改善抑郁结局[COMED]),我们从外部认证西酞普兰治疗组 (n=151) 。

我们从164名患者报告的变量中识别了25个最有预测治疗结果价值的变量,并使用这些变量来建立模型。这个模型从内部交叉验证,预测了STAR*D队列的结局,精确性显著高于机会预测 (64·6% [SD 3.2]; p<0·0001)。这个模型从外部验证了西酞普兰治疗组(N=151)COMED(准确性59.6%, p=0.043)。这个模型同样展现了COMED联合盐酸异地普伦治疗的结局(n=134; 准确性 59.7%, p=0.023),但是没有联合文拉法辛-米氮平组(n=140; 准确性 51.4%, p=0.53)。表明了特异性模型的潜在价值。

解释:

通过挖掘现有的临床试验数据建立统计模型使辨别患者对特定抗抑郁药物的反应成为可能。

原始出处:


Adam Mourad Chekroud,Ryan Joseph Zotti, et al,Cross-trial prediction of treatment outcome in depression: a machine learning approach.Lancet,20 January 2016


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    2016-02-06 howi
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    2016-01-29 李继凯

    赞一个

    0

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    2016-01-29 李继凯

    继续关注

    0

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    2016-01-29 medcardio

    新方法

    0

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