A New Prediction Model for Evaluating Treatment-Resistant Depression
A New Prediction Model for Evaluating Treatment-Resistant Depression
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DOI：
10.4088/jcp.15m10381
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发表时间：
2017-02-01
影响因子：
5.3
通讯作者：
Kasper, Siegfried
中科院分区：
文献类型：
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作者：
Kautzky, Alexander;Baldinger-Melich, Pia;Kasper, Siegfried
目的：尽管抗抑郁药物种类繁多，但约三分之一的重度抑郁障碍(MDD)患者对适当的治疗没有足够的反应。利用抵抗抑郁和机器学习研究小组的数据库，我们打算得出新的见解，包括48个临床、社会人口学和心理社会预测因素对治疗结果的预测。方法：患者从2000年1月开始登记，并根据DSM-IV进行诊断。在至少2次适当剂量和长度的抗抑郁药物试验后，通过17项汉密尔顿抑郁评定量表(HDRS)评分=17来定义难治性抑郁(TRD)。缓解率由HDRS评分8来定义。使用随机森林进行逐步预测因子缩减，以找到用于分类治疗结果的最佳数目。在生成重要值后，在400名患者的训练样本中进行缓解和耐药预测。结果：对于治疗结果，最有用的预测因素是首次和最后一次抑郁发作之间的时间间隔、首次抗抑郁治疗的年龄、首次抗抑郁治疗的反应、严重程度、自杀倾向、忧郁症、终生抑郁发作次数、患者入院类型、教育程度、职业、合并糖尿病、恐慌和甲状腺疾病。虽然单一预测指标无法达到与随机猜测有很大不同的预测精度，但通过组合所有预测指标，我们可以检测到0.737的抵抗和0.850的缓解。结论：使用机器学习算法，预测完全缓解和完全缓解的成功率分别为0.737和0.850，超过了临床医生的预测能力。我们的结果加强了数据挖掘，并表明了关注基于交互的统计数据的好处。考虑到所有预测因子都可以在临床环境中轻松获得，我们希望我们的模型可以被其他研究小组测试。
Objective: Despite a broad arsenal of antidepressants, about a third of patients suffering from major depressive disorder (MDD) do not respond sufficiently to adequate treatment. Using the data pool of the Group for the Study of Resistant Depression and machine learning, we intended to draw new insights featuring 48 clinical, sociodemographic, and psychosocial predictors for treatment outcome.Method: Patients were enrolled starting from January 2000 and diagnosed according to DSM-IV. Treatment-resistant depression (TRD) was defined by a 17-item Hamilton Depression Rating Scale (HDRS) score >= 17 after at least 2 antidepressant trials of adequate dosage and length. Remission was defined by an HDRS score < 8. Stepwise predictor reduction using randomForest was performed to find the optimal number for classification of treatment outcome. After importance values were generated, prediction for remission and resistance was performed in a training sample of 400 patients. For prediction, we used a set of 80 patients not featured in the training sample and computed receiver operating characteristics.Results: The most useful predictors for treatment outcome were the timespan between first and last depressive episode, age at first antidepressant treatment, response to first antidepressant treatment, severity, suicidality, melancholia, number of lifetime depressive episodes, patients' admittance type, education, occupation, and comorbid diabetes, panic, and thyroid disorder. While single predictors could not reach a prediction accuracy much different from random guessing, by combining all predictors, we could detect resistance with an accuracy of 0.737 and remission with an accuracy of 0.850. Consequently, 65.5% of predictions for TRD and 77.7% for remission can be expected to be accurate.Conclusions: Using machine learning algorithms, we could demonstrate success rates of 0.737 for predicting TRD and 0.850 for predicting remission, surpassing predictive capabilities of clinicians. Our results strengthen data mining and suggest the benefit of focus on interaction-based statistics. Considering that all predictors can easily be obtained in a clinical setting, we hope that our model can be tested by other research groups.