The Personalized Advantage Index: translating research on prediction into individualized treatment recommendations. A demonstration.
The Personalized Advantage Index: translating research on prediction into individualized treatment recommendations. A demonstration.
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DOI：
10.1371/journal.pone.0083875
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Lorenzo-Luaces L
中科院分区：
文献类型：
--
作者：
DeRubeis RJ;Cohen ZD;Forand NR;Fournier JC;Gelfand LA;Lorenzo-Luaces L
个性化医疗的进步需要识别预测对治疗的差异反应的变量，以及开发和改进将预测信息转化为可操作建议的方法。使用随机治疗比较的数据，说明并测试一种整合预测信息以帮助治疗选择的新方法。使用抗抑郁药物（N = 104）与认知行为疗法（N = 50）治疗重度抑郁症的试验数据，预测154名患者两种治疗中每一种治疗的汉密尔顿抑郁评定量表（HRSD）的治疗后评分。 在产生这些预测的模型中没有使用患者自己的数据。预测差异反应的五个预随机化变量（婚姻状况、就业状况、生活事件、共病人格障碍和既往药物试验）被纳入回归模型，允许以HRSD单位计算每个患者的个性化优势指数（派）。对于60%的样本，预测其中一种治疗相对于另一种治疗具有临床意义的优势（派≥3）。当这些患者被随机分为“最佳”治疗组和“非最佳”治疗组时，前一组的结局上级（d = 0.58，95%CI 0.17 -1.01）。 在两种同等有效治疗的背景下实施的这种治疗选择方法产生的效应，如果前瞻性获得，将与活性治疗与对照治疗比较中常规观察到的效应相媲美。
Advances in personalized medicine require the identification of variables that predict differential response to treatments as well as the development and refinement of methods to transform predictive information into actionable recommendations. To illustrate and test a new method for integrating predictive information to aid in treatment selection, using data from a randomized treatment comparison. Data from a trial of antidepressant medications (N = 104) versus cognitive behavioral therapy (N = 50) for Major Depressive Disorder were used to produce predictions of post-treatment scores on the Hamilton Rating Scale for Depression (HRSD) in each of the two treatments for each of the 154 patients. The patient's own data were not used in the models that yielded these predictions. Five pre-randomization variables that predicted differential response (marital status, employment status, life events, comorbid personality disorder, and prior medication trials) were included in regression models, permitting the calculation of each patient's Personalized Advantage Index (PAI), in HRSD units. For 60% of the sample a clinically meaningful advantage (PAI≥3) was predicted for one of the treatments, relative to the other. When these patients were divided into those randomly assigned to their “Optimal” treatment versus those assigned to their “Non-optimal” treatment, outcomes in the former group were superior (d = 0.58, 95% CI .17—1.01). This approach to treatment selection, implemented in the context of two equally effective treatments, yielded effects that, if obtained prospectively, would rival those routinely observed in comparisons of active versus control treatments.
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影响因子：
15.8
作者：
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