Algorithms Outperform Metabolite Tests in Predicting Response of Patients With Inflammatory Bowel Disease to Thiopurines
Algorithms Outperform Metabolite Tests in Predicting Response of Patients With Inflammatory Bowel Disease to Thiopurines
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DOI：
10.1016/j.cgh.2009.09.031
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发表时间：
2010-02-01
影响因子：
12.6
通讯作者：
Higgins, Peter D. R.
中科院分区：
文献类型：
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作者：
Waljee, Akbar K.;Joyce, Joel C.;Higgins, Peter D. R.
背景与目的：硫嘌呤代谢物6-硫代鸟嘌呤核苷酸（6-TGN）和6-甲基巯基嘌呤的水平通常在硫嘌呤治疗炎症性肠病期间进行监测，尽管该测试成本高且临床反应预测性差（敏感性，62%;特异性，72%）。我们研究了是否可以使用常见的实验室参数的模式来确定适当的免疫反应，以巯基嘌呤和他们是否更准确地比测量的巯基嘌呤代谢产物，以确定谁对治疗有反应的患者。方法：我们在24小时内使用代谢物和标准实验室检查确定了774例接受硫嘌呤治疗的炎症性肠病患者。机器学习算法是在70%的病例中使用实验室值和年龄开发的;这些算法在剩下的30%的病例中进行了测试。研究结果：基于实验室和年龄数据开发了一种随机森林算法;该算法在受试者工作特征（AUROC）曲线下面积为0.856的测试集中区分了临床应答者和无应答者。相比之下，6-TGN水平区分临床应答者和无应答者，AUROC为0.594（P < .001）。开发的用于识别硫嘌呤不依从（AUROC，0.813）和硫嘌呤分流（AUROC，0.797）的算法是准确的。结论：使用年龄和实验室值的算法可以区分临床反应，不遵守，以及服用硫嘌呤的患者之间的硫嘌呤代谢分流。这种方法在预测临床反应方面比6-TGN代谢物测量更便宜，更准确。如果得到验证，这种方法将提供一个低成本，快速替代代谢物测量监测硫嘌呤的使用。
BACKGROUND & AIMS: Levels of the thiopurine metabolites 6-thioguanine nucleotide (6-TGN) and 6-methylmercaptopurine commonly are monitored during thiopurine therapy for inflammatory bowel disease despite this test's high cost and poor prediction of clinical response (sensitivity, 62%; specificity, 72%). We investigated whether patterns in common laboratory parameters might be used to identify appropriate immunologic responses to thiopurine and whether they are more accurate than measurements of thiopurine metabolites in identifying patients who respond to therapy. METHODS: We identified 774 patients with inflammatory bowel disease on thiopurine therapy using metabolite and standard laboratory tests over a 24-hour time period. Machine learning algorithms were developed using laboratory values and age in a random training set of 70% of the cases; these algorithms were tested in the remaining 30% of the cases. RESULTS: A random forest algorithm was developed based on laboratory and age data; it differentiated clinical responders from nonresponders in the test set with an area under the receiver operating characteristic (AUROC) curve of 0.856. In contrast, 6-TGN levels differentiated clinical responders from nonresponders with an AUROC of 0.594 (P < .001). Algorithms developed to identify thiopurine nonadherence (AUROC, 0.813) and thiopurine shunters (AUROC, 0.797) were accurate. CONCLUSIONS: Algorithms that use age and laboratory values can differentiate clinical response, nonadherence, and shunting of thiopurine metabolism among patients who take thiopurines. This approach was less costly and more accurate than 6-TGN metabolite measurements in predicting clinical response. If validated, this approach would provide a low-cost, rapid alternative to metabolite measurements for monitoring thiopurine use.