Department of Veterans Affairs Center for Clinical Management Research, Ann Arbor, MI, USA.
Division of Gastroenterology, University of Michigan, Ann Arbor, MI, USA.
J Crohns Colitis. 2017 Jul 1;11(7):801-810. doi: 10.1093/ecco-jcc/jjx014.
Big data analytics leverage patterns in data to harvest valuable information, but are rarely implemented in clinical care. Optimising thiopurine therapy for inflammatory bowel disease [IBD] has proved difficult. Current methods using 6-thioguanine nucleotide [6-TGN] metabolites have failed in randomized controlled trials [RCTs], and have not been used to predict objective remission [OR]. Our aims were to: 1) develop machine learning algorithms [MLA] using laboratory values and age to identify patients in objective remission on thiopurines; and 2) determine whether achieving algorithm-predicted objective remission resulted in fewer clinical events per year.
Objective remission was defined as the absence of objective evidence of intestinal inflammation. MLAs were developed to predict three outcomes: objective remission, non-adherence, and preferential shunting to 6-methylmercaptopurine [6-MMP]. The performance of the algorithms was evaluated using the area under the receiver operating characteristic curve [AuROC]. Clinical event rates of new steroid prescriptions, hospitalisations, and abdominal surgeries were measured.
Retrospective review was performed on medical records of 1080 IBD patients on thiopurines. The AuROC for algorithm-predicted remission in the validation set was 0.79 vs 0.49 for 6-TGN. The mean number of clinical events per year in patients with sustained algorithm-predicted remission [APR] was 1.08 vs 3.95 in those that did not have sustained APR [p < 1 x 10-5]. Reductions in the individual endpoints of steroid prescriptions/year [-1.63, p < 1 x 10-5], hospitalisations/year [-1.05, p < 1 x 10-5], and surgeries/year [-0.19, p = 0.065] were seen with algorithm-predicted remission.
A machine learning algorithm was able to identify IBD patients on thiopurines with algorithm-predicted objective remission, a state associated with significant clinical benefits, including decreased steroid prescriptions, hospitalisations, and surgeries.
大数据分析利用数据中的模式来获取有价值的信息，但在临床护理中很少实施。优化炎症性肠病（IBD）的硫唑嘌呤治疗一直很困难。目前使用 6-巯基嘌呤核苷酸（6-TGN）代谢物的方法在随机对照试验（RCT）中失败，并且尚未用于预测客观缓解（OR）。我们的目标是：1）使用实验室值和年龄开发机器学习算法（MLA）来识别硫唑嘌呤治疗中处于客观缓解的患者；2）确定是否达到算法预测的客观缓解会导致每年发生的临床事件更少。
客观缓解定义为不存在肠道炎症的客观证据。开发 MLA 来预测三个结果：客观缓解、不依从和优先分流至 6-甲基巯基嘌呤（6-MMP）。使用接受者操作特征曲线下的面积（AuROC）评估算法的性能。测量新类固醇处方、住院和腹部手术的临床事件发生率。
对 1080 名接受硫唑嘌呤治疗的 IBD 患者的病历进行了回顾性审查。验证集中算法预测缓解的 AuROC 为 0.79，而 6-TGN 为 0.49。在持续算法预测缓解（APR）的患者中，每年的临床事件平均数量为 1.08，而在没有持续 APR 的患者中为 3.95 [p < 1 x 10-5]。每年类固醇处方/年减少[-1.63，p < 1 x 10-5]、住院/年减少[-1.05，p < 1 x 10-5]和手术/年减少[-0.19，p = 0.065]，与算法预测缓解有关。
机器学习算法能够识别出硫唑嘌呤治疗的 IBD 患者，这些患者具有算法预测的客观缓解，这与显著的临床益处相关，包括减少类固醇处方、住院和手术。
