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J Crohns Colitis. 2017 Jul 1;11(7):801-810. doi: 10.1093/ecco-jcc/jjx014.

Machine Learning Algorithms for Objective Remission and Clinical Outcomes with Thiopurines.

Author information

1
Department of Veterans Affairs Center for Clinical Management Research, Ann Arbor, MI, USA.
2
Division of Gastroenterology, University of Michigan, Ann Arbor, MI, USA.
3
Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
4
Department of Statistics, University of Michigan, Ann Arbor, MI, USA.
5
Department of Pathology, University of Michigan, Ann Arbor, MI, USA.

Abstract

Background and Aims:

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.

Methods:

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.

Results:

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.

Conclusions:

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.

KEYWORDS:

Inflammatory bowel disease; immunosuppression; inflammation; thiopurines

PMID:
28333183
PMCID:
PMC5881698
DOI:
10.1093/ecco-jcc/jjx014
[Indexed for MEDLINE]
Free PMC Article

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