Dataset Information

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

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.

SUBMITTER: Waljee AK

PROVIDER: S-EPMC5881698 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Publications

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

Waljee Akbar K AK Sauder Kay K Patel Anand A Segar Sandeep S Liu Boang B Zhang Yiwei Y Zhu Ji J Stidham Ryan W RW Balis Ulysses U Higgins Peter D R PDR

Journal of Crohn's & colitis 20170701 7

<h4>Background and aims</h4>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 v ...[more]

PMID: 28333183

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Dataset Information

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

Background and aims

Methods

Results

Conclusions

Publications

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

Similar Datasets

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