Oct 20, 2009

Algorithms outperform metabolite tests in predicting response of patients with inflammatory bowel disease to thiopurines

Clinical Gastroenterology and Hepatology : the Official Clinical Practice Journal of the American Gastroenterological Association
Akbar K WaljeePeter D R Higgins

Abstract

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. 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. 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 ...Continue Reading

  • References9
  • Citations27

References

  • References9
  • Citations27

Citations

Mentioned in this Paper

Metabolic Process, Cellular
Immune System
Laboratory Procedures
White Blood Cell Count Procedure
Immunology
6-thioguanylic acid
Cell Differentiation Process
Knowledge Representation (Computer)
6-methylthiopurine
Neutrophil Count (Procedure)

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