Journal
JOURNAL OF PEDIATRIC GASTROENTEROLOGY AND NUTRITION
Volume 69, Issue 4, Pages E105-E110Publisher
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/MPG.0000000000002436
Keywords
6-mercaptopurine; 6-thioguanine; azathioprine; Crohn disease
Funding
- Grant Agency of Charles University in Prague [136215, 364617, 246216]
- Ministry of Health, Czech Republic [00064203, 00098892]
- OP VVV ENOCH Project [CZ.02.1.01/0.0/0.0/16_019/0000868]
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Objectives: Therapeutic drug monitoring of thiopurine erythrocyte levels is not available in all centers and it usually requires quite a long time to obtain the results. The aims of this study were to build a model predicting low levels of 6-thioguanine and 6-methylmercaptopurine in pediatric inflammatory bowel disease (IBD) patients and to build a model to predict nonadherence in patients treated with azathioprine (AZA). Methods: The study consisted of 332 observations in 88 pediatric IBD patients. Low AZA dosing was defined as 6-thioguanine levels <125 pmol/8 x 10(8) erythrocytes and 6-methylmercaptopurine levels <5700 pmol/8 x 10(8) erythrocytes. Nonadherence was defined as undetectable levels of 6-thioguanine and 6-methylmercaptopurine <240 pmol/8 x 10(8) erythrocytes. Data were divided into training and testing part. To construct the model predicting low 6-thioguanine levels, nonadherence, and the level of 6-thioguanine, the modification of random forest method with cross-validation and resampling was used. Results: The final models predicting low 6-thioguanine levels and nonadherence had area under the curve, 0.87 and 0.94; sensitivity, 0.81 and 0.82; specificity, 0.80 and 86; and distance, 0.31 and 0.21, respectively, when applied on the testing part of the dataset. When the final model for prediction of 6-thioguanine values was applied on testing dataset, a root-mean-square error of 110 was obtained. Conclusions: Using easily obtained laboratory parameters, we constructed a model with sufficient accuracy to predict patients with low 6-thioguanine levels and a model for prediction of AZA treatment nonadherence (web applications: https://hradskyo.shinyapps.io/6TG_prediction/ and https://hradskyo.shinyapps.io/Non_adherence/).
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