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Comparison and development of machine learning for thalidomideinduced peripheral neuropathy prediction of refractory Crohn’s disease in Chinese population

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World Journal of Gastroenterology 2023年第24期29卷 3855-3870页

作者： Jing Mao Kang Chao Fu-Lin Jiang Xiao-Ping Ye Ting Yang Pan Li Xia Zhu Pin-Jin Hu Bai-Jun Zhou Min Huang Xiang Gao Xue-Ding Wang Institute of Clinical Pharmacology School of Pharmaceutical SciencesSun Yat-sen UniversityGuangzhou 510006Guangdong ProvinceChina Guangdong Provincial Key Laboratory of New Drug Design and Evaluation Sun Yat-sen UniversityGuangzhou 510006Guangdong ProvinceChina Department of Gastroenterology The Sixth Affiliated HospitalSun Yat-sen UniversityGuangzhou 510006Guangdong ProvinceChina Department of Pharmacy Guangdong Women and Children HospitalGuangzhou 510000Guangdong ProvinceChina

BACKGROUND thalidomide is an effective treatment for refractory Crohn’s disease(CD).However,thalidomide-induced peripheral neuropathy(TiPN),which has a large individual variation,is a major cause of treatment *** is rarely predictable and recognized,especially in *** is necessary to develop a risk model to predict TiPN *** To develop and compare a predictive model of TiPN using machine learning based on comprehensive clinical and genetic *** A retrospective cohort of 164 CD patients from January 2016 to June 2022 was used to establish the *** National Cancer Institute Common Toxicity Criteria Sensory Scale(version 4.0)was used to assess *** 18 clinical features and 150 genetic variables,five predictive models were established and evaluated by the confusion matrix receiver operating characteristic curve(AUROC),area under the precision-recall curve(AUPRC),specificity,sensitivity(recall rate),precision,accuracy,and F1 *** The top-ranking five risk variables associated with TiPN were interleukin-12 rs1353248[P=0.0004,odds ratio(OR):8.983,95%confidence interval(CI):2.497-30.90],dose(mg/d,P=0.002),brainderived neurotrophic factor(BDNF)rs2030324(P=0.001,OR:3.164,95%CI:1.561-6.434),BDNF rs6265(P=0.001,OR:3.150,95%CI:1.546-6.073)and BDNF rs11030104(P=0.001,OR:3.091,95%CI:1.525-5.960).In the training set,gradient boosting decision tree(GBDT),extremely random trees(ET),random forest,logistic regression and extreme gradient boosting(XGBoost)obtained AUROC values>0.90 and AUPRC>*** these models,XGBoost and GBDT obtained the first two highest AUROC(0.90 and 1),AUPRC(0.98 and 1),accuracy(0.96 and 0.98),precision(0.90 and 0.95),F1 score(0.95 and 0.98),specificity(0.94 and 0.97),and sensitivity(1).In the validation set,XGBoost algorithm exhibited the best predictive performance with the highest specificity(0.857),accuracy(0.818),AUPRC(0.86)and AUROC(0.89).ET and GBDT obtained the highest sensitivity(1)and F1 score(0.8)

关键词： thalidomide-induced peripheral neuropathy Refractory Crohn’s disease Neurotoxicity prediction models Machine learning Gene polymorphisms

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