Machine learning algorithm as a prognostic tool for Epstein-Barr virus reactivation after haploidentical hematopoietic stem cell transplantation

作     者：ShuangFan Hao-Yang Hong Xin-Yu Dong Lan-Ping Xu Xiao-Hui Zhang Yu Wang Chen-Hua Yan Huan Chen Yu-Hong Chen Wei Han Feng-Rong Wang Jing-Zhi Wanga Kai-Yan Liu Meng-Zhu Shen Xiao-Jun Huang Shen-Da Hong Xiao-Dong Mo Shuang Fan;Hao-Yang Hong;Xin-Yu Dong;Lan-Ping Xu;Xiao-Hui Zhang;Yu Wang;Chen-Hua Yan;Huan Chen;Yu-Hong Chen;Wei Han;Feng-Rong Wang;Jing-Zhi Wang;Kai-Yan Liu;Meng-Zhu Shen;Xiao-Jun Huang;Shen-Da Hong;Xiao-Dong Mo

作者机构：Peking University People’s HospitalPeking University Institute of HematologyNational Clinical Research Center for Hematologic DiseaseBeijing Key Laboratory of Hematopoietic Stem Cell TransplantationBeijingChina The Chinese University of Hong KongShenzhenShenzhenChina National Institute of Health Data Science at Peking UniversityPeking University Health Science CenterBeijingChina Peking-Tsinghua Center for Life SciencesAcademy for Advanced Interdisciplinary StudiesPeking UniversityBeijingChina Research Unit of Key Technique for Diagnosis and Treatments of Hematologic MalignanciesChinese Academy of Medical SciencesBeijingChina 

出 版 物：《Blood Science》 (血液科学（英文）)

年 卷 期：2023年第5卷第1期

页      面：51-59页

学科分类：1002[医学-临床医学] 1010[医学-医学技术（可授医学、理学学位）] 100215[医学-康复医学与理疗学] 10[医学] 

基　　金：the Program of the National Natural Science Foundation of China(grant number 82170208) the Foundation for Innovative Research Groups of the National Natural Science Foundation of China(grant number 81621001) the CAMS Innovation Fund for Medical Sciences(CIFMS)(grant number 2019-I2M-5-034) the Key Program of the National Natural Science Foundation of China(grant number 81930004) the Fundamental Research Funds for the Central Universities,National Natural Science Foundation of China(No.62102008) 

主　　题：Anti-thymocyte globulin Epstein-Barr virus Haplo-identical hematopoietic stem cell transplant Machine learning Predictive model 

摘      要：Epstein-Barr virus(EBV)reactivation is one of the most important infections after hematopoietic stem cell transplantation(HSCT)using haplo-identical related donors(HID).We aimed to establish a comprehensive model with machine learning,which could predict EBV reactivation after HID HSCT with anti-thymocyte globulin(ATG)for graft-versus-host disease(GVHD)*** enrolled 470 consecutive acute leukemia patients,60%of them(n=282)randomly selected as a training cohort,the remaining 40%(n=188)as a validation *** equation was as follows:Probability(EBV reactivation)=1/1+exp(−Y),where Y=0.0250×(age)–0.3614×(gender)+0.0668×(underlying disease)–0.6297×(disease status before HSCT)–0.0726×(disease risk index)–0.0118×(hematopoietic cell transplantation-specific comorbidity index[HCT-CI]score)+1.2037×(human leukocyte antigen disparity)+0.5347×(EBV serostatus)+0.1605×(conditioning regimen)–0.2270×(donor/recipient gender matched)+0.2304×(donor/recipient relation)–0.0170×(mononuclear cell counts in graft)+0.0395×(CD34+cell count in graft)–*** threshold of probability was 0.4623,which separated patients into low-and high-risk *** 1-year cumulative incidence of EBV reactivation in the low-and high-risk groups was 11.0%versus 24.5%(P.001),10.7%versus 19.3%(P=.046),and 11.4%versus 31.6%(P=.001),respectively,in total,training and validation *** model could also predict relapse and survival after HID *** established a comprehensive model that could predict EBV reactivation in HID HSCT recipients using ATG for GVHD prophylaxis.