Fan Shuang, Hong Hao-Yang, Dong Xin-Yu, Xu Lan-Ping, Zhang Xiao-Hui, Wang Yu, Yan Chen-Hua, Chen Huan, Chen Yu-Hong, Han Wei, Wang Feng-Rong, Wang Jing-Zhi, Liu Kai-Yan, Shen Meng-Zhu, Huang Xiao-Jun, Hong Shen-Da, Mo Xiao-Dong
Peking University People's Hospital, Peking University Institute of Hematology, National Clinical Research Center for Hematologic Disease, Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China.
The Chinese University of Hong Kong, Shenzhen, Shenzhen, China.
Blood Sci. 2022 Dec 7;5(1):51-59. doi: 10.1097/BS9.0000000000000143. eCollection 2023 Jan.
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) prophylaxis. We 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 cohort. The equation was as follows: Probability (EBV reactivation) = , 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) - 2.4510. The threshold of probability was 0.4623, which separated patients into low- and high-risk groups. The 1-year cumulative incidence of EBV reactivation in the low- and high-risk groups was 11.0% versus 24.5% ( < .001), 10.7% versus 19.3% ( = .046), and 11.4% versus 31.6% ( = .001), respectively, in total, training and validation cohorts. The model could also predict relapse and survival after HID HSCT. We established a comprehensive model that could predict EBV reactivation in HID HSCT recipients using ATG for GVHD prophylaxis.
在使用单倍体相合相关供者（HID）进行造血干细胞移植（HSCT）后，爱泼斯坦-巴尔病毒（EBV）激活是最重要的感染之一。我们旨在建立一个机器学习综合模型，该模型能够预测在使用抗胸腺细胞球蛋白（ATG）预防移植物抗宿主病（GVHD）的HID HSCT后EBV的激活情况。我们纳入了470例连续的急性白血病患者，其中60%（n = 282）被随机选为训练队列，其余40%（n = 188）作为验证队列。公式如下：EBV激活概率 = ，其中Y = 0.0250×（年龄） - 0.3614×（性别） + 0.0668×（基础疾病） - 0.6297×（HSCT前疾病状态） - 0.0726×（疾病风险指数） - 0.0118×（造血细胞移植特异性合并症指数[HCT-CI]评分） + 1.2037×（人类白细胞抗原差异） + 0.5347×（EBV血清学状态） + 0.1605×（预处理方案） - 0.2270×（供者/受者性别匹配） + 0.2304×（供者/受者关系） - 0.0170×（移植物中单核细胞计数） + 0.0395×（移植物中CD34+细胞计数） - 2.4510。概率阈值为0.4623，据此将患者分为低风险和高风险组。在总队列、训练队列和验证队列中，低风险组和高风险组1年EBV激活的累积发生率分别为11.0%对24.5%（<0.001）、10.7%对19.3%（=0.046）和11.4%对31.6%（=0.001）。该模型还能够预测HID HSCT后的复发和生存情况。我们建立了一个综合模型，该模型能够预测使用ATG预防GVHD的HID HSCT受者中EBV的激活情况。
