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Abstract：
Background: Although acute kidney injury (AKI) is a common complication in patients undergoing hematopoietic stem cell transplantation (HSCT), its prophylaxis remains a clinical challenge. Attempts at prevention or early diagnosis focus on various methods for the identification of factors influencing the incidence of AKI. Our aim was to test the artificial intelligence (AI) potential in the construction of a model defining parameters predicting AKI development. Methods: The analysis covered the clinical data of children followed up for 6 months after HSCT. Kidney function was assessed before conditioning therapy, 24 h after HSCT, 1, 2, 3, 4, and 8 weeks after transplantation, and, finally, 3 and 6 months post-transplant. The type of donor, conditioning protocol, and complications were incorporated into the model. Results: A random forest classifier (RFC) labeled the 93 patients according to presence or absence of AKI. The RFC model revealed that the values of the estimated glomerular filtration rate (eGFR) before and just after HSCT, as well as methotrexate use, acute graft versus host disease (GvHD), and viral infection occurrence, were the major determinants of AKI incidence within the 6-month post-transplant observation period. Conclusions: Artificial intelligence seems a promising tool in predicting the potential risk of developing AKI, even before HSCT or just after the procedure.
摘要：
背景:尽管急性肾损伤(AKI)是造血干细胞移植(HSCT)患者常见的并发症,它的预防仍然是一个临床挑战。预防或早期诊断的尝试集中在确定影响AKI发生率的因素的各种方法上。我们的目的是在构建定义预测AKI发展的参数的模型中测试人工智能（AI）的潜力。方法:分析HSCT术后6个月随访患儿的临床资料。在预处理治疗前评估肾功能，HSCT后24小时，移植后1、2、3、4和8周，and,最后,移植后3个月和6个月。捐赠者的类型,调节协议，并将并发症纳入模型。结果:根据AKI的存在或不存在,随机森林分类器(RFC)标记93名患者。RFC模型显示，HSCT前后的肾小球滤过率（eGFR）估计值，以及甲氨蝶呤的使用，急性移植物抗宿主病（GvHD），和病毒感染的发生，是移植后6个月观察期内AKI发生率的主要决定因素。结论：人工智能似乎是预测AKI潜在风险的有前途的工具，甚至在HSCT之前或手术之后。
