A total of 757 de novo AML patients were eligible for training cohort, including 401 males and 356 females, with a median follow-up of 30 (range, 1–91) months after allo-HCT. The median age at allo-HCT was 40 (range, 14–69) years. According to the ELN2022 classification, 34% (n = 257) were favourable, 42% (n = 316) were intermediate, and 24% (n = 180) were adverse. Compared to the ELN2017 categories, 95% of favourable, 80% of intermediate, and 84% of adverse patients in the ELN2022 categories remained in their previous risk stratification. The redistribution of patients and major mutation types from the ELN2017 to ELN2022 is shown in Fig. 1B and Table S1, and the detailed baseline demographic is provided in Table S2.

According to the ELN2017, the 3-year cumulative incidence of relapse (CIR) for the favourable, intermediate, and adverse groups was 13%, 18 and 40%, respectively, with corresponding relapse-free survival (RFS) rates of 81%, 75 and 52%, and overall survival (OS) rates of 85%, 81%, and 59%, all showing statistically significant differences (P < 0.001) (Fig. 1C, E, G). In the ELN2022, the 3-year CIR were 11%, 19%, and 40% (P < 0.001); the 3-year RFS were 84%, 74%, and 52% (P < 0.001); and the 3-year OS were 88%, 79%, and 59% (P < 0.001) across the favourable, intermediate, and adverse groups, respectively (Fig. 1D, F, H). Similar results were observed when survival analysis was confined to patients who achieved complete remission (CR) at allo-HCT (Fig. S1). Furthermore, in multivariate model, the ELN2022 risk classification at diagnosis was an independent prognostic factor for CIR, RFS and OS (Table S3). Receiver operating characteristic (ROC) analysis was performed to compare the prognostic prediction power of the ELN2022 and ELN2017 risk systems in our training cohort. The C-statistics (area under the curves [AUC]) for predicting relapse (AUC_ELN2017 = 0.660 vs. AUC_ELN2022 = 0.668, P = 0.530), RFS (AUC_ELN2017 = 0.648 vs. AUC_ELN2022 = 0.658, P = 0.372), and OS (AUC_ELN2017 = 0.641 vs. AUC_ELN2022 = 0.653, P = 0.318) were not distinct between the ELN2022 and ELN2017 (Fig. 1I–K and Table S4).

共有 757 名新发 AML 患者符合训练队列的条件，包括 401 名男性和 356 名女性，allo-HCT 后中位随访时间为 30 个月 （范围，1-91）。同种异体 HCT 的中位年龄为 40 岁 （范围，14-69） 岁。根据 ELN2022 分类，34% （n = 257） 为好评，42% （n = 316） 为中等，24% （n = 180） 为不良反应。与 ELN2017 类别相比，ELN2022类别中 95% 的阳性患者、80% 的中级患者和 84% 的不良患者仍处于之前的风险分层中。患者和主要突变类型从 ELN2017 到 ELN2022 的重新分布如图 1B 和表 S1 所示，详细的基线人口统计数据见表 S2。

根据ELN2017，有利、中等和不良反应组的 3 年累积复发率 （CIR） 分别为 13%、18% 和 40%，相应的无复发生存率 （RFS） 分别为 81%、75 和 52%，总生存率 （OS） 分别为 85%、81% 和 59%，均显示出统计学上的显着差异 （P < 0.001） （图 1C， E， G）。ELN2022 年，3 年 CIR 分别为 11% 、 19% 和 40% （P < 0.001）;3 年 RFS 分别为 84% 、 74% 和 52% （P < 0.001）;有利、中等和不良组的 3 年 OS 分别为 88% 、 79% 和 59% （P < 0.001） （图 1D、F、H）。当生存分析仅限于在 allo-HCT 中达到完全缓解 （CR） 的患者时，观察到类似的结果（图 S1）。此外，在多变量模型中，诊断时的 ELN2022 风险分类是 CIR 、 RFS 和 OS 的独立预后因素 （表 S3）。进行受试者工作特征 （ROC） 分析，比较我们训练队列中 ELN2022 和 ELN2017 风险系统的预后预测能力。预测复发的 C 统计量（曲线下面积 [AUC]） （AUC_ELN2017 = 0.660 vs. AUC_ELN2022 = 0.668，P = 0.530）、RFS （AUC_ELN2017 = 0.648 vs. AUC_ELN2022 = 0.658，P = 0.372）和 OS （AUC_ELN2017 = 0.641 vs. AUC_ELN2022 = 0.653，P = 0.318）在 ELN2022 和 ELN2017 之间没有区别 （图 1I-K 和表 S4）。