尽管已经报道了基于移植前信息的各种复发预测模型，但它们无法更新考虑移植后患者状态的预测概率。因此，这些模型不适合在移植后随访期间决定治疗调整和先发性干预。动态预测模型可以通过考虑随访期间获得的信息来更新预测概率。本研究旨在开发和评估急性髓系白血病（AML）和骨髓增生异常同种异体造血细胞移植（allo-HCT）后的动态复发预测模型使用外周血肾母细胞瘤 1 信使 RNA (WT1mRNA) 检测综合征 (MDS)。我们回顾性分析了在我们机构接受异基因 HCT 的 AML 或 MDS 患者。为了开发动态模型，我们采用了标志性超模型方法，使用年龄、精细疾病风险指数、调节强度和移植次数作为移植前协变量，并使用移植前和移植后外周血 WT1mRNA 水平作为时间依赖性协变量。最后，我们在时间依赖性受试者工作特征曲线下按面积比较了传统模型和动态模型的预测性能。本研究共纳入 238 例异基因 HCT 病例。与仅考虑移植前协变量或同时考虑移植前协变量和移植后 WT1mRNA 水平而不考虑其动力学的模型相比，考虑所有移植前 WT1mRNA 水平及其动力学的动态模型显示出优越的预测性能；它们的时间相关曲线下面积分别为 0.89、0.73 和 0.87。复发的预测概率从复发前约90天开始逐渐增加。此外，我们开发了一个网络应用程序，使我们的模型用户友好。该模型有助于在 allo-HCT 后的任何时间点进行实时、高度准确和个性化的复发预测。这将通过为医生提供客观的复发预测来帮助移植后随访期间的决策。版权所有 © 2024。由 Elsevier Inc. 出版。

Although various relapse prediction models based on pre-transplant information have been reported, they cannot update the predictive probability considering post-transplant patient status. Therefore, these models are not appropriate for deciding on treatment adjustment and preemptive intervention during post-transplant follow-up. A dynamic prediction model can update the predictive probability by considering the information obtained during follow-up.This study aimed to develop and assess a dynamic relapse prediction model after allogeneic hematopoietic cell transplantation (allo-HCT) for acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) using peripheral blood Wilms' tumor 1 messenger RNA (WT1mRNA).We retrospectively analyzed patients with AML or MDS who underwent allo-HCT at our institution. To develop dynamic models, we employed the landmarking supermodel approach, using age, refined disease risk index, conditioning intensity, and number of transplantations as pre-transplant covariates and both pre- and post-transplant peripheral blood WT1mRNA levels as time-dependent covariates. Finally, we compared the predictive performances of the conventional and dynamic models by area under the time-dependent receiver operating characteristic curves.A total of 238 allo-HCT cases were included in this study. The dynamic model that considered all pre-transplant WT1mRNA levels and their kinetics showed superior predictive performance compared to models that considered only pre-transplant covariates or factored in both pre-transplant covariates and post-transplant WT1mRNA levels without their kinetics; their time-dependent areas under the curve were 0.89, 0.73, and 0.87, respectively. The predictive probability of relapse increased gradually from approximately 90 days before relapse. Furthermore, we developed a web application to make our model user friendly.This model facilitates real-time, highly accurate, and personalized relapse prediction at any time point after allo-HCT. This will aid decision-making during post-transplant follow-up by offering objective relapse forecasts for physicians.Copyright © 2024. Published by Elsevier Inc.