Hernández-Boluda Juan Carlos, Mosquera-Orgueira Adrián, Gras Luuk, Koster Linda, Tuffnell Joe, Kröger Nicolaus, Gambella Massimiliano, Schroeder Thomas, Robin Marie, Sockel Katja, Passweg Jakob, Blau Igor Wolfgang, Yakoub-Agha Ibrahim, Van Dijck Ruben, Stelljes Mattias, Sengeloev Henrik, Vydra Jan, Platzbecker Uwe, de Witte Moniek, Baron Frédéric, Carlson Kristina, Rojas Javier, Pérez Míguez Carlos, Crucitti Davide, Raj Kavita, Drozd-Sokolowska Joanna, Battipaglia Giorgia, Polverelli Nicola, Czerw Tomasz, McLornan Donal P
Hematology Department, Hospital Clínico Universitario, Instituto de Investigación Sanitaria del Hospital Clínico de Valencia, University of Valencia, Valencia, Spain.
Hematology Department. University Hospital of Santiago de Compostela, Instituto de Investigación Sanitaria de Santiago de Compostela, Santiago de Compostela, Spain.
Blood. 2025 Jun 26;145(26):3139-3152. doi: 10.1182/blood.2024027287.
With the incorporation of effective therapies for myelofibrosis (MF), accurately predicting outcomes after allogeneic hematopoietic cell transplantation (allo-HCT) is crucial for determining the optimal timing for this procedure. Using data from 5183 patients with MF who underwent first allo-HCT between 2005 and 2020 at European Society for Blood and Marrow Transplantation centers, we examined different machine learning (ML) models to predict overall survival after transplant. The cohort was divided into a training set (75%) and a test set (25%) for model validation. A random survival forests (RSF) model was developed based on 10 variables: patient age, comorbidity index, performance status, blood blasts, hemoglobin, leukocytes, platelets, donor type, conditioning intensity, and graft-versus-host disease prophylaxis. Its performance was compared with a 4-level Cox regression-based score and other ML-based models derived from the same data set, and with the Center for International Blood and Marrow Transplant Research score. The RSF outperformed all comparators, achieving better concordance indices across both primary and postessential thrombocythemia/polycythemia vera MF subgroups. The robustness and generalizability of the RSF model was confirmed by Akaike information criterion and time-dependent receiver operating characteristic area under the curve metrics in both sets. Although all models were prognostic for nonrelapse mortality, the RSF provided better curve separation, effectively identifying a high-risk group comprising 25% of patients. In conclusion, ML enhances risk stratification in patients with MF undergoing allo-HCT, paving the way for personalized medicine. A web application (https://gemfin.click/ebmt) based on the RSF model offers a practical tool to identify patients at high risk for poor transplantation outcomes, supporting informed treatment decisions and advancing individualized care.
随着骨髓纤维化（MF）有效治疗方法的纳入，准确预测异基因造血细胞移植（allo-HCT）后的结果对于确定该手术的最佳时机至关重要。利用2005年至2020年期间在欧洲血液和骨髓移植中心接受首次allo-HCT的5183例MF患者的数据，我们研究了不同的机器学习（ML）模型来预测移植后的总生存期。该队列被分为训练集（75%）和测试集（25%）用于模型验证。基于10个变量开发了随机生存森林（RSF）模型：患者年龄、合并症指数、体能状态、血原始细胞、血红蛋白、白细胞、血小板、供体类型、预处理强度和移植物抗宿主病预防。将其性能与基于四级Cox回归的评分以及从同一数据集衍生的其他基于ML的模型进行比较，并与国际血液和骨髓移植研究中心评分进行比较。RSF的表现优于所有比较对象，在原发性和原发性血小板增多症/真性红细胞增多症MF亚组中均获得了更好的一致性指数。RSF模型的稳健性和可推广性通过赤池信息准则以及两组中曲线下时间依赖性受试者操作特征面积指标得到证实。尽管所有模型对非复发死亡率都具有预后价值，但RSF提供了更好的曲线分离，有效识别出占患者25%的高危组。总之，ML增强了接受allo-HCT的MF患者的风险分层，为个性化医疗铺平了道路。基于RSF模型的网络应用程序（https://gemfin.click/ebmt）提供了一个实用工具，用于识别移植结果较差的高危患者，支持明智的治疗决策并推进个体化护理。
