预测多发性骨髓瘤中基于化疗的干细胞调动风险分层的不良事件
F Schwarz1,2,3, L Levien4, M Maulhardt4
1Department for Hematology and Medical Oncology, University Medical Center Göttingen, Göttingen, Germany. friedrich.schwarz@uni-goettingen.de.
NPJ digital medicine
|February 3, 2026
概括
针对多发性骨髓瘤的门诊干细胞动员 (SCM) 是安全的和可行的. 机器学习模型可以预测不良事件,使医院病床使用率减少三分之一.
科学领域:
- 血液学 血液学 血液学
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
背景情况:
- 自主干细胞移植对于多发性骨髓瘤治疗至关重要.
- 德国目前的住院干细胞动员 (SCM) 紧张了医疗保健资源.
- 门诊SCM提供了一种潜在的解决方案来缓解这些限制.
研究的目的:
- 评估多发性骨髓瘤患者门诊SCM的安全性和可行性.
- 开发机器学习模型来预测不良事件 (AE) 和它们的发病时间.
- 模拟风险分层门诊协议对医疗保健资源利用的影响.
主要方法:
- 对109名多发性骨髓瘤患者进行SCM和收集的分析.
- 训练机器学习模型 (分类和回归) 来预测AE及其发病.
- 模拟门诊协议,以评估床位使用率的减少.
主要成果:
- 在97%的患者中,成功采集干细胞.
- 69%的患者经历了严重的副作用,需要住院治疗.
- 机器学习模型在预测一些AE方面表现出很高的准确性 (例如,肌素升高,ROC-AUC1.0),但在中性衰竭发烧 (ROC-AUC0.67) 方面面临挑战.
- 回归模型预测AE发作的平均误差略高于一天.
- 模拟表明,在不影响安全的情况下,通过风险分层的门诊协议,可能减少三分之一的床位使用量.
结论:
- 门诊性SCM是多发性骨髓瘤的住院性SCM的安全和可行的替代方案.
- 机器学习可以有效地预测某些AE并预测它们的发病,有助于病房管理.
- 数据驱动,风险分层的门诊方法可以优化资源配置,减少医疗保健负担.
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