开发一个动态的反事实风险分层战略,用于新诊断的AML患者治疗venetoclax和azacitidine
Nazmul Islam1, Justin L Dale1, Jamie S Reuben1
1RefinedScience, Aurora, CO.
JCO clinical cancer informatics
|December 2, 2025
概括
这项研究开发了一种灵活的机器学习策略,用于针对venetoclax加azacitidine (ven/aza) 治疗的急性髓性白血病 (AML) 风险分层. 可适应的模型解决了现实世界的数据挑战,并改善了患者风险评估.
科学领域:
- 血液学 血液学 血液学
- 机器学习在医学中的应用
- 临床信息学 临床信息学
背景情况:
- 急性髓性白血病 (AML) 的治疗需要精确的风险分层.
- 威尼托克拉克斯加上阿扎西提丁 (ven/aza) 是一种关键的AML治疗方法.
- 现有的风险模型可能无法完全解决现实数据 (RWD) 的复杂性或治疗特异性.
研究的目的:
- 为AML制定一个灵活的风险分层策略,以适应venetoclax加azacitidine (ven/aza) 治疗.
- 创建一个战略,适应现实世界数据 (RWD) 的挑战,并适应各种用例.
- 使用机器学习 (ML) 来生成可调节的风险模型 (RMs).
主要方法:
- 使用动态反事实机器学习 (ML) 策略来生成可调节的风险模型 (RMs).
- 采用了来自AML诊断样本和测试模型的特征,用于316名新诊断患者的队列,他们接受了ven/aza治疗.
- 使用各种模型假设,数据元素,终点和外部现实世界队列 (RWC) 验证了RM性能.
主要成果:
- 使用基于ML的RM与遗传和/或表型数据识别了有利,中间和不利的风险组.
- 实现了公平的患者分布 (每组20%-40%) 和显著的风险层分离 (P <0.001).
- 在外部的RWC上显示出强大的可预测性 (AUC 0.60-0.70) 和与欧洲白血病网2022可比的性能.
结论:
- 拟议的ML战略有效地解决了RWD在AML风险分层方面的考虑.
- 该策略可以通过编码和参数更新进行调整,从而提高适应不同环境和用例的适应性.
- 这种新的方法为开发AML和潜在的其他疾病的风险模型提供了更有效的方法.
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