基于基于模拟试验的电子健康记录的提前疗效预测,用于重新定位心力衰竭疗法
Nansu Zong1, Shaika Chowdhury2, Shibo Zhou2
1Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA. Zong.Nansu@mayo.edu.
NPJ digital medicine
|May 24, 2025
概括
这项研究引入了一个新的框架来预测重新定位的药物是否对心力衰竭 (HF) 有效. 通过将药物向预测与电子健康记录 (EHR) 仿真试验相结合,它可以改善治疗疗效预测.
科学领域:
- 药理学 药理学是指药理学的学科.
- 生物医学信息学 生物医学信息学
- 临床试验 临床试验
背景情况:
- 电子健康记录 (EHR) 数据在将现实世界的证据与随机对照试验 (RCT) 协调方面存在挑战.
- 准确预测心力衰竭 (HF) 等疾病中重新定位药物的治疗疗效仍然是一个重大障碍.
- 现有的方法很难弥合观察数据和临床试验结果之间的差距.
研究的目的:
- 开发和验证一个新的框架,用于预测HF临床试验中重用药物的疗效方向.
- 通过将药物向相互作用与基于EHR的仿真试验相结合,提高治疗效益的预测能力.
- 建立一种可靠的方法,使用HF预后标记来推导代用终点,以提高预测准确度.
主要方法:
- 提出了一个框架,将先进的药物标预测模型与基于EHR的仿真试验 (ET) 整合在一起.
- 利用HF预后标记来获得替代终点以预测疗效.
- 将药物标预测模型与BETA基准验证,并将其性能与现有算法进行比较.
- 评估了框架的预测能力,使用来自梅奥诊所的59000名患者的大数据集.
主要成果:
- 新型药物标预测模型在BETA基准上的基线算法相比显示出更高的性能.
- 疗效方向预测框架成功识别了17种用于心力衰竭的重定向药物.
- 该框架在识别潜在有效的HF治疗方法方面取得了显著的预测准确性.
结论:
- 综合框架提供了一种强大的方法来预测心力衰竭中重用药物的药物疗效.
- 这种方法提高了在临床试验的背景下从EHR数据中解释现实世界的证据.
- 这些发现支持使用该框架来加快对心力衰竭有效治疗方法的识别.
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