随机临床试验参与者的计算现象映射,使其能够评估其现实世界的代表性和个性化的推理
Phyllis M Thangaraj1, Evangelos K Oikonomou1, Lovedeep S Dhingra1
1Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT (P.M.T., E.K.O., L.S.D., A.A., R.J., R.K.).
Circulation. Cardiovascular quality and outcomes
|April 22, 2025
概括
一个新的指标评估了随机临床试验 (RCT) 参与者在电子健康记录 (EHR) 中代表真实世界患者的程度. 这有助于更准确地预测不同患者群体的治疗效果.
科学领域:
- 临床试验 临床试验
- 现实世界的证据.
- 医疗信息学 医疗信息学
背景情况:
- 随机临床试验 (RCT) 对现实患者的概括性是一个重大挑战.
- 电子健康记录 (EHR) 提供了大量现实世界患者数据的来源.
- 弥合RCT结果和临床实践之间的差距需要强大的方法来评估队列代表性.
研究的目的:
- 开发和验证一个多维度指标来量化RCT队列在EHR群体中的代表性.
- 通过利用来自RCT的个性化治疗效应来估计现实世界的治疗效应.
- 评估TOPCAT试验对心力衰竭患者的概括性,在EHR数据中保留喷射分数.
主要方法:
- 在TOPCAT试验和EHR数据中确定了65个心力衰竭的预随机化特征,其中包括保存的喷射分数患者.
- 开发了一个表型距离度量来量化RCT参与者在EHR队列中的代表性.
- 应用机器学习来学习个性化治疗效果,并根据区域RCT数据预测EHR人群中螺旋的益处.
主要成果:
- 电子心脏病患者 (N=8121) 总体上比TOPCAT-US参与者 (N=3445) 更相似.
- 与TOPCAT-东欧参与者相比,表型距离指标表明TOPCAT-US参与者对EHR患者的概括性更高.
- 一个TOPCAT-US衍生模型预测了所有EHR患者的螺旋乳益处,而一个TOPCAT-EE衍生模型预测的益处仅为13%.
结论:
- 一个新的多维指标有效地评估了与EHR数据对比的RCT参与者的现实世界的代表性.
- 这一指标可以更好地评估RCT对不同,现实世界患者群体的影响.
- 这些发现突出了区域差异的概括性及其对预测治疗疗效的影响.
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