基于EHR的模拟试验在重定位心力衰竭治疗中提升有效性预测
Nansu Zong1, Shaika Chowdhury1, Shibo Zhou1
1Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA.
medRxiv : the preprint server for health sciences
|July 3, 2023
概括
这项研究开发了一个模拟试验 (ET) 框架,使用电子健康记录 (EHR) 来预测心力衰竭 (HF) 药物疗效. 该框架准确地识别了重新使用的HF药物,改善了治疗策略的预测.
科学领域:
- 生物医学信息学 生物医学信息学
- 药理学 药理学是指药理学的学科.
- 临床试验 临床试验
背景情况:
- 高心力衰竭 (HF) 死亡率需要新的治疗策略,推动人们对药物重用感兴趣.
- 电子健康记录 (EHR) 为模拟试验 (ET) 提供现实数据,以评估治疗疗效.
- 在EHR数据质量和完整性方面存在挑战,以准确的临床试验模拟.
研究的目的:
- 在随机对照试验 (RCT) 中预测重定向心力衰竭 (HF) 药物的疗效,使用模拟试验 (ET) 框架内的EHR数据.
- 开发和验证一个新的框架,整合目标预测和EHR分析,以提高药物疗效预测.
主要方法:
- 提出了一个模拟试验 (ET) 框架,将生物医学数据库的目标预测与电子健康记录数据的统计分析相结合.
- 开发了一个新的目标预测模型,使用药物分子,蛋白质序列和知识图协会的低维表示.
- 纳入HF药物和生物因素之间的相互作用,包括HF预后标志物,以改进预测.
主要成果:
- 与BETA基准相比,药物标预测模型获得了优异的性能 (AUCROC 97.7%,PRAUC 97.4%).
- 该ET框架准确地预测了来自266个第三阶段RCT的17种重新使用的HF药物的疗效,这些RCT使用了来自59,000名患者的数据.
- 与基线模型相比,ET框架显著提高了预测准确性,AUC-ROC从75.71%增加到93.57%,PRAUC从78.66%增加到90.34%.
结论:
- 开发的ET框架通过将EHR分析与目标预测相结合,有效地提高药物疗效模拟.
- 在预测重定向HF药物的有效性方面取得了显著的成功,用于第3期RCT.
- 该框架有可能通过改进药物重定向评估来推进心力衰竭治疗策略.
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