自动匹配算法用于识别符合中风试验资格的参与者:一项概念验证研究
Pattarawut Charatpangoon1, Nishita Singh2, Brian H Buck3
1Departments of Biomedical Engineering, the Hotchkiss Brain Institute, University of Calgary, Calgary, Canada.
概括
自动化临床试验查算法显著改善了患者的识别,并减少了查时间. 这项技术提高了对中风等疾病的试验效率和包容性.
科学领域:
- 临床信息学是一种临床信息学.
- 医学成像分析分析 医学成像分析
- 医疗服务研究 医疗服务研究
背景情况:
- 临床试验招募面临重大挑战,只有10%的符合条件的患者被招募.
- 前线临床医生手动对患者进行查是耗时的,容易忽视符合条件的个人,特别是在诸如中风等时间敏感的疾病中.
- 低效的选过程阻碍了试验的进展,并限制了研究结果的概括性.
研究的目的:
- 开发和评估一个自动匹配算法,以进行高效和包容的临床试验参与者查.
- 评估算法在多个试验中识别符合条件的患者的性能.
- 量化算法对选时间和资源利用的影响.
主要方法:
- 使用Act试验 (NCT03889249) 的成像和临床数据开发了一个匹配算法.
- 该算法采用基于规则的逻辑来将患者变量与试验包含/排除标准相匹配.
- 该算法用于识别六个特定试验 (EASI-TOC,CATIS-ICAD,CONVINCE,TEMPO-2,ESCAPE-MEVO,ENDOLOW) 的潜在候选人,其性能与手动审查和招生数据进行验证.
主要成果:
- 该算法识别出了更多的潜在合格候选人,而不是评估的试验中的实际招生人数.
- 自动查显示超过90%的灵敏度和特异性.
- 与传统的手工方法相比,查时间缩短了100倍以上.
结论:
- 自动匹配算法为快速识别符合临床试验条件的患者提供了强大的解决方案.
- 这项技术可以显著减少患者招生所需的资源,提高试验效率.
- 开发的算法可以适应各种试验和医疗条件的应用.
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