使用现实数据和人工智能评估瘤试验的资格标准
Ruishan Liu1, Shemra Rizzo2, Samuel Whipple2
1Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
Nature
|April 8, 2021
概括
在癌症临床试验中扩大限制性资格标准可以使患者的资格增加一倍,并可能改善生存结果. 这种以数据为导向的方法提高了试验的包容性,同时确保了患者的安全.
科学领域:
- 癌症学
- 临床试验设计
- 医疗信息学
背景情况:
- 临床试验在患者包容性方面面临挑战,原因是限制性资格标准.
- 设计包容性癌症试验对于更广泛的患者代表性和可通用的结果至关重要.
研究的目的:
- 系统地评估资格标准对癌症试验群体和结果的影响.
- 评估常见实验室价值排除对试验危险率的影响.
- 探索以数据为导向的方法来扩大限制性标准以提高患者的资格.
主要方法:
- 利用试验路径的计算框架来模拟已完成的高级非小细胞肺癌试验.
- 分析了来自全国电子健康记录数据库的现实数据 (61,094名患者).
- 采用数据驱动的方法来扩大限制性标准并评估符合条件的患者群体和生存结果的变化.
主要成果:
- 许多常见的资格标准,包括实验室值的排除,对试验危险率的影响很小.
- 扩大限制性标准使得符合条件的患者人数平均增加了一倍以上.
- 扩大标准导致整体生存危险比率平均下降0.05.
结论:
- 限制性资格标准可能会排除可能受益于癌症治疗的患者.
- 基于数据的评估和扩大标准可以显著提高试验的包容性.
- 这种方法有助于在不影响患者安全的情况下设计更具包容性的临床试验.
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