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Evaluating eligibility criteria of oncology trials using real-world data and AI
Nature ( IF 42.778 ) Pub Date : 2021-04-07 , DOI: 10.1038/s41586-021-03430-5
Ruishan Liu, Shemra Rizzo, Samuel Whipple, Navdeep Pal, Arturo Lopez Pineda, Michael Lu, Brandon Arnieri, Ying Lu, William Capra, Ryan Copping, James Zou

There is a growing focus on making clinical trials more inclusive but the design of trial eligibility criteria remains challenging1,2,3. Here we systematically evaluate the effect of different eligibility criteria on cancer trial populations and outcomes with real-world data using the computational framework of Trial Pathfinder. We apply Trial Pathfinder to emulate completed trials of advanced non-small-cell lung cancer using data from a nationwide database of electronic health records comprising 61,094 patients with advanced non-small-cell lung cancer. Our analyses reveal that many common criteria, including exclusions based on several laboratory values, had a minimal effect on the trial hazard ratios. When we used a data-driven approach to broaden restrictive criteria, the pool of eligible patients more than doubled on average and the hazard ratio of the overall survival decreased by an average of 0.05. This suggests that many patients who were not eligible under the original trial criteria could potentially benefit from the treatments. We further support our findings through analyses of other types of cancer and patient-safety data from diverse clinical trials. Our data-driven methodology for evaluating eligibility criteria can facilitate the design of more-inclusive trials while maintaining safeguards for patient safety.



中文翻译:

使用真实数据和AI评估肿瘤学试验的资格标准

人们越来越关注使临床试验更具包容性,但是试验资格标准的设计仍然具有挑战性1,2,3。在这里,我们使用Trial Pathfinder的计算框架,使用现实世界的数据系统地评估了不同资格标准对癌症试验人群和结果的影响。我们使用Trial Pathfinder来模拟晚期非小细胞肺癌的完整试验,该研究使用了来自全国电子健康记录数据库的数据,该数据库包含61,094例晚期非小细胞肺癌患者。我们的分析表明,许多通用标准,包括基于多个实验室值的排除标准,对试验风险比的影响最小。当我们使用数据驱动的方法来扩大限制性标准时,合格患者的数量平均增加了一倍以上,总体生存的风险比平均降低了0.05。这表明许多不符合原始试验标准的患者可能会从治疗中受益。通过对其他类型癌症的分析和来自各种临床试验的患者安全性数据,我们进一步支持了我们的发现。我们以数据为依据的评估资格标准的方法可以帮助设计更具包容性的试验,同时保持对患者安全的保障。

更新日期:2021-04-08
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