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Towards clinical data-driven eligibility criteria optimization for interventional COVID-19 clinical trials
Journal of the American Medical Informatics Association ( IF 6.4 ) Pub Date : 2020-12-01 , DOI: 10.1093/jamia/ocaa276
Jae Hyun Kim 1 , Casey N Ta 1 , Cong Liu 1 , Cynthia Sung 2 , Alex M Butler 1 , Latoya A Stewart 1 , Lyudmila Ena 1 , James R Rogers 1 , Junghwan Lee 1 , Anna Ostropolets 1 , Patrick B Ryan 1, 3, 4 , Hao Liu 1 , Shing M Lee 5 , Mitchell S V Elkind 6, 7 , Chunhua Weng 1
Affiliation  

Abstract
Objective
This research aims to evaluate the impact of eligibility criteria on recruitment and observable clinical outcomes of COVID-19 clinical trials using electronic health record (EHR) data.
Materials and Methods
On June 18, 2020, we identified frequently used eligibility criteria from all the interventional COVID-19 trials in ClinicalTrials.gov (n = 288), including age, pregnancy, oxygen saturation, alanine/aspartate aminotransferase, platelets, and estimated glomerular filtration rate. We applied the frequently used criteria to the EHR data of COVID-19 patients in Columbia University Irving Medical Center (CUIMC) (March 2020–June 2020) and evaluated their impact on patient accrual and the occurrence of a composite endpoint of mechanical ventilation, tracheostomy, and in-hospital death.
Results
There were 3251 patients diagnosed with COVID-19 from the CUIMC EHR included in the analysis. The median follow-up period was 10 days (interquartile range 4–28 days). The composite events occurred in 18.1% (n = 587) of the COVID-19 cohort during the follow-up. In a hypothetical trial with common eligibility criteria, 33.6% (690/2051) were eligible among patients with evaluable data and 22.2% (153/690) had the composite event.
Discussion
By adjusting the thresholds of common eligibility criteria based on the characteristics of COVID-19 patients, we could observe more composite events from fewer patients.
Conclusions
This research demonstrated the potential of using the EHR data of COVID-19 patients to inform the selection of eligibility criteria and their thresholds, supporting data-driven optimization of participant selection towards improved statistical power of COVID-19 trials.


中文翻译:

迈向临床数据驱动的 COVID-19 介入临床试验资格标准优化

摘要
客观的
本研究旨在使用电子健康记录 (EHR) 数据评估资格标准对 COVID-19 临床试验的招募和可观察临床结果的影响。
材料和方法
2020 年 6 月 18 日,我们从 ClinicalTrials.gov(n = 288)中的所有介入性 COVID-19 试验中确定了常用的资格标准,包括年龄、妊娠、氧饱和度、丙氨酸/天冬氨酸氨基转移酶、血小板和估计的肾小球滤过率. 我们将常用的标准应用于哥伦比亚大学欧文医学中心 (CUIMC) 的 COVID-19 患者的 EHR 数据(2020 年 3 月至 2020 年 6 月),并评估了它们对患者增加的影响以及机械通气、气管切开术复合终点的发生,以及院内死亡。
结果
分析中包括了来自 CUIMC EHR 的 3251 名被诊断为 COVID-19 的患者。中位随访期为 10 天(四分位距为 4-28 天)。在随访期间,18.1% (n = 587) 的 COVID-19 队列发生了复合事件。在一项具有共同资格标准的假设试验中,33.6% (690/2051) 的患者符合可评估的数据,22.2% (153/690) 的患者发生复合事件。
讨论
通过根据 COVID-19 患者的特征调整通用资格标准的阈值,我们可以从更少的患者中观察到更多的复合事件。
结论
这项研究证明了使用 COVID-19 患者的 EHR 数据为选择资格标准及其阈值提供信息的潜力,支持数据驱动的参与者选择优化以提高 COVID-19 试验的统计能力。
更新日期:2021-01-16
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