Department of Biomedical Informatics, Columbia University, New York, New York, USA.
Health Services and Systems Research, Duke-NUS Medical School, Singapore.
J Am Med Inform Assoc. 2021 Jan 15;28(1):14-22. doi: 10.1093/jamia/ocaa276.
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.
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.
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.
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.
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.
本研究旨在利用电子健康记录（EHR）数据评估 COVID-19 临床试验入选标准对招募和可观察临床结局的影响。
2020 年 6 月 18 日，我们从 ClinicalTrials.gov 上所有干预性 COVID-19 试验（n=288）中确定了常用的入选标准，包括年龄、妊娠、氧饱和度、丙氨酸/天冬氨酸转氨酶、血小板和估算肾小球滤过率。我们将这些常用标准应用于哥伦比亚大学欧文医学中心（CUIMC）COVID-19 患者的 EHR 数据（2020 年 3 月至 6 月），评估其对患者入组的影响，以及机械通气、气管切开术和院内死亡复合终点的发生情况。
CUIMC EHR 中纳入分析的 COVID-19 患者有 3251 例。中位随访期为 10 天（四分位距 4-28 天）。在随访期间，COVID-19 队列中有 18.1%（n=587）发生复合事件。在一个常见入选标准的假设性试验中，在可评估数据的患者中，有 33.6%（690/2051）符合入选标准，其中 22.2%（153/690）发生了复合事件。
通过根据 COVID-19 患者的特征调整常见入选标准的阈值，我们可以从较少的患者中观察到更多的复合事件。
本研究表明，利用 COVID-19 患者的 EHR 数据来指导入选标准及其阈值的选择具有潜力，支持基于数据驱动的参与者选择优化，以提高 COVID-19 试验的统计效力。
