Department of Biomedical Informatics, Columbia University, New York, NY, United States.
Department of Pediatrics, Division of Pediatric Hematology, Oncology, and Stem Cell Transplantation, Columbia University Irving Medical Center, New York, NY, United States.
J Biomed Inform. 2022 Mar;127:104032. doi: 10.1016/j.jbi.2022.104032. Epub 2022 Feb 18.
To present an approach on using electronic health record (EHR) data that assesses how different eligibility criteria, either individually or in combination, can impact patient count and safety (exemplified by all-cause hospitalization risk) and further assist with criteria selection for prospective clinical trials.
Trials in three disease domains - relapsed/refractory (r/r) lymphoma/leukemia; hepatitis C virus (HCV); stages 3 and 4 chronic kidney disease (CKD) - were analyzed as case studies for this approach. For each disease domain, criteria were identified and all criteria combinations were used to create EHR cohorts. Per combination, two values were derived: (1) number of eligible patients meeting the selected criteria; (2) hospitalization risk, measured as the hazard ratio between those that qualified and those that did not. From these values, k-means clustering was applied to derive which criteria combinations maximized patient counts but minimized hospitalization risk.
Criteria combinations that reduced hospitalization risk without substantial reductions on patient counts were as follows: for r/r lymphoma/leukemia (23 trials; 9 criteria; 623 patients), applying no infection and adequate absolute neutrophil count while forgoing no prior malignancy; for HCV (15; 7; 751), applying no human immunodeficiency virus and no hepatocellular carcinoma while forgoing no decompensated liver disease/cirrhosis; for CKD (10; 9; 23893), applying no congestive heart failure.
Within each disease domain, the more drastic effects were generally driven by a few criteria. Similar criteria across different disease domains introduce different changes. Although results are contingent on the trial sample and the EHR data used, this approach demonstrates how EHR data can inform the impact on safety and available patients when exploring different criteria combinations for designing clinical trials.
介绍一种利用电子健康记录（EHR）数据的方法，评估不同的入选标准（单独或组合使用）如何影响患者数量和安全性（以全因住院风险为例），并进一步帮助选择前瞻性临床试验的标准。
以三种疾病领域的试验 - 复发/难治性（r/r）淋巴瘤/白血病；丙型肝炎病毒（HCV）；3 期和 4 期慢性肾脏病（CKD）- 作为该方法的案例研究进行分析。对于每个疾病领域，确定了标准，并使用所有标准组合创建了 EHR 队列。对于每个组合，得出了两个值：（1）符合所选标准的合格患者人数；（2）住院风险，以符合条件的患者与不符合条件的患者之间的风险比来衡量。从这些值中，应用 K-均值聚类来确定哪些标准组合最大限度地增加了患者数量，但最小化了住院风险。
在不显著减少患者数量的情况下降低住院风险的标准组合如下：对于 r/r 淋巴瘤/白血病（23 项试验；9 项标准；623 名患者），应用无感染和足够的绝对中性粒细胞计数，同时放弃无先前恶性肿瘤；对于 HCV（15 项；7 项；751 名患者），应用无人类免疫缺陷病毒和无肝细胞癌，同时放弃无失代偿性肝病/肝硬化；对于 CKD（10 项；9 项；23893 名患者），应用无充血性心力衰竭。
在每个疾病领域内，更剧烈的影响通常是由少数几个标准驱动的。不同疾病领域的相似标准会带来不同的变化。尽管结果取决于试验样本和使用的 EHR 数据，但该方法展示了如何利用 EHR 数据在探索不同的临床试验设计标准组合时，了解对安全性和可用患者的影响。
