Automatic Population of the Case Report Forms for an International Multifactorial Adaptive Platform Trial Amid the COVID-19 Pandemic.

Andrew J King, Lisa Higgins, Carly Au, Salim Malakouti, Edvin Music, Kyle Kalchthaler, Gilles Clermont, William Garrard, David T Huang, Bryan J McVerry, Christopher W Seymour, Kelsey Linstrum, Amanda McNamara, Cameron Green, India Loar, Tracey Roberts, Oscar Marroquin, Derek C Angus, Christopher M Horvat
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Abstract

Objectives: To automatically populate the case report forms (CRFs) for an international, pragmatic, multifactorial, response-adaptive, Bayesian COVID-19 platform trial.

Methods: The locations of focus included 27 hospitals and 2 large electronic health record (EHR) instances (1 Cerner Millennium and 1 Epic) that are part of the same health system in the United States. This paper describes our efforts to use EHR data to automatically populate four of the trial's forms: baseline, daily, discharge, and response-adaptive randomization.

Results: Between April 2020 and May 2022, 417 patients from the UPMC health system were enrolled in the trial. A MySQL-based extract, transform, and load pipeline automatically populated 499 of 526 CRF variables. The populated forms were statistically and manually reviewed and then reported to the trial's international data coordinating center.

Conclusions: We accomplished automatic population of CRFs in a large platform trial and made recommendations for improving this process for future trials.

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在 COVID-19 大流行中自动生成国际多因素自适应平台试验的病例报告表。
目的为一项国际性、务实、多因素、反应自适应、贝叶斯 COVID-19 平台试验自动填充病例报告表 (CRF):重点研究地点包括隶属于美国同一医疗系统的 27 家医院和 2 个大型电子病历 (EHR) 实例(1 个 Cerner Millennium 和 1 个 Epic)。本文介绍了我们在使用电子病历数据自动填充试验的四种表格方面所做的努力:基线、日常、出院和反应自适应随机化:2020年4月至2022年5月期间,UPMC医疗系统的417名患者加入了试验。基于 MySQL 的提取、转换和加载管道自动填充了 526 个 CRF 变量中的 499 个。填充后的表格经过统计和人工审核,然后报告给试验的国际数据协调中心:我们在一项大型平台试验中实现了 CRF 的自动填充,并为今后的试验提出了改进这一流程的建议。
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