Developing a synthetic control group using electronic health records: Application to a single-arm lifestyle intervention study

• Type of Material: Article
• Authors: Yi-An Ko, Emory University; Zhenchao Chen, Emory University; Chang Liu, Emory University; Yingtian Hu, Emory University; Arshed Quyyumi, Emory University; Lance Waller, Emory University; Melinda Higgins, Emory University; Thomas Ziegler, Emory University; Kenneth Brigham, Emory University; Gregory Martin, Emory University
• Language: English
• Date: 2021-10-06
• Publisher: ELSEVIER
• Publication Version: Final Published Version
• Copyright Statement: © 2021 The Author(s)
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.pmedr.2021.101572
• Title of Journal or Parent Work: PREVENTIVE MEDICINE REPORTS
• Volume: 24
• Start Page: 101572
• End Page: 101572
• Grant/Funding Information: This work is supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR002378 and supported in part by 2020 Synergy II Nexus Award (Differentially-Private, Synthetic Controls for the Center for Health Discovery and Well-Being (CHDWB) Cohort: Data Science to Assess Health, Wellness and Disease) from the Woodruff Health Science Center of Emory University. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Woodruff Health Science Center, Emory University, or the National Institutes of Health.
• Keywords: DESIGN; HISTORICAL CONTROL DATA; Pseudo control; Science & Technology; PREDICTIVE HEALTH; Life Sciences & Biomedicine; Electronic medical record; Controlled trials; Doubly robust; Public, Environmental & Occupational Health
• Research Categories: Health Sciences, Health Care Management; Health Sciences, Public Health

Abstract

The electronic health records (EHR) infrastructure offers a tremendous resource for identifying controls who match the characteristics of study participants in a single-arm trial. The objectives are to (1) demonstrate the feasibility of curating a synthetic control group for an existing study cohort through EHR data extraction and (2) evaluate the effect of a lifestyle intervention on selected cardiovascular health metrics. A total of 711 university employees were recruited between 2008 and 2012 to participate in a health partner intervention to improve cardiovascular health and were followed for five years. Data of nearly 8000 eligible subjects were extracted from the EHR to create a synthetic control cohort during the same study period. To minimize confounding, crude comparison, exact matching, propensity score matching, and doubly robust estimation were used to compare the selected cardiovascular health metrics at 1 and 5 years of follow-up. Blood pressure and body mass index improved in the intervention group compared to the EHR synthetic controls. The findings of changes in lipid measurements were somewhat unexpected. When analyzing the subgroup without lipid-lowering medications, the intervention group exhibited better control of cholesterol levels over time than did our synthetic controls. Some measurements in the EHR system may be more robust for synthetic selection than others. EHR synthetic controls can provide an alternative to estimate intervention effects appropriately in single-arm studies for these measurements.