Journal Article Epublish
PubMed: 42892882 DOI: 10.2337/doc26-0082
Electronic Health Record-Based Algorithms May Improve Identification of Cases of Atypical Diabetes, Including in Groups Underrepresented in Biomedical Research.
Diabetes Obes Cardiometab CARE 1(5):874-882
Authors: Victoria Chen, Cristina I Fernández Hernández, Megan Griff, Julia L Douvas, Micah Koss, Melton Fan, Will Marshall, Evelyn Greaux, Wyatt Pfau, Marjan Rezaei, Jeremy Graber, Vatsala Singh, Laura K Wiley, Sridharan Raghavan, Neda Rasouli, Miriam S Udler, Sara J Cromer
Affiliations
- Diabetes Unit, Massachusetts General Hospital, Boston, MA.
- Diabetes Unit, Massachusetts General Hospital, Boston, MA. University of Puerto Rico School of Medicine, San Juan, Puerto Rico.
- Division of General Internal Medicine, Department of Medicine, University of Colorado School of Medicine, Aurora, CO.
- Division of Endocrinology, Metabolism and Diabetes, University of Colorado School of Medicine, Aurora, CO.
- Physical Therapy Program, Department of Physical Medicine and Rehabilitation, University of Colorado, Aurora, CO.
- U.S. Department of Veterans Affairs Eastern Colorado Health Care System, Aurora, CO.
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO. Institute for Informatics Data Science and Biostatistics, Department of Neurology, Washington University in St. Louis, St. Louis, MO.
- Department of Medicine, Harvard Medical School, Boston, MA.
- Program in Metabolism, Broad Institute of MIT and Harvard, Cambridge, MA.
Abstract
OBJECTIVE
To determine whether electronic health record (EHR)-based algorithms can identify individuals with atypical diabetes (AD) eligible for the Rare and Atypical Diabetes Network (RADIANT) study and whether these algorithms improve identification and enrollment of individuals from populations underrepresented in biomedical research (UBR).
RESEARCH DESIGN AND METHODS
Two RADIANT clinical sites independently developed EHR-based algorithms to identify AD phenotypes, including a lean type 2 diabetes-like physiology without features of metabolic syndrome (site 1) and concurrent diagnoses of type 1 and type 2 diabetes (site 2). Demographic characteristics were compared using χ 2 tests between algorithm-identified individuals and RADIANT participants enrolled prior to the implementation of EHR-based algorithm recruitment.
RESULTS
Deployment of EHR-based algorithms identified 550 individuals in site 1 and 620 individuals in site 2, with 108 and 147 deemed eligible for RADIANT recruitment, respectively. Compared with prior RADIANT participants at these sites, EHR-identified, RADIANT-eligible individuals were more likely to self-identify as a race or ethnicity besides non-Hispanic White and prefer a language other than English. Of the 53 and 57 individuals who were successfully contacted at sites 1 and 2, respectively, 8 and 14 enrolled in RADIANT, representing meaningful contributions to total enrollment (21% and 22% at sites 1 and 2, respectively) compared with pre-EHR-based recruitment. However, demographics of newly enrolled individuals did not differ from traditionally recruited participants.
CONCLUSIONS
EHR-based algorithms successfully identified rare diabetes phenotypes and yielded greater representation of study-eligible individuals from UBR populations than traditional participant identification methods. However, additional strategies are needed to address persistent barriers to participation of UBR populations in AD research.
MeSH
Diabetes Mellitus, Biomedical Research, Phenotype, Algorithms, Electronic Health Records, Research, Records, Demography, Diabetes Mellitus, Type 2