An Iterative Process for Identifying Pediatric Patients With Type 1 Diabetes: Retrospective Observational Study

Heather Lynne Morris; William Troy Donahoo; Brittany Bruggeman; Chelsea Zimmerman; Paul Hiers; Victor W Zhong; Desmond Schatz

doi:10.2196/18874

An Iterative Process for Identifying Pediatric Patients With Type 1 Diabetes: Retrospective Observational Study

JMIR Med Inform. 2020 Sep 4;8(9):e18874. doi: 10.2196/18874.

Authors

Heather Lynne Morris¹, William Troy Donahoo², Brittany Bruggeman², Chelsea Zimmerman², Paul Hiers², Victor W Zhong³, Desmond Schatz⁴

Affiliations

¹ Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, United States.
² Department of Medicine, University of Florida, Gainesville, FL, United States.
³ Division of Nutritional Sciences, Cornell University, Ithaca, NY, United States.
⁴ Department of Pediatrics, University of Florida, Gainesville, FL, United States.

PMID: 32886067
PMCID: PMC7501576
DOI: 10.2196/18874

Abstract

Background: The incidence of both type 1 diabetes (T1DM) and type 2 diabetes (T2DM) in children and youth is increasing. However, the current approach for identifying pediatric diabetes and separating by type is costly, because it requires substantial manual efforts.

Objective: The purpose of this study was to develop a computable phenotype for accurately and efficiently identifying diabetes and separating T1DM from T2DM in pediatric patients.

Methods: This retrospective study utilized a data set from the University of Florida Health Integrated Data Repository to identify 300 patients aged 18 or younger with T1DM, T2DM, or that were healthy based on a developed computable phenotype. Three endocrinology residents/fellows manually reviewed medical records of all probable cases to validate diabetes status and type. This refined computable phenotype was then used to identify all cases of T1DM and T2DM in the OneFlorida Clinical Research Consortium.

Results: A total of 295 electronic health records were manually reviewed; of these, 128 cases were found to have T1DM, 35 T2DM, and 132 no diagnosis. The positive predictive value was 94.7%, the sensitivity was 96.9%, specificity was 95.8%, and the negative predictive value was 97.6%. Overall, the computable phenotype was found to be an accurate and sensitive method to pinpoint pediatric patients with T1DM.

Conclusions: We developed a computable phenotype for identifying T1DM correctly and efficiently. The computable phenotype that was developed will enable researchers to identify a population accurately and cost-effectively. As such, this will vastly improve the ease of identifying patients for future intervention studies.

Keywords: computable phenotype; electronic health record; pediatric; type 1 diabetes.

©Heather Lynne Morris, William Troy Donahoo, Brittany Bruggeman, Chelsea Zimmerman, Paul Hiers, Victor W Zhong, Desmond Schatz. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 04.09.2020.