Abstract
BackgroundThe 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.ObjectiveThe purpose of this study was to develop a computable phenotype for accurately and efficiently identifying diabetes and separating T1DM from T2DM in pediatric patients.MethodsThis 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.ResultsA 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.ConclusionsWe 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.
Highlights
Diabetes is one of the most common chronic diseases seen during childhood and adolescence
We developed a computable phenotype for identifying type 1 diabetes mellitus T2DM (T1DM) correctly and efficiently
In our first query of 300 medical records drawn from the UF University of Florida Health system (Health) Integrated Data Repository (IDR), 5 cases had no discerning diagnosis based on the diagnosis ratio, and these were excluded from the study
Summary
Diabetes is one of the most common chronic diseases seen during childhood and adolescence. Through the use of algorithms derived from electronic health record data, accurate identification of patients with T1DM versus T2DM may be possible. This study was conducted within a self-contained data set overseen by Kaiser Permanente As such, this does not give a comprehensive insight into patients seen at a variety of health settings using different electronic record systems. There is a need for timely real-world population-level monitoring of the incidence, prevalence, and disease course of diabetes in youth that includes the ability to separate T1DM from T2DM. The incidence of both type 1 diabetes (T1DM) and type 2 diabetes (T2DM) in children and youth is increasing. The current approach for identifying pediatric diabetes and separating by type is costly, because it requires substantial manual efforts
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