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Developing an Automated Algorithm for Identification of Children and Adolescents with Diabetes using Electronic Health Records from OneFlorida+ Clinical Research Network
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1 2025
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Source: Diabetes Obes Metab. 27(1):102-110
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Alternative Title:Diabetes Obes Metab
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Personal Author:
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Description:Objective
The rapid growth of electronic health records (EHRs) nationwide presents a unique opportunity for conducting automated diabetes surveillance in the United States. However, the validity of such a surveillance system relies on the accuracy of algorithms used to identify diabetes cases, which are currently lacking. This study aimed to develop an automated computable phenotype (CP) algorithm for identifying diabetes cases in children and adolescents within the EHR.
Materials and Methods
The CP algorithm was iteratively derived based on structured data from EHRs (UF Health system 2012–2020). We randomly selected 536 presumed cases among individuals < 18 years old who has (1) HbA1c ≥ 6.5%; or (2) fasting glucose ≥ 126 mg/dL; or (3) random plasma glucose ≥ 200 mg/dL; (4) diabetes-related diagnosis code from an inpatient or outpatient encounter; or (5) prescribed, administered, or dispensed diabetes-related medication. Four reviewers independently reviewed the patient charts to determine diabetes status and type.
Results
Presumed cases without type 1 (T1D) or type 2 (T2D) diabetes diagnosis codes were categorized as non-diabetes/other types of diabetes. The rest were categorized as T1D if the most recent diagnosis was T1D, or otherwise categorized as T2D if the most recent diagnosis was T2D. Next, we applied a list of diagnoses and procedures that can determine diabetes type (e.g., steroid use suggests induced diabetes) to correct misclassifications from step 1. Among the 536 reviewed cases, 159 and 64 had T1D and T2D. The sensitivity, specificity, and positive predictive values of the CP algorithm were 94%, 98%, and 96% for T1D; 95%, 95%, and 73% for T2D.
Conclusion
We developed a highly accurate EHR-based CP for diabetes in youth based on EHR data from UF Health. Consistent with prior studies, T2D was more difficult to identify using these methods.
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Pubmed ID:39344840
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Pubmed Central ID:PMC11620941
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Funding:
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Volume:27
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Issue:1
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Supporting Files:No Additional Files