Li Piaopiao, Spector Eliot, Alkhuzam Khalid, Patel Rahul, Donahoo William T, Bost Sarah, Lyu Tianchen, Wu Yonghui, Hogan William, Prosperi Mattia, Dixon Brian E, Dabelea Dana, Utidjian Levon H, Crume Tessa L, Thorpe Lorna, Liese Angela D, Schatz Desmond A, Atkinson Mark A, Haller Michael J, Shenkman Elizabeth A, Guo Yi, Bian Jiang, Shao Hui
Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, Florida, USA.
Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.
Diabetes Obes Metab. 2025 Jan;27(1):102-110. doi: 10.1111/dom.15987. Epub 2024 Sep 30.
To develop an automated computable phenotype (CP) algorithm for identifying diabetes cases in children and adolescents using electronic health records (EHRs) from the UF Health System.
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 aged <18 years who had (1) glycated haemoglobin levels ≥ 6.5%; or (2) fasting glucose levels ≥126 mg/dL; or (3) random plasma glucose levels ≥200 mg/dL; or (4) a 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.
Presumed cases without type 1 (T1D) or type 2 diabetes (T2D) 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, respectively. The sensitivity, specificity, and positive predictive values of the CP algorithm were 94%, 98% and 96%, respectively, for T1D and 95%, 95% and 73% for T2D.
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.
利用佛罗里达大学健康系统的电子健康记录（EHR）开发一种自动可计算表型（CP）算法，用于识别儿童和青少年糖尿病病例。
CP算法基于EHR（佛罗里达大学健康系统2012 - 2020年）的结构化数据迭代得出。我们在年龄小于18岁的个体中随机选择了536例疑似病例，这些个体具有以下情况之一：（1）糖化血红蛋白水平≥6.5%；或（2）空腹血糖水平≥126mg/dL；或（3）随机血浆葡萄糖水平≥200mg/dL；或（4）住院或门诊就诊的糖尿病相关诊断代码；或（5）开具、使用或配发糖尿病相关药物。四名评审员独立查阅患者病历以确定糖尿病状态和类型。
无1型糖尿病（T1D）或2型糖尿病（T2D）诊断代码的疑似病例被归类为非糖尿病/其他类型糖尿病。其余病例若最近诊断为T1D则归类为T1D，若最近诊断为T2D则归类为T2D。接下来，我们应用一系列可确定糖尿病类型的诊断和检查项目清单（例如，使用类固醇提示为继发性糖尿病）来纠正第一步中的错误分类。在536例经审查的病例中，分别有159例和64例为T1D和T2D。CP算法对T1D的敏感性、特异性和阳性预测值分别为94%、98%和96%，对T2D的敏感性、特异性和阳性预测值分别为95%、95%和73%。
我们基于佛罗里达大学健康系统的EHR数据开发了一种针对青少年糖尿病的高度准确的基于EHR的CP。与先前研究一致，使用这些方法更难识别T2D。
