Department of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA.
Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA.
BMC Med Res Methodol. 2021 Oct 10;21(1):210. doi: 10.1186/s12874-021-01394-8.
Disease surveillance of diabetes among youth has relied mainly upon manual chart review. However, increasingly available structured electronic health record (EHR) data have been shown to yield accurate determinations of diabetes status and type. Validated algorithms to determine date of diabetes diagnosis are lacking. The objective of this work is to validate two EHR-based algorithms to determine date of diagnosis of diabetes.
A rule-based ICD-10 algorithm identified youth with diabetes from structured EHR data over the period of 2009 through 2017 within three children's hospitals that participate in the SEARCH for Diabetes in Youth Study: Cincinnati Children's Hospital, Cincinnati, OH, Seattle Children's Hospital, Seattle, WA, and Children's Hospital Colorado, Denver, CO. Previous research and a multidisciplinary team informed the creation of two algorithms based upon structured EHR data to determine date of diagnosis among diabetes cases. An ICD-code algorithm was defined by the year of occurrence of a second ICD-9 or ICD-10 diabetes code. A multiple-criteria algorithm consisted of the year of first occurrence of any of the following: diabetes-related ICD code, elevated glucose, elevated HbA1c, or diabetes medication. We assessed algorithm performance by percent agreement with a gold standard date of diagnosis determined by chart review.
Among 3777 cases, both algorithms demonstrated high agreement with true diagnosis year and differed in classification (p = 0.006): 86.5% agreement for the ICD code algorithm and 85.9% agreement for the multiple-criteria algorithm. Agreement was high for both type 1 and type 2 cases for the ICD code algorithm. Performance improved over time.
Year of occurrence of the second ICD diabetes-related code in the EHR yields an accurate diagnosis date within these pediatric hospital systems. This may lead to increased efficiency and sustainability of surveillance methods for incidence of diabetes among youth.
青少年糖尿病的疾病监测主要依赖于人工图表审查。然而，越来越多的结构化电子健康记录（EHR）数据已被证明可以准确确定糖尿病的状态和类型。目前还缺乏用于确定糖尿病诊断日期的验证算法。本研究的目的是验证两种基于 EHR 的算法，以确定糖尿病的诊断日期。
基于规则的 ICD-10 算法从 2009 年至 2017 年期间参与 SEARCH for Diabetes in Youth 研究的三个儿童医院（俄亥俄州辛辛那提儿童医院、华盛顿州西雅图儿童医院和科罗拉多州丹佛儿童医院）的结构化 EHR 数据中确定了患有糖尿病的青少年。先前的研究和一个多学科团队为基于结构化 EHR 数据的两种算法的创建提供了信息，这些算法用于确定糖尿病病例的诊断日期。ICD 代码算法是通过第二份 ICD-9 或 ICD-10 糖尿病代码的发生年份定义的。多标准算法包括以下任何一项的首次出现年份：糖尿病相关的 ICD 代码、血糖升高、糖化血红蛋白升高或糖尿病药物。我们通过与通过图表审查确定的黄金标准诊断日期的百分比一致性来评估算法性能。
在 3777 例病例中，两种算法均与真实诊断年份高度一致，且分类不同（p=0.006）：ICD 代码算法的一致性为 86.5%，多标准算法的一致性为 85.9%。ICD 代码算法对于 1 型和 2 型病例均具有较高的一致性。随着时间的推移，性能有所提高。
EHR 中第二个 ICD 糖尿病相关代码的发生年份可在这些儿科医院系统中得出准确的诊断日期。这可能会提高青少年糖尿病发病率监测方法的效率和可持续性。
