Using EHR Data to Evaluate the Burden of Diabetes Mellitus in a National Network of Children's Hospital Health Systems

使用 EHR 数据评估全国儿童医院卫生系统网络中糖尿病的负担

• 批准号: 10413797
• 负责人: Charles Bailey
• 金额: $ 44.98万
• 依托单位: CHILDREN'S HOSP OF PHILADELPHIA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2025-09-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10413797
• 关键词: Using; EHR; Data; Evaluate; Burden

Title: Using EHR Data to Evaluate the Burden of Diabetes Mellitus in a National Network of Children’s Hospital Health Systems Project Summary/Abstract Diabetes mellitus is a group of disorders characterized by hyperglycemia resulting from defects in insulin production, insulin action, or both. In children and adolescents 0-17 years-old, pediatric diabetes mellitus (PDM) is one of the more common chronic diseases. Mounting evidence suggests that rates of both Type 1 and 2 diabetes mellitus in children and adolescents have been increasing over the past 30 years. Nonetheless, there has been limited research on how these rates differ by sociodemographics (e.g., race/ethnicity, geography) and clinical characteristics (e.g., body mass index) across diverse regions of the US. To help fill this gap, the proposed project will use electronic health record (EHR) data to assess the prevalence and incidence of PDM, overall and by diabetes type and patient sociodemographic and clinical characteristics. Data will come from PEDSnet, a national pediatric clinical research network that has transformed EHR data to a common data model for over 6.5 million children. PEDSnet includes 8 pediatric medical centers that provide care to children in all 50 states; however, the 11 states with the greatest concentration of children are: CO, DE, FL, IL, IN, KY, MO, NJ, OH, PA, and WA. In addition to participating in the DiCAYA consortium, we propose: (Aim 1)—to evaluate and improve the quality of the EHR data that will be used for identifying patients with PDM; (Aim 2)—to implement an EHR-based computable phenotype methodology for each type of PDM to support accurate, efficient, and timely surveillance; and, (Aim 3) to compute prevalence and incidence rates of PDM, overall and by diabetes type and patient sociodemographic and clinical characteristics. Aim 1 (data quality) will take advantage of PEDSnet’s well-established data quality program that evaluates both structural and semantic data quality and works with institutional data contributors to remediate data quality problems. Aim 2 (Computable Phenotyping) will implement validated algorithms for identifying children and adolescents with PDM. And, the denominator population for Aim 3 (Rate Computations) will be patients who reside in one of the 62 counties for which PEDSnet has representative data and who have >1 contacts with a PEDSnet institution during the observation period. We plan to harmonize our methods with the rest of the DiCAYA consortium to enable standardized assessments of disease rates. Our team has extensive experience working in consortia, such as PCORnet and OHDSI, that share and execute each other’s data science methods. Our attention to evaluating and improving EHR data quality, constructing and testing pediatric EHR-based computable phenotypes, and use of a national network of major pediatric medical centers are key strengths of this proposal.

标题：使用EHR数据评估全国糖尿病网络中的糖尿病负担 儿童医院卫生系统 项目总结/摘要 糖尿病是一组以高血糖症为特征的疾病，高血糖症是由以下缺陷引起的： 胰岛素产生、胰岛素作用或两者。在0-17岁的儿童和青少年中， 糖尿病（PDM）是一种常见的慢性疾病。越来越多的证据表明 儿童和青少年的1型和2型糖尿病发病率一直在上升， 在过去的30年里。尽管如此，关于这些比率如何不同的研究有限， 社会人口统计学（例如，种族/民族、地理）和临床特征（例如，体重 在美国的不同地区。为了帮助填补这一空白，拟议项目将使用电子 健康记录（EHR）数据，以评估总体和按糖尿病类型划分的PDM患病率和发病率 以及患者的社会人口统计学和临床特征。数据将来自PEDSnet，一个全国性的 儿科临床研究网络已将EHR数据转换为通用数据模型， 6.5百万儿童PEDSnet包括8个儿科医疗中心， 50个州;然而，儿童最集中的11个州是：CO，DE，FL，IL，IN， KY，MO，NJ，OH，PA和WA。除了参加DiCAYA财团外，我们还建议： (Aim 1）-评估和提高EHR数据的质量，这些数据将用于识别 PDM患者;（目的2）-实施基于EHR的可计算表型方法， 每种类型的PDM，以支持准确，有效和及时的监督;以及，（目标3）计算 总体和按糖尿病类型和患者社会人口统计学列出的PDM患病率和发生率 和临床特征。目标1（数据质量）将利用PEDSnet完善的 数据质量程序，用于评估结构和语义数据质量， 机构数据贡献者，以补救数据质量问题。目标2（可计算表型） 将实施经验证的算法，用于识别PDM中的儿童和青少年。并且 目标3（发生率计算）的分母人群将是居住在62个国家之一的患者 PEDSnet具有代表性数据且与PEDSnet有1次以上联系的县 在观察期内。我们计划使我们的方法与其他的方法协调一致。 DiCAYA联盟，使疾病率的标准化评估。我们的团队拥有广泛的 在PCORnet和OHDSI等联盟中工作的经验，这些联盟共享并执行彼此的 数据科学方法。我们关注评估和提高EHR数据质量，构建和 测试儿科EHR为基础的可计算表型，并使用一个国家网络的主要 儿科医疗中心是该提案的主要优势。