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: 10223878
• 负责人: Charles Bailey
• 金额: $ 24.83万
• 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/10223878
• 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 数据评估全国网络中糖尿病的负担 儿童医院卫生系统 项目概要/摘要 糖尿病是一组以高血糖为特征的疾病，其原因是糖尿病的缺陷 胰岛素产生、胰岛素作用或两者兼而有之。 0-17岁儿童和青少年，儿科 糖尿病（PDM）是较常见的慢性疾病之一。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增加 过去30年。尽管如此，关于这些比率如何因人而异的研究还很有限。 社会人口统计学（例如种族/民族、地理）和临床特征（例如体重 指数）遍布美国不同地区。为了帮助填补这一空白，拟议的项目将使用电子 健康记录 (EHR) 数据，用于评估整体和按糖尿​​病类型划分的 PDM 患病率和发病率 以及患者的社会人口学和临床特征。数据将来自 PEDSnet，一个国家 儿科临床研究网络已将 EHR 数据转换为通用数据模型超过 650 万儿童。 PEDSnet 包括 8 个儿科医疗中心，为世界各地的儿童提供护理 50 个州；然而，儿童最集中的 11 个州是：科罗拉多州、特拉华州、佛罗里达州、伊利诺伊州、印第安纳州、 肯塔基州、密苏里州、新泽西州、俄亥俄州、宾夕法尼亚州和华盛顿州。除了参与 DiCAYA 联盟外，我们建议： （目标 1）——评估和提高用于识别的 EHR 数据的质量 PDM患者； （目标 2）—实施基于 EHR 的可计算表型方法 各类PDM支持准确、高效、及时的监控；并且，（目标 3）计算 PDM 的总体患病率和发病率，以及按糖尿病类型和患者社会人口统计划分的患病率和发病率 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，评估结构和语义数据质量并与 机构数据贡献者修复数据质量问题。目标 2（可计算表型分析） 将实施经过验证的算法来识别患有 PDM 的儿童和青少年。并且， 目标 3（比率计算）的分母人口将是居住在 62 个国家之一的患者 PEDSnet 拥有代表性数据并且与 PEDSnet 有 >1 个联系人的县 观察期间的机构。我们计划将我们的方法与其他方法相协调 DiCAYA 联盟致力于对疾病发生率进行标准化评估。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。我们注重评估和提高 EHR 数据质量、构建和 测试基于儿科 EHR 的可计算表型，并使用全国主要网络 儿科医疗中心是该提案的主要优势。