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

标题：使用电子病历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 项目摘要/摘要 糖尿病是一组以高血糖为特征的疾病，其特征是 胰岛素产生，胰岛素作用，或两者兼而有之。0-17岁的儿童和青少年，儿科 糖尿病是较为常见的慢性病之一。越来越多的证据表明 儿童和青少年中1型和2型糖尿病的发病率一直在上升 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 社会人口学(例如种族/民族、地域)和临床特征(例如身体质量 指数)在美国的不同地区。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，用于评估总体和按糖尿病类型划分的DM的患病率和发病率 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 650万儿童。PEDSnet包括8个儿科医疗中心，总共为儿童提供护理 50个州；然而，儿童最集中的11个州是：CO、DE、FL、IL、IN、 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财团外，我们还建议： (目标1)--评价和改进将用于确定的电子健康记录数据的质量 患有PDM的患者；(目标2)-实施基于EHR的可计算表型方法 每种类型的产品数据管理，以支持准确、高效和及时的监控；以及，(目标3)计算 总体和按糖尿病类型和患者社会人口学分类的糖尿病的患病率和发病率 和临床特点。目标1(数据质量)将利用PEDSnet成熟的 数据质量计划，同时评估结构和语义数据质量，并与 机构数据贡献者，以补救数据质量问题。AIM 2(可计算表型) 将实施经过验证的算法，以识别具有产品数据管理的儿童和青少年。还有， 目标3的分母群体(比率计算)将是居住在62个 PEDSnet具有代表性数据且与PEDSnet有联系的县 在观察期内的机构。我们计划将我们的方法与其他方法协调一致 DiCAYA联盟，以实现对发病率的标准化评估。我们的团队有广泛的 在PCORnet和OHDSI等财团中工作的经验，这些财团分享和执行彼此的经验 数据科学方法。我们注重评估和提高电子病历数据质量，建设和 测试基于儿科EHR的可计算表型，并使用全国主要疾病网络 儿科医疗中心是这项提议的关键优势。

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.