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数据评估国家网络中糖尿病的负担 儿童医院卫生系统 项目摘要/摘要 糖尿病是一组疾病，其特征是因缺陷而导致的高血糖症。 胰岛素产生，胰岛素作用或两者兼而有之。在儿童和青少年0-17岁的小儿 糖尿病（PDM）是最常见的慢性疾病之一。越来越多的证据表明 儿童和青少年中1型和2型糖尿病的比率一直在增加 在过去的30年中。但是，关于这些速率如何不同的研究有限 社会人口统计学（例如种族/种族，地理）和临床特征（例如，体重 索引）在美国的潜水区域。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，以评估PDM的患病率和事件，总体和糖尿病类型 以及患者的社会人口统计学和临床​​特征。数据将来自全国PEDSNET 将EHR数据转换为过度数据模型的小儿临床研究网络 650万儿童。 PEDSNET包括8个儿科医疗中心，可为所有人提供护理 50个州；但是，儿童集中最高的11个州是：Co，de，de，fl，il，in，in， KY，MO，NJ，OH，PA和WA。除了参加Dicaya财团外，我们建议： （目标1） - 评估和改善将用于识别的EHR数据的质量 PDM患者； （AIM 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，高效和及时的监视；并且（目标3）计算 PDM的患病率和发病率，整体和糖尿病类型和患者社会人口统计学 和临床特征。 AIM 1（数据质量）将利用PEDSNET良好的 评估结构和语义数据质量的数据质量计划，并与 机构数据贡献者可以补充数据质量问题。 AIM 2（可计算表型） 将实施经过验证的算法，以识别PDM的儿童和青少年。而且， AIM 3（速率计算）的分母种群将是居住在62中之一的患者 PEDSNET具有代表性数据并且与PEDSNET有> 1个联系的县 在观察期间的机构。我们计划将我们的方法与其余的 DICAYA联盟能够对疾病率进行标准化评估。我们的团队有广泛的 在Consortia（例如PCORNET和OHDSI）中工作的经验，共享和执行彼此的经验 数据科学方法。我们关注评估和改善EHR数据质量，构建和 测试基于小儿EHR的可计算表型，并使用主要的主要网络 小儿医疗中心是该提议的关键优势。