Revealing Health Trajectories of Chronic Kidney Disease for Precision Medicine

揭示精准医学慢性肾脏病的健康轨迹

• 批准号: 10445907
• 负责人: Jing Su
• 金额: $ 32.94万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10445907
• 关键词: Acute Renal Failure with Renal Papillary Necrosis; Address; Adult; Affect; Artificial Intelligence; Big Data; Characteristics; Chronic Disease; Chronic Kidney Failure; Clinic; Clinic Visits; Clinical; Clinical Informatics; Clinical Sciences; Clonidine; Collection; Complex; Computerized Medical Record; Data; Data Collection; Databases; Decision Making; Development; Diagnosis; Diet; Disease; Disease Management; Disease Progression; Drug Combinations; Drug Interactions; Drug usage; Early identification; Economic Burden; Electronic Health Record; Fluconazole; General Population; Genetic; Glomerular Filtration Rate; Goals; Graph; Health; Health Insurance; Health system; Incidence; Indiana; Individual; Institutes; Kidney; Knowledge; Learning; Life Expectancy; Mathematics; Medical; Modeling; Myopathy; Omeprazole; Pathogenicity; Patient Care; Patients; Pattern; Pharmaceutical Preparations; Population; Quality Control; Quality of life; Research; Risk; Risk Factors; Societies; Source; Translational Research; Treatment outcome; Universities; Work; base; clinical care; clinical decision support; clinical encounter; cohort; comorbidity; complex data; data harmonization; data modeling; economic determinant; experience; follow-up; heterogenous data; indexing; insurance claims; lifestyle factors; medical schools; medical specialties; modifiable risk; nephrotoxicity; novel; novel strategies; novel therapeutics; patient subsets; personalized decision; phenome; precision medicine; prevent; reconstruction; socioeconomics; success; trait; underserved community

Project summary Chronic kidney disease (CKD) is common, affecting 14.8% of US adults, and disproportionately more in diverse and underserved communities. CKD significantly reduces life expectancy and quality of life, while imposing tremendous economic burden on society. A critical need persists for early identification of modifiable risk factors in susceptible populations and to establish actionable support for medical decision making. Among the modifiable risk factors, drug induced acute kidney injury (AKI) contributes to CKD development and progression. The current knowledge of nephrotoxic drug-drug interactions (DDIs) is insufficient to prevent harm in heterogenous patient subpopulations. Electronic health records (EHRs) from electronic medical records (EMR) and health insurance claims data can help predict disparate CKD progression trajectories and uncover novel nephrotoxic drug interactions. The Indiana University School of Medicine (IUSM) EHR collection includes rich clinical information for 38 million individuals from regional and national populations over two-to-three decades. The IUSM EHR collection is composed of Optum EHR derived from the Optum Clinformatics™ claim data and the Indiana EHR incorporated from the EMR data of Indiana Network for Patient Care (INPC) Research Database, Indiana University Health (IUH), and Eskenazi Health (EH). We propose to develop the DisEase PrOgression Trajectory (DEPOT), an evidence-driven, graph-based clinical informatics approach to model CKD progression trajectories and individualize clinical decision support. We hypothesize that there are different CKD progression paths which are: 1) driven by different pathogenic mechanisms, 2) susceptible to different nephrotoxic drugs, and 3) identified by unique EHR data patterns. Mathematically, such CKD trajectory landscapes can be learned as principle graphs representing the topological and temporal characteristics of the observed, fragmented EHR data. The goal of this work is to use the IUSM EHR data collection to 1) establish EHR-based CKD progression trajectories and 2) to learn actionable knowledge to prevent drug-induced AKI and CKD. The multi-specialty team proposes to: Aim 1. Construct CKD progression trajectories using graph artificial intelligence model and the IUSM EHR data and Aim 2) Identify nephrotoxic DDIs in the general population and trajectory-specific subpopulations that increase risks of AKI and CKD. The success of the proposed work will generate novel knowledge about the landscape of CKD health trajectories and nephrotoxic DDIs, bridging gaps between rich longitudinal EHR data and decision support for precision medicine in CKD. This work will shift paradigms of big data and complex disease research, enabling EHR data to become part of daily CKD management.

项目摘要 慢性肾脏疾病（CKD）是常见的，影响14.8%的美国成年人，在不同的国家和地区更不成比例。 和服务不足的社区。CKD显著降低了预期寿命和生活质量， 给社会带来巨大的经济负担。仍然迫切需要及早查明可改变的危险因素 在易感人群中，并为医疗决策提供可操作的支持。在可修改的 风险因素，药物诱导的急性肾损伤（AKI）有助于CKD的发展和进展。当前 肾毒性药物相互作用（DDI）知识不足以预防异质性患者的伤害 亚群电子医疗记录（EMR）和医疗保险中的电子健康记录（EHR） 索赔数据可以帮助预测不同的CKD进展轨迹并发现新的肾毒性药物 交互.印第安纳州大学医学院（IUSM）的EHR集合包括丰富的临床信息 在20到30年的时间里，为来自区域和国家人口的3800万人提供服务。IUSM EHR 该集合由来自Optum Clinformatics ™索赔数据的Optum EHR和印第安纳州EHR组成 从印第安纳州患者护理网络（INPC）研究数据库（印第安纳州）的EMR数据中合并 大学健康（IUH）和Eskenazi健康（EH）。我们建议开发疾病传播轨迹 （DEPOT），一种循证驱动、基于图表的临床信息学方法，用于模拟CKD进展轨迹 个性化临床决策支持。我们假设存在不同的CKD进展路径， 是：1）由不同的致病机制驱动，2）对不同的肾毒性药物敏感，以及3）识别 通过独特的EHR数据模式。在数学上，这样的CKD轨迹景观可以被学习为原则 表示所观察到的、碎片化的EHR数据的拓扑和时间特征的图。的 这项工作的目标是使用IUSM EHR数据收集1）建立基于EHR的CKD进展轨迹 学习预防药物性AKI和CKD的实用知识。多专业团队建议 目的：目标1。使用图形人工智能模型和IUSM EHR构建CKD进展轨迹 数据和目的2）确定一般人群和特定人群中的肾毒性DDI 增加AKI和CKD风险的亚群。拟议工作的成功将产生新的 了解CKD健康轨迹和肾毒性DDI的情况，弥合丰富的 纵向EHR数据和CKD精准医疗的决策支持。这项工作将改变大的范式 数据和复杂的疾病研究，使EHR数据成为日常CKD管理的一部分。