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）很常见，影响14.8％的美国成年人，而潜水 和服务不足的社区。 CKD大大降低了预期寿命和生活质量，同时强加了 社会上的巨大经济焚烧。至关重要的需求持续到早期确定可修改的风险因素 在易感人群中，并为医疗决策建立可行的支持。在可修改之中 危险因素，药物诱导的急性肾脏损伤（AKI）有助于CKD的发展和进展。电流 肾毒性药物相互作用（DDI）的知识不足以防止异源患者伤害 亚群。电子医疗记录（EMR）和健康保险的电子健康记录（EHRS） 索赔数据可以帮助预测不同的CKD进展轨迹并发现新型的肾毒性药物 互动。印第安纳大学医学院（IUSM）EHR系列包括丰富的临床信息 在两到三十年中，有3800万人来自地区和国家人口的人。 IUSM EHR 收集由源自optum clinformatics™索赔数据和印第安纳州EHR的optum EHR组成 印第安纳州印第安纳州患者护理网络（INPC）研究数据库的EMR数据合并 大学卫生（IUH）和埃斯凯纳济卫生（EH）。我们建议发展疾病进展轨迹 （仓库），一种循证驱动的基于图的临床信息，以模拟CKD进展轨迹 并个性化临床决策支持。我们假设存在不同的CKD进程路径 是：1）受不同致病机制驱动，2）易受不同的肾毒性药物； 3）鉴定 通过唯一的EHR数据模式。从数学上讲，可以将这种CKD轨迹景观作为原理学习 代表观察到的，零散的EHR数据的拓扑和临时特征的图。 这项工作的目标是使用IUSM EHR数据收集到1）建立基于EHR的CKD进程轨迹 2）学习可行的知识，以防止药物引起的AKI和CKD。多专科团队的建议 TO：AIM 1。使用图形人工智能模型和IUSM EHR构造CKD进程轨迹 数据和目标2）确定普通人群和特定轨迹的肾毒性DDI 增加AKI和CKD风险的亚群。拟议作品的成功将产生小说 有关CKD健康轨迹和肾毒性DDI的景观的知识 CKD中精确医学的纵向EHR数据和决策支持。这项工作将改变大的范式 数据和复杂的疾病研究，使EHR数据成为每日CKD管理的一部分。