Revealing Health Trajectories of Chronic Kidney Disease for Precision Medicine

项目总结 阿尔茨海默病(AD)和相关痴呆(ADRD)是一种异质性神经退行性疾病 对患者、家庭、照顾者和社会来说都是毁灭性的。患者表现出各种进行性下降 不同认知领域的模式，如记忆、语言、执行功能、视觉空间功能、 个性和行为。每个进展轨迹都与特定的基因类型、分子标记有关 在药物管理和其他护理方面的特征、脑成像模式、风险因素和需求。管理ADRD 是具有挑战性的，因为疾病本身的异质性和复杂性，医学上的并存，以及 健康的社会决定因素(SDoH)。具体地说，迫切需要对药品安全进行管理 患者1)表现出不同的认知障碍模式和认知衰退率，2) 疾病发展轨迹的不同阶段，3)表现出不同的临床、分子和遗传学 标记物，4)不同的共病条件，5)与社会经济地位有关的健康差距 以及获得医疗保健和其他社区资源。在最近由National资助的父R01项目中 医学图书馆(R01LM013771)，我们一直在开发Depot(疾病进展轨迹)，a 用于揭示复杂慢性疾病异质性健康轨迹的通用临床信息学系统 并确定药物-药物相互作用在普通人群和 使用纵向电子健康记录(EHR)的轨迹特定亚群，慢性肾脏疾病 以慢性肾脏病(CKD)为疾病模型，以急性肾脏病(AKI)为药物不良事件。父项目是 基于IUSM纵向EHR集合(队列规模：8200万)，该集合由Optom组成 临床信息学™索赔数据和印第安纳州患者护理网络研究数据库。我们建议 扩展父提案并开发量身定制的仓库系统，以满足精度方面的迫切需求 ADRD患者的药物管理。我们假设存在不同的ADRD级数路径 它们是：1)由不同的致病机制驱动，2)对不同的肾毒性药物和DDiS易感，以及 3)可由纵向EHR数据识别。这项工作的目标是1)建立基于电子病历的ADRD进展 2)学习可操作的知识以预防药物相互作用引起的AKI。多专业团队 提出：目标1。利用图形化人工智能模型建立ADRD行进轨迹。 以一种特殊的意识确定一种预防DDI所致AKI的精确医学方法 ADRD患者。拟议的开发ADRD车辆段模式的成功将产生 关于ADRD健康轨迹和肾毒性药物相互作用的新知识，弥合了RICH之间的差距 ADRD纵向电子病历数据与精准医疗决策支持。这项工作将改变BIG的范式 数据和复杂疾病研究，使电子病历数据成为日常ADRD管理的一部分。

Project summary Alzheimer’s disease (AD) and related dementias (ADRD) are heterogeneous neurodegenerative disease devastating for patients, families, caregivers, and society. Patients demonstrate various progressive decline patterns in different cognitive domains such as memory, language, executive function, visuospatial function, personality and behaviors. Each progression trajectory is associated with specific genotypes, molecular marker features, brain imaging patterns, risk factors, and needs in drug management and other cares. Managing ADRD is challenging due to the heterogeneity and complexity in the disease itself, in the medical comorbidities, and in social determinants of health (SDoH). Specifically, there is an urgent need in managing medicine safety for patients who are 1) demonstrating different cognitive impairment patterns and cognitive declining rates, 2) at different stages of the disease progression trajectories, 3) demonstrating different clinical, molecular, and genetic markers, 4) with different comorbid conditions, and 5) showing health disparity related with socioeconomic status and access to healthcare and other community resources. In the Parent R01 project recently funded by National Library of Medicine (R01LM013771), we have been developing DEPOT (DisEase PrOgression Trajectory), a generalizable clinical informatics system to reveal the heterogeneous health trajectories of complex chronic diseases and identify adverse effects of drug-drug interactions (DDIs) in both the general population and trajectory-specific subpopulations using longitudinal electronic health records (EHR), with chronic kidney disease (CKD) as the disease model and acute kidney disease (AKI) as the drug adverse event. The Parent Project is based on the IUSM longitudinal EHR collection (cohort size: 82million), which is composed of the Optum Clinformatics™ claim data and the Indiana Network for Patient Care (INPC) Research Database. We propose to extend the Parent Proposal and develop a tailored DEPOT system to address the urgent need in precision drug management for ADRD patients. We hypothesize that there are different ADRD progression paths which are: 1) driven by different pathogenic mechanisms, 2) susceptible to different nephrotoxic drugs and DDIs, and 3) identifiable by longitudinal EHR data. The goal of this work is to 1) establish EHR-based ADRD progression trajectories and 2) learn actionable knowledge to prevent drug interaction induced AKI. The multi-specialty team proposes to: Aim 1. Establish ADRD progression trajectories using graph artificial intelligence model, Aim 2. Identify a precision medicine approach to protect against DDI-induced AKI with a special conscious on patients with ADRD. The success of the proposed development of the DEPOT model for ADRD will generate novel knowledge about ADRD health trajectories and nephrotoxic drug interactions, bridging gaps between rich longitudinal EHR data and decision support for precision medicine in ADRD. This work will shift paradigms of big data and complex disease research, enabling EHR data to become part of daily ADRD management.