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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

基本信息

批准号：
10714792
负责人：
Jing Su
金额：
$ 33.61万
依托单位：
INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
依托单位国家：
美国
项目类别：
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/10714792
关键词：
Acute Acute Renal Failure with Renal Papillary Necrosis Address Adverse drug effect Adverse drug event Affect Alzheimer&apos s Disease Alzheimer&apos s disease diagnosis Alzheimer&apos s disease model Alzheimer&apos s disease patient Alzheimer&apos s disease related dementia Anti-Cholinergics Artificial Intelligence Behavior Big Data Brain imaging COVID-19 pandemic Caregivers Caring Cholinergic Agents Chronic Chronic Disease Chronic Kidney Failure Clinic Clinical Clinical Informatics Clinical Markers Cognitive Collection Communities Complex Confusion Conscious Data Data Collection Databases Deglutition Dementia Deterioration Development Disease Disease Progression Disease model Drug Combinations Drug Interactions Effectiveness Electronic Health Record Family Family member Funding General Population Genetic Genetic Markers Genotype Goals Graph Health Heterogeneity Impaired cognition Impairment Incidence Indiana Kidney Diseases Knowledge Knowledge acquisition Language Learning Life Style Liver diseases Medical Medicine Memory Memory Loss Modeling Neurodegenerative Disorders Parents Pathogenicity Patient Care Patients Pattern Personality Pharmaceutical Preparations Polypharmacy Predisposition Research Resources Risk Risk Factors Safety Management Social Environment Societies Socioeconomic Status System United States National Library of Medicine Universities Visuospatial Work clinical care clinical decision support clinical diagnosis cognitive function cognitive testing cohort comorbidity data modeling drug efficacy executive function health care availability health disparity indexing instrument medical specialties medication safety mild cognitive impairment molecular marker nephrotoxicity novel parent project patient safety personalized decision pharmacokinetics and pharmacodynamics phenome pill precision drugs precision medicine prevent social health determinants success

项目摘要

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.

项目摘要阿尔茨海默病（AD）和相关痴呆（ADRD）是异质性神经退行性疾病对患者、家庭、护理人员和社会都是毁灭性的。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。每个进展轨迹与特定的基因型、分子标志物特征、脑成像模式、风险因素以及药物管理和其他护理的需求。管理ADRD 由于疾病本身、医学合并症和健康的社会决定因素（SDoH）。具体而言，迫切需要管理药品安全，患者1）表现出不同的认知损害模式和认知下降率，2）疾病进展轨迹的不同阶段，3）证明不同的临床、分子和遗传标志物，4）具有不同的共病条件，以及5）显示与社会经济地位相关的健康差异以及获得医疗保健和其他社区资源。在最近由国家资助的Parent R01项目中，医学图书馆（R01LM013771），我们一直在开发DEPOT（疾病传播轨迹），可推广的临床信息学系统，以揭示复杂慢性疾病的异质性健康轨迹疾病，并确定药物相互作用（DDI）在一般人群和使用纵向电子健康记录（EHR）的慢性肾脏疾病患者的特定亚群 (CKD)作为疾病模型，急性肾脏疾病（阿基）作为药物不良事件。父项目是基于IUSM纵向EHR收集（队列规模：8200万），由Optum Clinformatics™索赔数据和印第安纳州患者护理网络（INPC）研究数据库。我们提出扩展母公司提案并开发定制的DEPOT系统，以满足精确度的迫切需求 ADRD患者的药物管理。我们假设有不同的ADRD进展途径，是：1）由不同的致病机制驱动，2）对不同的肾毒性药物和DDI敏感，以及 3)可通过纵向EHR数据识别。这项工作的目标是1）建立基于EHR的ADRD进展轨迹和2）学习可操作的知识，以防止药物相互作用诱导的阿基。多专业团队建议：目标1。使用图形人工智能模型建立ADRD进展轨迹，目标2。确定一种精确的医学方法，以防止DDI诱导的阿基， ADRD患者ADRD DEPOT模型的拟议开发成功将产生关于ADRD健康轨迹和肾毒性药物相互作用的新知识，纵向EHR数据和ADRD中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疾病研究，使EHR数据成为日常ADRD管理的一部分。