Leveraging Electronic Health Records and Genomic Biobanks for Kidney Stone Disease

利用电子健康记录和基因组生物库治疗肾结石疾病

• 批准号: 10395992
• 负责人: Cosmin Adrian Bejan
• 金额: $ 21.63万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10395992
• 关键词: Address; Adoption; Algorithms; Area; Bioinformatics; Clinical; Clinical Data; Collaborations; Coronary heart disease; DNA; Data; Data Set; Diabetes Mellitus; Disease; Disease susceptibility; Electronic Health Record; Emergency department visit; Event; Future; Genetic Variation; Genetic study; Genomic medicine; Genomics; Genotype; Goals; Gold; Hypertension; Investigation; Kidney Calculi; Link; Manuals; Methods; Mind; Natural Language Processing; Observational Study; Operative Surgical Procedures; Outcome; Patient-Focused Outcomes; Patients; Performance; Phenotype; Precision Health; Radiology Specialty; Recurrence; Reporting; Research; Research Design; Research Personnel; Risk Factors; Sample Size; Sampling; Scanning; Standardization; Structure; Symptoms; Testing; Text; Time; Training; Variant; Vision; Work; artificial neural network; base; biobank; case control; clinical care; clinical data repository; clinical decision support; clinical decision-making; clinical practice; clinically relevant; computerized; cost effective; deep learning; deep learning algorithm; design; disorder risk; electronic structure; genetic association; genetic variant; genome wide association study; genomic tools; knowledgebase; learning strategy; machine learning algorithm; novel; phenotypic data; radiological imaging; secondary analysis; support tools; text searching; tool; treatment response; unstructured data; web site

PROJECT SUMMARY Kidney stones are highly prevalent and recurrent. Our current understanding of kidney stone disease risk factors and disease associations has relied primarily on data from chart review, nonspecific administrative datasets, and secondary analyses of observation studies. Current study designs suffer from small sample sizes, heterogenous patient groups, and lack of standardized accuracy data and outcome definitions. The widespread adoption of electronic health records (EHRs) provides novel research opportunities for kidney stone disease. EHRs contain a robust clinical repository of data collected over time from clinical care. However, there are currently limited tools to identify and characterize kidney stone patients in the EHR. The objective of this study is to establish feasibility of utilizing EHR data to investigate kidney stone disease. To structure EHR data in an efficient and cost-effective manner, natural language processing and deep learning methods can be designed for identifying and phenotyping kidney stone patients and clinical outcomes. Our de-identified EHR is linked to a DNA biobank that can enable investigation of genetic associations with disease. This project has two specific aims. In Aim 1, we will perform genetic association studies in our EHR and linked DNA biobank. We will replicate previously described associations with genetic variants and kidney stone disease. We will then perform a genome-wide association study to discover novel associations. In Aim 2, our goal is to develop and validate a computable framework to extract clinical outcomes of kidney stone disease from the EHR. Clinically meaningful outcomes include symptomatic stone passage and radiographic stone characterization. We will develop and test natural language processing and deep learning algorithms to extract keywords and context-based information in clinical notes and reports. We will train and test these algorithms using manual annotation as the gold standard. This aim will enable rigorous phenotyping of each kidney stone patient using structured and unstructured EHR data. Successful completion of this project will lay the groundwork towards advancing genomic medicine and precision health to support clinical decision-making in kidney stone patients.

项目概要 肾结石非常普遍且复发。我们目前对肾结石疾病危险因素的了解 和疾病协会主要依赖于图表审查、非特定管理数据集和 观察研究的二次分析。目前的研究设计存在样本量小、异质性等问题 患者群体，缺乏标准化的准确性数据和结果定义。广泛采用 电子健康记录（EHR）为肾结石疾病提供了新的研究机会。电子病历包含 随着时间的推移从临床护理中收集的数据的强大临床存储库。但目前数量有限 在 EHR 中识别和表征肾结石患者的工具。本研究的目的是建立 利用 EHR 数据研究肾结石疾病的可行性。以高效且有效的方式构建 EHR 数据 可以设计经济有效的方式，自然语言处理和深度学习方法来识别 以及肾结石患者的表型和临床结果。我们的去识别化 EHR 与 DNA 生物库相关联 这可以使研究与疾病的遗传关联成为可能。该项目有两个具体目标。在目标 1 中， 我们将在我们的 EHR 和关联 DNA 生物库中进行遗传关联研究。我们将复制之前的内容 描述了与遗传变异和肾结石疾病的关联。然后我们将进行全基因组研究 关联研究以发现新的关联。在目标 2 中，我们的目标是开发和验证可计算的 从 EHR 中提取肾结石疾病临床结果的框架。具有临床意义的结果 包括有症状的结石通道和放射学结石特征。我们将开发和测试天然 语言处理和深度学习算法，用于提取临床中的关键词和基于上下文的信息 笔记和报告。我们将使用手动注释作为黄金标准来训练和测试这些算法。这 目标将利用结构化和非结构化 EHR 数据对每位肾结石患者进行严格的表型分析。 该项目的成功完成将为推进基因组医学和精准化奠定基础 健康支持肾结石患者的临床决策。

项目成果

Mendelian Randomization Analysis of Genetic Proxies of Thiazide Diuretics and the Reduction of Kidney Stone Risk.

• DOI: 10.1001/jamanetworkopen.2023.43290
• 发表时间: 2023-11-01
• 期刊: JAMA NETWORK OPEN
• 影响因子: 13.8
• 作者: Triozzi, Jefferson L.;Hsi, Ryan S.;Wang, Guanchao;Akwo, Elvis A.;Wheless, Lee;Chen, Hua-Chang;Tao, Ran;Ikizler, T. Alp;Robinson-Cohen, Cassianne;Hung, Adriana M.
• 通讯作者: Hung, Adriana M.

Evaluation of genetic associations with clinical phenotypes of kidney stone disease.

评估遗传与肾结石疾病临床表型的关联。

• DOI: 10.1101/2024.01.18.24301501
• 发表时间: 2024
• 期刊: medRxiv : the preprint server for health sciences
• 作者: Hsi,RyanS;Zhang,Siwei;Triozzi,JeffersonL;Hung,AdrianaM;Xu,Yaomin;Bejan,CosminA
• 通讯作者: Bejan,CosminA