Precision Approaches to Reduce Asthma Disparities with Electronic Health Record Data

利用电子健康记录数据减少哮喘差异的精确方法

• 批准号: 10689250
• 负责人: Blanca E Himes
• 金额: $ 79.67万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10689250
• 关键词: Accounting; Address; Adult; Adult asthma; Affect; Air Pollution; Algorithms; American; Area; Asthma; Black race; COVID-19 pandemic; Caring; Cessation of life; Characteristics; Child; Chronic Disease; Chronic lung disease; Clinical; Community Surveys; Complex; Data; Data Element; Data Sources; Disparity; Economic Factors; Economics; Electronic Health Record; Emergency Situation; Environmental Exposure; Environmental Risk Factor; Ethnic Origin; Geographic Distribution; Geographic Locations; Goals; Health system; Hypersensitivity; Income; Individual; Insurance Coverage; Intervention; Life; Link; Manuals; Measurement; Medicine; Methods; Modeling; Morbidity - disease rate; Natural Language Processing; Occupational; Occupations; Outcome; Outpatients; Patient Care; Patients; Pattern; Persons; Phenotype; Population; Prevalence; Public Health; Race; Records; Risk; Risk Factors; Severities; Socioeconomic Status; Source; Stimulus; Subgroup; Surveys; Symptoms; Techniques; Telephone; Time; United States Environmental Protection Agency; Visit; Vulnerable Populations; Woman; airway hyperresponsiveness; airway obstruction; asthma exacerbation; asthmatic patient; clinical decision support; clinically significant; cost; data integration; design; electronic health information; high risk; improved; individual patient; novel; pandemic impact; personalized approach; personalized care; point of care; population health; post-pandemic; pre-pandemic; prototype; sex; social; social factors; social health determinants; software development; support tools; therapy design; tool

Asthma, a chronic disease that manifests as airway hyperresponsiveness to specific environmental stimuli, affects over 20 million American adults. Disparities in adult asthma prevalence, severity, and death in the U.S. are well known but few approaches have significantly decreased them. Studies of real-world populations such as those derived from Electronic Health Records (EHRs) are invaluable to guide the design of personalized care strategies because they capture a large number of diverse and vulnerable people. Additionally, EHR data can be leveraged to identify geospatial areas where people are at peak risk of a condition by studying the geographic distribution of affected patients. We have identified individual- and area-level factors that are associated with asthma using EHRs linked to rich and diverse sources of social, economic, and environmental variables, and we have developed methods to appropriately extract information from EHR data that are heterogeneously available across patients and reduce bias in the analysis of these imperfect data. This proposal will identify sub-groups of adults with asthma who share common patterns of demographic, clinical, social, and environmental exposure characteristics using EHR data augmented with data on social, economic, and environmental factors, which will enable the design of effective precision strategies to reduce asthma exacerbations. Our aims are to: 1) develop and validate natural language processing (NLP) algorithms to extract social, occupational and allergy information from EHR notes; 2) determine geospatial areas that have increased asthma exacerbation risk using spatial generalized linear mixed models and identify risk factors in these areas; and 3) use Bayesian hierarchical clustering techniques to identify asthma sub-phenotypes that will form the basis of a clinical decision support tool that offers precision care strategies. This project will result in the creation of a tool that can assist in the care of adults with asthma based on actionable risk factors and motivate public health strategies to mitigate the burden of asthma in specific regions. Our novel data integration approaches, sub-phenotyping methods, and software developed will have broad applicability for the study of any condition using EHR or other real-world data.

哮喘是一种慢性疾病，表现为气道对特定环境刺激的高反应性， 影响超过 2000 万美国成年人。美国成人哮喘患病率、严重程度和死亡率的差异 众所周知，但很少有方法能够显着减少它们。对现实世界人口的研究，例如 因为源自电子健康记录 (EHR) 的信息对于指导个性化护理的设计非常宝贵 战略，因为它们吸引了大量多样化和弱势群体。此外，EHR 数据可以 通过研究地理空间来确定人们处于疾病最高风险的地理空间区域 受影响患者的分布。我们已经确定了与以下因素相关的个人和地区层面的因素： 使用电子病历的哮喘与丰富多样的社会、经济和环境变量来源相关，我们 开发了从异构可用的 EHR 数据中适当提取信息的方法 跨患者并减少分析这些不完美数据时的偏差。该提案将确定以下小组： 具有人口统计学、临床、社会和环境暴露共同模式的哮喘成年患者 使用 EHR 数据并添加社会、经济和环境因素数据的特征，这将 能够设计有效的精确策略来减少哮喘恶化。我们的目标是：1）开发 并验证自然语言处理 (NLP) 算法以提取社交、职业和过敏信息 来自电子病历笔记； 2) 使用空间确定哮喘恶化风险增加的地理空间区域 广义线性混合模型并识别这些领域的风险因素； 3）使用贝叶斯分层 用于识别哮喘亚表型的聚类技术，这将构成临床决策支持工具的基础 提供精准护理策略。该项目将创建一个可以帮助护理的工具 根据可操作的危险因素，制定患有哮喘的成年人，并采取公共卫生策略来减轻哮喘的负担 特定区域的哮喘。我们新颖的数据集成方法、亚表型分析方法和软件 所开发的技术将具有广泛的适用性，可用于使用 EHR 或其他现实世界数据研究任何状况。

项目成果

Benefits of Aeroallergen Testing on Oral Corticosteroid Bursts in Adults with Asthma.

气源性过敏原测试对成人哮喘患者口服皮质类固醇爆发的好处。

• DOI: 10.1101/2024.01.29.24301962
• 发表时间: 2024
• 期刊: medRxiv : the preprint server for health sciences
• 作者: Gleeson,PatrickK;Morales,KnashawnH;Buckey,TimothyM;Fadugba,OlajumokeO;Apter,AndreaJ;Christie,JasonD;Himes,BlancaE
• 通讯作者: Himes,BlancaE

Linking Ambient NO2 Pollution Measures with Electronic Health Record Data to Study Asthma Exacerbations.

将环境二氧化氮污染测量与电子健康记录数据联系起来研究哮喘加重。

• 发表时间: 2023
• 期刊: AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
• 作者: Schreibman,Alana;Xie,Sherrie;Hubbard,RebeccaA;Himes,BlancaE
• 通讯作者: Himes,BlancaE