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

基本信息

批准号：
10540025
负责人：
Blanca E Himes
金额：
$ 81.23万
依托单位：
UNIVERSITY OF PENNSYLVANIA
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：
2022-09-01 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10540025
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 Analyses Data Element Data Sources Demographic Accounting Economic Factors Economics Electronic Health Record Emergency Situation Environmental Exposure Environmental Risk Factor Ethnic Origin Geographic Distribution Geographic Locations Goals Gold Health Occupations Health system Hypersensitivity Income Individual Insurance Coverage Intervention Life Link Manuals Measurement Medicine Methods Modeling Morbidity - disease rate Natural Language Processing Occupational 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 base clinical decision support clinically significant cost data integration design high risk improved individual patient novel pandemic disease personalized approach personalized care point of care population health prototype sex social social factors social health determinants software development support tools therapy design tool

项目摘要

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.

哮喘是一种表现为对特定环境刺激的气道高反应性的慢性疾病，影响超过2000万美国成年人。美国成人哮喘患病率，严重程度和死亡的差异众所周知，但很少有方法大大降低了它们。对现实世界的研究因为从电子健康记录（EHR）衍生出的那些非常宝贵，可以指导个性化护理的设计策略是因为它们捕捉了大量不同和脆弱的人。此外，EHR数据可以通过研究地理位置患者的分布。我们已经确定了与使用EHR与社会，经济和环境变量的多样来源相关的哮喘，我们已经开发了适当从EHR数据中提取信息的方法跨患者并减少对这些不完美数据的分析。该建议将确定患有哮喘的成年人共享人口，临床，社会和环境暴露的共同模式使用EHR数据增加了有关社会，经济和环境因素的数据的特征，这将启用有效的精确策略的设计，以减少哮喘加重。我们的目标是：1）发展并验证自然语言处理（NLP）算法以提取社会，职业和过敏信息摘自EHR笔记； 2）确定使用空间增加哮喘加重风险的地理空间区域普遍的线性混合模型并确定这些领域的风险因素； 3）使用贝叶斯分层聚类技术以识别哮喘子表格型，这些型将构成临床决策支持工具的基础提供精确的护理策略。该项目将导致创建一个可以帮助照顾的工具患有哮喘的成年人基于可行的风险因素，并激励公共卫生策略减轻负担特定地区的哮喘。我们的新型数据集成方法，子表型方法和软件开发的使用EHR或其他现实世界数据将对任何条件的研究都有广泛的适用性。