Precision Approaches to Reduce Asthma Disparities with Electronic Health Record Data

哮喘是一种慢性疾病，表现为对特定环境刺激的呼吸道高反应， 影响了2000多万美国成年人。美国成人哮喘患病率、严重程度和死亡率的差异。 这些都是众所周知的，但很少有方法能显著降低它们。对现实世界人口的研究，如 因为那些来自电子健康记录(EHR)的记录对于指导个性化护理的设计是无价的 战略，因为它们抓住了大量不同和脆弱的人。此外，电子病历数据可以 通过研究地理位置来识别人们处于疾病风险高峰期的地理空间区域 受影响患者的分布情况。我们已经确定了个人和地区层面的因素，这些因素与 哮喘使用的EHR与丰富和多样化的社会、经济和环境变量来源有关，而我们 我已经开发了从EHR数据中适当地提取信息的方法，这些信息是异质可用的 并在分析这些不完美的数据时减少偏差。这项提案将确定以下小组 具有共同的人口统计、临床、社会和环境暴露模式的成人哮喘患者 使用电子健康记录数据并加上社会、经济和环境因素数据的特征，这将 能够设计有效的精准战略，以减少哮喘的恶化。我们的目标是：1)发展 并验证自然语言处理(NLP)算法以提取社交、职业和过敏信息 从EHR记录中；2)确定具有增加哮喘恶化风险的地理空间区域使用空间 广义线性混合模型和识别这些领域的风险因素；以及3)使用贝叶斯分层 用于识别哮喘亚型的聚类技术将构成临床决策支持工具的基础 这提供了精准的护理策略。该项目将导致创建一个工具，可以帮助护理 根据可操作的危险因素和激励公共卫生战略来减轻成人哮喘患者的负担 特定地区的哮喘。我们的新数据集成方法、子表型方法和软件 开发的将具有广泛的适用性，以研究任何情况下使用电子健康记录或其他真实世界的数据。

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.