Asthma ascertainment and characterization through electronic health records

通过电子健康记录确定和表征哮喘

• 批准号: 8853379
• 负责人: YOUNG J JUHN
• 金额: $ 38.89万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-01 至 2018-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8853379
• 关键词: Accounting; Address; Adult; Algorithms; Asthma; Attention; Biological Markers; Birth; Caring; Child; Childhood; Childhood Asthma; Chronic; Chronic Disease; Clinic; Clinical; Clinical Data; Clinical Decision Support Systems; Clinical Research; Clinical Trials; Code; Cohort Studies; Computerized Medical Record; Computers; Confusion; Data; Data Set; Databases; Development; Diagnosis; Diagnostic; Disease; Electronic Health Record; Epidemiologic Studies; Epidemiology; Evaluation; Future; Genetic study; Goals; Gold; Heterogeneity; ICD-9; Information Retrieval; International Classification of Disease Codes; Investigation; Laboratories; Manuals; Medical Records; Methods; Morbidity - disease rate; Natural Language Processing; Outcome; Outcome Study; Patient Care; Patients; Play; Population; Process; Records; Reproducibility; Research; Research Personnel; Respiratory physiology; Risk; Role; Sampling; Sensitivity and Specificity; Site; Societies; Software Tools; Solutions; Specific qualifier value; Specificity; Structure; Subgroup; Symptoms; System; Techniques; Testing; Text; Time; Translational Research; Translations; United States; Variant; Work; base; clinical care; clinical practice; cohort; cost; evidence based guidelines; genome wide association study; improved; indexing; meetings; outcome forecast; population based; public health relevance; tool

 DESCRIPTION (provided by applicant): Asthma is the most common chronic condition in children and one of the five most burdensome disease in the United States. Despite this, epidemiologic investigations into childhood asthma are limited by variations in asthma diagnosis across sites and inefficient utilization of electronic medical records (EMRs) to facilitate large- scale studies. Algorithms based on structured data (e.g., ICD-9 codes) have shown strong specificity, but lack the sensitivity required for population-based studies for asthma. Manual EMR reviews allow application of well- recognized criteria-based definitions such as the Asthma Predictive Index (API) or the Predetermined Asthma Criteria (PAC), but are labor-intensive and expensive, and therefore not feasible for population-level studies. Because of the lack of consistent, reproducible, and efficient asthma ascertainment methods, the use of inconsistent a. asthma criteria, b. ascertainment processes, and c. sampling frames results in inconsistent asthma cohorts and study results for clinical trials or other studies. This inconsistency causes confusion, delayed translation of important study findings into clinical practice, and may obscure the true heterogeneity of asthma. Our long-term goal is to advance research and clinical care for asthma, by developing a robust software tool to streamline the process of automatic medical record ascertainment of asthma based on the asthma criteria (PAC and API). We propose to augment traditional structured data criteria with natural language processing (NLP) techniques to account for unstructured text. Thus, the main goal of this proposal is to develop NLP-API, an NLP algorithm for automating API, and apply the NLP algorithms for both PAC and API to identify a cohort of children with asthma. In addition, we will use the tools to characterize children with asthma thereby demonstrating its usefulness in epidemiological investigations and also possibly in asthma management. We hypothesize that asthma criteria-based NLP algorithms applied to the EMR will allow us to identify and characterize asthma status accurately, consistently, and efficiently. In Aim 1, we will develop NLP-API, an NLP algorithm for API. In Aim 2, we will apply both NLP-API (developed under Aim 1) and NLP-PAC (our recently developed PAC-based NLP algorithm) to two evaluation cohorts. In Aim 3, we will characterize the subgroups of children with asthma identified under Aim 2 by assessing the association of NLP-ascertained asthma status with lung function and biomarkers for asthma. The expected outcomes of the proposed study are: (i) enhanced research capabilities for asthma by enabling more consistent, reproducible, and efficient large-scale asthma ascertainment, sampling frames, and timing estimations; (ii) a basis for improving timely asthma diagnosis and care through clinical decision support systems; and (iii) advancement of the use of NLP techniques for clinical studies. Successful completion of this project will provide an accurate, consistent, and efficient tool for addressing the significant burden of asthma in children and a framework for extension to other chronic diseases and adults.

 描述（由申请人提供）：哮喘是儿童中最常见的慢性疾病，也是美国五种最令人负担的疾病之一，尽管如此，对儿童哮喘的流行病学调查仍因不同地点的哮喘诊断差异和低效利用而受到限制。基于结构化数据（例如 ICD-9 代码）的电子病历（EMR）显示出很强的特异性，但缺乏基于人群的哮喘研究所需的敏感性。 EMR 审查允许应用公认的基于标准的定义，例如哮喘预测指数 (API) 或预定哮喘标准 (PAC)，但它是劳动密集型且昂贵的，因此对于人群水平的研究不可行。缺乏一致、可重复和有效的哮喘确定方法，使用不一致的哮喘标准，b. 导致临床试验的哮喘队列和研究结果不一致。这种不一致会导致混乱，延迟将重要研究结果转化为临床实践，并可能掩盖哮喘的真正异质性，我们的长期目标是通过开发强大的软件工具来简化哮喘的研究和临床护理。基于哮喘标准（PAC 和 API）的哮喘病历自动确定过程，我们建议使用自然语言处理（NLP）技术来增强传统的结构化数据标准，以解释非结构化文本。是为了发展NLP-API，一种用于自动化 API 的 NLP 算法，并将 NLP 算法应用于 PAC 和 API 来识别哮喘儿童队列。此外，我们将使用这些工具来描述哮喘儿童的特征，从而证明其在流行病学调查中的有用性。我们努力将基于哮喘标准的 NLP 算法应用于 EMR，以便我们能够准确、一致且高效地识别和表征哮喘状态。在目标 1 中，我们将开发 NLP-API，这是一种 NLP 算法。为了在目标 2 中，我们将 NLP-API（根据目标 1 开发）和 NLP-PAC（我们最近开发的基于 PAC 的 NLP 算法）应用于两个评估队列。在目标 3 中，我们将描述患有以下疾病的儿童亚组的特征。通过评估 NLP 确定的哮喘状态与肺功能和哮喘生物标志物的关联来确定目标 2 下的哮喘。拟议研究的预期结果是：（i）通过实现更一致的、增强的哮喘研究能力。可重复且高效的大规模哮喘确定、采样框架和时间估计；(ii) 通过临床决策支持系统改进及时哮喘诊断和护理的基础；以及 (iii) 推进 NLP 技术在临床研究中的应用。该项目的成功完成将为解决儿童哮喘的重大负担提供一个准确、一致和有效的工具，并为扩展到其他慢性病和成人提供一个框架。