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(申请人提供)：哮喘是儿童中最常见的慢性疾病，也是美国五大负担最重的疾病之一。尽管如此，儿童哮喘的流行病学调查受到不同地点哮喘诊断的差异以及电子病历(EMR)的低效利用以促进大规模研究的限制。基于结构化数据(如ICD-9代码)的算法已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。手动EMR审查允许应用公认的基于标准的定义，如哮喘预测指数(API)或预先确定的哮喘标准(PAC)，但这是劳动密集型和昂贵的，因此不适用于人口水平的研究。由于缺乏一致的、可重复的和有效的哮喘确定方法，使用不一致的哮喘标准、B.确定过程和C.采样框架会导致临床试验或其他研究的哮喘队列和研究结果不一致。这种不一致会导致混淆，延迟将重要的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一个强大的软件工具来简化基于哮喘标准(PAC和API)的哮喘自动病历确定过程，从而推动哮喘的研究和临床护理。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，以说明非结构化文本。因此，这项提议的主要目标是开发NLP-API，一种用于自动化API的NLP算法，并应用PAC和API的NLP算法来识别哮喘儿童队列。此外，我们将使用这些工具来确定患有哮喘的儿童的特征，从而证明其在流行病学调查以及可能在哮喘管理中的作用。我们假设，将基于哮喘标准的NLP算法应用于EMR将使我们能够准确、一致和高效地识别和描述哮喘状态。在目标1中，我们将开发一个针对API的NLP算法NLP-API。在目标2中，我们将同时将NLP-API(在AIM 1下开发)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系来表征目标2中确定的哮喘儿童的亚组。拟议研究的预期结果是：(I)通过实现更一致、可重复和更有效的大规模哮喘确定、采样框架和时间估计，增强哮喘的研究能力；(Ii)通过临床决策支持系统，为改进哮喘的及时诊断和护理奠定基础；以及(Iii)促进临床研究中NLP技术的使用。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，并为推广到其他慢性病和成人提供一个框架。