Predicting Exacerbations of Asthma in Real-World Patients with Low Medical Utilization (PEARL)

预测现实世界中医疗利用率低的患者的哮喘恶化 (PEARL)

• 批准号: 10585179
• 负责人: Wansu Chen
• 金额: $ 77.38万
• 依托单位: KAISER FOUNDATION RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-21 至 2027-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10585179
• 关键词: Acute; Adrenal Cortex Hormones; Affect; Age; Air; Air Pollutants; American; Anxiety; Asthma; Belief; California; Caring; Categories; Chest; Chronic; Classification; Clinical; Complex; Comprehensive Health Care; Consultations; Coughing; Crime; Data; Data Sources; Disease Management; Economic Burden; Electronic Health Record; Emergency Situation; Ethnic Population; Etiology; Frequencies; Future; Guidelines; Health Surveys; Heterogeneity; Impairment; Inflammatory; Integrated Health Care Systems; Intervention; Joints; Knowledge; Machine Learning; Measures; Medical; Medical center; Medicine; Mental Depression; Methods; Modeling; Neighborhoods; Oral; Patients; Pharmaceutical Preparations; Phenotype; Physicians; Population; Prevalence; Productivity; Prospective cohort; Provider; Publishing; Pulmonary function tests; Quality of Care; Reporting; Research; Retrospective cohort study; Risk; Risk Assessment; Risk Factors; Severities; Shortness of Breath; Specialist; Surveys; Symptoms; Terminology; Time; Translations; Wheezing; Work; asthma exacerbation; asthma inhaler; asthmatic patient; burden of illness; community-level factor; deep learning; ethnic diversity; evidence base; health care service; health care service organization; health plan; high dimensionality; high risk; high risk population; improved; individual patient; insight; model development; multi-ethnic; patient stratification; personalized approach; personalized care; personalized management; personalized risk prediction; predictive modeling; predictive tools; prospective; pulmonary function; racial diversity; racial population; risk prediction; risk prediction model; sex; social health determinants; treatment planning; violent crime

PROJECT SUMMARY Asthma is a chronic inflammatory condition that affects > 20 million Americans. The prevalence of asthma has been increasing since the early 1980s in all age, sex, and racial groups. There is no universal method for de- termining asthma severity. The terminology and the definition used in various asthma guidelines have evolved over time. Most commonly, asthma severity is determined by clinical parameters such as medica- tion use, presence and/or frequency of asthma symptoms, number of asthma exacerbations (which are acute or subacute episodes of progressively worsening shortness of breath, cough, wheezing, and chest tight- ness or some combination of these symptoms), and/or the results of lung function tests. Patients with persis- tent asthma are at elevated risk for exacerbations (attack) and often have decreased lung function. Yet the bur- den of intermittent asthma is also significant: It affects 50-75% of all asthma patients and represents 30-40% of total asthma exacerbations requiring emergency consultation. Risk factors for asthma exacerbations have been studied in patients with persistent asthma. However, little is known about risk factors in patients with in- termittent asthma, nor have risk prediction models been reported. A focused study on risk factor identification and future risk prediction will provide valuable insights into the etiology of asthma exacerbations in intermittent asthma patients and facilitate a personalized approach in the management of the disease. Without a clear un- derstanding of the risk of asthma exacerbation for each individual patient with intermittent asthma, we will not be able to optimally define the most appropriate intervention strategies to reduce the burden of the disease in this group of patients. To operationalize the clinical definition of intermittent asthma, we will focus on a pheno- typic group of low utilizers referred to in guidelines as intermittent asthma. We propose to identify potential risk factors for asthma exacerbation in low utilizers using high-dimensional and longitudinal KPSC EHR and exter- nal data sources (including air quality measures, social determinants of health and violent crime), subsequently develop and validate risk prediction models to stratify patients into low- and high-risk groups, and externally validate the risk prediction model using EHR data of another large health care organization. We also propose to establish a prospective cohort of low utilizers and collect patient-reported information (PRI) via a survey. The PRI will help characterize the patients of low utilizers in terms of asthma symptoms, activities, impairment and risk assessment, work productivity, beliefs about medicines and anxiety/depression scales. We will develop and internally validate a risk prediction model based on both EHR and PRI data. The proposed models will al- low physicians to provide personalized care (e.g., develop or adjust treatment plans, provide personal asthma action plans accordingly, and refer patients to asthma specialists when necessary) and thus improve the qual- ity of care and reduce asthma burden. Our proposal to examine heterogeneity across different racial/ethnic groups has the potential to inform practice for more accurate asthma risk assessment.

项目摘要 哮喘是一种慢性炎症性疾病，影响超过2000万美国人。哮喘的发病率 自20世纪80年代初以来，在所有年龄、性别和种族群体中，这一数字一直在增加。没有一个通用的方法可以... 确定哮喘的严重程度各种哮喘指南中使用的术语和定义 随着时间的推移而进化。最常见的是，哮喘的严重程度是由临床参数，如药物， 哮喘症状的存在和/或频率、哮喘急性发作的次数（其是 急性或亚急性发作的呼吸短促、咳嗽、喘息和胸闷进行性恶化- 或这些症状的某种组合）和/或肺功能测试的结果。患者持续- 帐篷型哮喘恶化（发作）风险升高，且通常具有降低的肺功能。但是， 间歇性哮喘的发病率也很高：它影响了50 - 75%的哮喘患者，占30 - 40%的 需要紧急会诊的哮喘急性发作总数。哮喘急性发作的危险因素有 在持续性哮喘患者中进行了研究。然而，对患者的危险因素知之甚少- 间歇性哮喘，也没有风险预测模型的报告。风险因素识别的重点研究 未来的风险预测将为间歇性哮喘发作的病因学提供有价值的见解， 哮喘患者，并促进疾病管理的个性化方法。如果没有明确的联合国- 了解每个间歇性哮喘患者的哮喘急性发作风险，我们不会 能够以最佳方式确定最适当的干预战略，以减少疾病的负担， 这群病人。为了使间歇性哮喘的临床定义具有可操作性，我们将重点关注一种表型- 在指南中称为间歇性哮喘的低利用率的典型群体。我们建议识别潜在风险 使用高维和纵向KPSC EHR和埃克斯特评估低利用者哮喘加重的因素。 最终数据来源（包括空气质量指标、健康的社会决定因素和暴力犯罪）， 开发和验证风险预测模型，将患者分为低风险组和高风险组， 使用另一家大型医疗机构的EHR数据验证风险预测模型。我们亦建议 建立一个低利用率的前瞻性队列，并通过调查收集患者报告信息（PRI）。的 PRI将有助于从哮喘症状、活动、损害和 风险评估，工作效率，对药物的信念和焦虑/抑郁量表。我们将开发 并在内部验证基于EHR和PRI数据的风险预测模型。所提出的模型将- 低医生提供个性化护理（例如，制定或调整治疗计划，提供个人哮喘 相应的行动计划，并在必要时将患者转介给哮喘专家），从而提高质量， 减轻哮喘负担。我们建议检查不同种族/民族的异质性 小组有可能为更准确的哮喘风险评估提供信息。