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)

基本信息

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
项目类别：
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

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

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项目摘要

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状况，影响> 2000万美国人。哮喘的患病率自1980年代初以来，所有年龄，性别和种族群体都在增加。没有普遍的方法终止哮喘严重程度。各种哮喘指南中使用的术语和定义具有随着时间的流逝而发展。最常见的是，哮喘的严重程度取决于临床参数，例如Medica- 哮喘症状的使用，存在和/或频率，哮喘病毒的数量（即急性或亚急性发作的逐渐恶化，呼吸急促，咳嗽，喘息和胸部紧绷 - NESS或这些症状的某种组合）和/或肺功能测试的结果。 persis的患者帐篷哮喘患加重（攻击）的风险较高，并且肺功能降低。但是间歇性哮喘的DEN也很重要：它影响所有哮喘患者的50-75％，占30-40％需要紧急咨询的总哮喘加重。哮喘恶化的危险因素在患有持续性哮喘的患者中进行了研究。但是，关于IN-患者的危险因素知之甚少现成的哮喘，也没有报告风险预测模型。关于危险因素识别的重点研究未来的风险预测将为间歇性哮喘恶化的病因提供宝贵的见解哮喘患者并促进了该疾病管理的个性化方法。没有明确的对每位间歇性哮喘患者哮喘加重风险的说明，我们不会能够最佳地定义最合适的干预策略，以减轻疾病负担这组患者。为了实现间歇性哮喘的临床定义，我们将重点放在一个现场上指南中提到的低利用者典型群体是间歇性哮喘。我们建议确定潜在风险使用高维和纵向KPSC EHR和外部的低利用率哮喘加重因素随后开发和验证风险预测模型以将患者分层为低风险组，并在外部使用另一个大型医疗保健组织的EHR数据验证风险预测模型。我们也建议通过调查建立一个低利用者的前瞻性队列并收集患者报告的信息（PRI）。这 PRI将在哮喘症状，活动，障碍和风险评估，工作生产力，对药物和焦虑/抑郁量表的信念。我们将发展并在内部验证基于EHR和PRI数据的风险预测模型。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，并在必要时将患者推荐给哮喘专家），从而提高质量照顾和减轻哮喘负担。我们的提议检查不同种族/种族的异质性小组有可能为实践提供信息，以进行更准确的哮喘风险评估。