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

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.

Grant Number: 5R01HL163049-04
NIH Institute/Center: NIH
Principal Investigator: Wansu Chen