Asthma is a common disease of the lungs and airways that can take many forms and patients can have several different symptoms, such as wheeze, chest tightness, breathlessness and cough. Different subgroups of the disease, called phenotypes, have been distinguished by gathering asthma patients with the same features together using a small number of primary care patients. Patients with an asthma phenotype share a collection of several features. Examples of these features are mild asthma, allergies, obesity, and cell counts of a type of white blood cell in blood tests. However, it is not known if these asthma phenotypes exist in the real world as less than 10% of asthma patients are eligible for the test setting where these phenotypes were first studied. In this study, we aim to study the features, medication, other illnesses and frequency of asthma attacks of people with different asthma phenotypes using data from a sizeable amount of primary care patients. This work is clinically important as the best asthma treatment may differ between phenotypes.
Clinical phenotypes of asthma in primary care have been established using cluster analysis by Haldar et al. in a small primary care trial population. It is not known whether these phenotypes exists in a large cohort of primary care patients. The objective of this study is to classify the asthma patients into predefined phenotypes, and assess their clinical profile, medication use, comorbidities and health-care resource utilization and enumerate their rate of asthma exacerbations during follow-up and compare with existing data from clinical cohorts. In order to do this, we will assign asthma patients in CPRD GOLD a phenotype; the pre-defined phenotypes in primary care are 'Benign asthma', 'Atopic asthma', 'Obese non-eosinophilic asthma' and 'Asthma Not Otherwise Specified'. We will use blood eosinophil counts, asthma severity and additional phenotype-specific criteria as defining criteria for the phenotypes. Thus, we will be able to describe the profile of the asthma patients including demographic criteria, comorbidities and treatment step as defined by the BTS guidelines. The frequency of asthma exacerbations in each phenotype will be studied using Poisson regression to control for potential confounders. As novel asthma treatments aim to target clinical phenotypes, it is important to understand their occurrence using real world evidence.
Asthma exacerbation rates, including asthma hospitalizations and ER visits; Asthma medication use (BTS step); Pre-defined comorbidities: (Atopy, COPD, Gastro-oesophageal reflux disease, Nasal polyps, Sleep Apnoea, Anxiety, Depression).
Jennifer Quint - Chief Investigator - Imperial College London
Francis Nissen - Corresponding Applicant - Roche
Chloe Bloom - Collaborator - Imperial College London
Hana Mullerova - Collaborator - AstraZeneca Ltd - UK Headquarters
Ian Douglas - Collaborator - London School of Hygiene & Tropical Medicine ( LSHTM )
Liam Smeeth - Collaborator - London School of Hygiene & Tropical Medicine ( LSHTM )
Neil Pearce - Collaborator - London School of Hygiene & Tropical Medicine ( LSHTM )
HES Accident and Emergency;HES Admitted Patient Care;ONS Death Registration Data;Patient Level Index of Multiple Deprivation