Asthma is very common in the UK, affecting 5.4 million people. People with milder disease can usually be managed successfully within primary care, by their GP or asthma nurse, and often have “well controlled disease” and experience relatively few adverse effects, such as asthma attacks (also known as exacerbations). However, some patients, irrespective of the severity of their disease but particularly those with more severe disease, have asthma attacks. It is not clear whether there are specific factors that affect the likelihood of having an asthma attack. This is important to recognise, not only for patients but also their health carers. This study aims to build on existing tools that look at certain factors and how they are associated with an asthma attack to create a personalised risk prediction model for asthma exacerbations. Information from this prediction model will feed into a larger project that involves the development of a conversational agent that people can interact with to better manage their disease and reduce their exacerbation risk.
In the UK, over 5.4 million people have asthma, and the condition accounts for over 65,000 hospital admissions and 1,000 deaths annually. People with asthma may experience periods of acute worsening of symptoms: cough, wheeze, breathlessness and sputum production. Asthma exacerbations range from mild attacks, which interrupt daily life and work productivity, to severe and life-threatening attacks. People who have exacerbations are at risk of having further exacerbations however whether there are specific factors that affect the likelihood of having an exacerbation of asthma and how this may differ from one individual to the next has not been fully investigated. Our objective is to develop a personalised risk prediction model for asthma exacerbations by exploring factors collected in routine data that may be associated with exacerbations. We will include a cohort of asthma patients aged 18 years and older and investigate factors associated with exacerbations using CPRD Aurum data linked with HES APC, HES A&E, HES OP and IMD data. The study period will be 2010 to 2019. We will determine risk factors for prediction of asthma exacerbations using logistic regression. This information will feed into a larger project that involves the development of a conversational agent that people can interact with to better manage their disease and reduce their exacerbation risk.
Asthma exacerbations will be defined using previous methodology and according to the source of data. We will use ICD-10 codes starting with J45 and J46 to find asthma exacerbations in HES APC and ONS mortality data. We will use diagnostic code 251 (Respiratory conditions - bronchial asthma) for asthma exacerbations in HES A&E data. We will use treating specialty codes 258 (Paediatric Respiratory Medicine) and 340 (Respiratory Medicine (Previously Known As Thoracic Medicine)) for HES OP events. We will use a list of medical codes specified in the appendix (asthma_codes_sep2020_for_extract.txt) to find asthma exacerbations in CPRD AURUM observation data. Finally, we will an appropriate list of product codes for OCS medications to identify asthma exacerbation events in CPRD AURUM drug issue data.
Jennifer Quint - Chief Investigator - Imperial College London
Constantinos Kallis - Corresponding Applicant - Imperial College London
Bjoern Schuller - Collaborator - Imperial College London
Rafael Calvo - Collaborator - Imperial College London
HES Accident and Emergency;HES Admitted Patient Care;HES Outpatient;ONS Death Registration Data;Patient Level Index of Multiple Deprivation