The AWARD study
Research type
Research Study
Full title
The hospitalised acute wheezy adult with airways disease: studying the different characteristics and treatment responses
IRAS ID
191475
Contact name
Mona Bafadhel
Contact email
Sponsor organisation
University of Oxford
Duration of Study in the UK
1 years, 4 months, 30 days
Research summary
Research Summary
This is an observational study of adult patients admitted to the Emergency Department at a busy teaching hospital with acute worsening (exacerbations) of their pre-existing airways disease (including asthma and COPD). All patients attending will be asked to participate and following consent all participants will be characterised carefully and their clinical progress will be followed. There will be no changes made to their treatment and no exposure to any investigational drugs. The aim is to see whether clinical outcomes vary depending upon the initial clinical characteristics of the participants.
Summary of Results
COPD and asthma exacerbations result in many emergency department admissions. Not all treatments are successful, often leading to hospital readmissions.
Aims
We sought to develop predictive models for exacerbation treatment outcome in a cohort of exacerbating asthma and COPD patients presenting to the emergency department.
Methods
Treatment failure was defined as the need for additional systemic corticosteroids (SCS) and/or antibiotics, hospital readmissison or death within 30 days of initial emergency department visit. We performed analysis comparing characteristics of patients either given or not given SCS at exacerbation and of patients who succeeded versus failed treatment. Patient demographics, medications and exacerbation symptoms, physiology and biology were available. We developed multivariate random forest models to identify predictors of SCS prescription and for predicting treatment failure.
Results
Data were available for 81 patients, 43 (53%) of whom failed treatment. 64 (79%) of patients were given SCS. A random forest model using presence of wheeze at exacerbation and blood eosinophil percentage predicted SCS prescription with area under receiver operating characteristic curve (AUC) 0.69. An 11 variable random forest model (which included medication, previous exacerbations, symptoms and quality of life scores) could predict treatment failure with AUC 0.81. A random forest model using just the two best predictors of treatment failure, namely, visual analogue scale for breathlessness and sputum purulence, predicted treatment failure with AUC 0.68.
Conclusion
Prediction of exacerbation treatment outcome can be achieved via supervised machine learningREC name
London - Fulham Research Ethics Committee
REC reference
15/LO/2119
Date of REC Opinion
18 Dec 2015
REC opinion
Favourable Opinion