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

    mona.bafadhel@ndm.ox.ac.uk

  • 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 learning

  • REC name

    London - Fulham Research Ethics Committee

  • REC reference

    15/LO/2119

  • Date of REC Opinion

    18 Dec 2015

  • REC opinion

    Favourable Opinion