- Exacerbations of asthma
- Definition, prediction and prevention
G. ter Riet
- Award date
- 19 October 2018
- Number of pages
- Document type
- PhD thesis
- Faculty of Medicine (AMC-UvA)
Asthma is a highly prevalent inflammatory airways disease, characterized by symptoms such as coughing, wheezing and difficulty with breeding that vary over time. Exacerbations are events of sudden deterioration of asthma. These events are an important outcome measure in asthma research: they cause considerable patient burden, leading to healthcare utilization.
This thesis aims to answer the following research questions:
1. Is the currently recommended definition of asthma exacerbations appropriate for use in clinical studies?
In chapter 2 we established that currently there is no commonly accepted definition for asthma exacerbations.
2. To what extent are we able to predict exacerbations in individual patients with asthma?
In chapter 3 we developed prediction models for severe exacerbations of asthma, based on (1) history items, extended with (2) spirometry values and (3) fractional exhaled nitric oxide. In chapter 4 we reviewed the literature to identify prediction models for asthma exacerbations. This study demonstrates that a generalizable model predicting severe exacerbations is feasible.
3. In order to prevent exacerbations, which level of asthma symptom control should be targeted, and which controller regimen is preferred for this aim?
Chapter 5 describes the Asthma Control Cost-Utility RAndomized Trial Evaluation study comparing the clinical effectiveness of steering at three different ways of asthma control. There were no differences in severe exacerbation rate. In chapter 6 we aimed to determine the comparative effectiveness of current maintenance strategies. For prevention of severe exacerbations, combined inhaled corticosteroids and long acting β agonists, were ranked first.
Thesis (complete) (Embargo up to and including 15 February 2019)
Chapter 4: Exacerbations in adults with asthma: A systematic review and external validation of prediction models (Embargo up to and including 15 February 2019)
If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library, or send a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.