Asthma is an increasing health problem worldwide¹, but the long-term temporal pattern of clinical symptoms is not understood and predicting asthma episodes is not generally possible^{2, 3}. We analyse the time series of peak expiratory flows, a standard measurement of airway function that has been assessed twice daily in a large asthmatic population during a long-term crossover clinical trial⁴. Here we introduce an approach to predict the risk of worsening airflow obstruction by calculating the conditional probability that, given the current airway condition, a severe obstruction will occur within 30 days. We find that, compared with a placebo, a regular long-acting bronchodilator (salmeterol) that is widely used to improve asthma control decreases the risk of airway obstruction. Unexpectedly, however, a regular short-acting ₂-agonist bronchodilator (albuterol) increases this risk. Furthermore, we find that the time series of peak expiratory flows show long-range correlations that change significantly with disease severity, approaching a random process with increased variability in the most severe cases. Using a nonlinear stochastic model, we show that both the increased variability and the loss of correlations augment the risk of unstable airway function. The characterization of fluctuations in airway function provides a quantitative basis for objective risk prediction of asthma episodes and for evaluating the effectiveness of therapy.

1. Paediatric Respiratory Medicine, Department of Paediatrics, University Hospital of Berne, Inselspital 3010 Berne, Switzerland
2. Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215, USA
3. Faculty of Medicine, Dunedin/Christchurch Schools of Medicine, University of Otago, New Zealand
4. Institute for Lung Health, Leicester University, Leicester LE2 7LX, UK

Correspondence to: Urs Frey¹ Correspondence and requests for materials should be addressed to U.F. (Email: urs.frey@insel.ch).

Received 25 June 2005; Accepted 31 August 2005

MORE ARTICLES LIKE THIS

These links to content published by NPG are automatically generated.