Risk of severe asthma episodes predicted from fluctuation analysis of airway function

Asthma is an increasing health problem worldwide(1), 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 trial(4). 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 beta(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.