Predicting asthma exacerbations: Peak expiratory flow revisited

Abstract

The ability to predict and prevent future exacerbations has been a consistent focus of asthma research. Although some progress has been made on identifying factors associated with an increased risk of exacerbation at the population and individual levels, no measure or instrument is available for widespread use in the asthma community. In this issue of the Journal, Thamrin et al present a statistical method used to calculate the future exacerbation risk for a patient (as defined by deterioration in percent predicted peak expiratory flow [PEF]) based on the history of fluctuations in lung function (percent predicted PEF). The authors challenge us to reframe our analytic approach to asthma. Frey et al have suggested that a patient with asthma should be thought of as a complex, nonlinear dynamic systems network; such a network exhibits fluctuations over time in physiologic and clinical variables. However, such a system also exhibits memory effects, and although changes can occur over time, the recent past will include most of the pertinent information needed to predict the short-term future risk. Viewingasthma through this framework, it follows that the single best individual parameter to predict the risk of an exacerbation over the next year is the history of an exacerbation in the prior year. The natural history of asthma in any given patient is a summation of personal characteristics (eg, demographics, inflammatory profile, and lungmechanics), genetics, both the physical (exposure to environment [environmental tobacco smoke, pollution, and allergens]) and psychosocial (access to care) environment, and therapy (including adherence). The complex interaction of these factors results in the overall clinical phenotype expressed by a patient, and this helps explain why the recent past is a strong predictor of future events. Modeling asthma as a complex, nonlinear dynamic system also fits with our increasing recognition of the heterogeneity of asthma. Patients with asthma present differently in regard to symptoms, physiologic measures, inflammatory indices, and perception of obstruction; measuring a single parameter at a single point might not