Phenotyping severe asthma: For what purpose?
Published Online: June 27, 2014
Severe asthma is widely recognized as a heterogeneous disease. However, uncertainties exist regarding their potential relevance and use for specific therapeutic management, as well as regarding future risk. Clinical and biological heterogeneities encouraged statistical identification of different phenotypes using sophisticated cluster analysis. Whether an existing algorithm for cluster identification will successfully predict different outcomes is of particular importance for patients and physicians’ guidance.
In an original research paper recently published in The Journal of Allergy and Clinical Immunology (JACI), Bourdin and colleagues applied the Severe Asthma Research Program (SARP) algorithm to a French longitudinal cohort of 112 patients with severe asthma. This allowed identification of the five clusters. Level of control, occurrence of exacerbations and lung function were chosen as relevant future risks related to severe asthma and were then analyzed and compared through the different clusters after a year of attentive clinical follow-up.
As populations included in this study differed from the original SARP study, a new algorithm was created using the same statistical method. Newly identified clusters ability to predict different outcomes were also tested.
Time to first exacerbation, exacerbation rates, level of control, and lung function did not differ among clusters, regardless of the algorithm used. Actually, the authors found that belonging to any cluster, which means having a particular kind of severe asthma according to statistically identified criteria, free of any a-priori hypothesis, was of no particular significance in terms of one-year future risk.
The author’s findings suggest that current algorithms are inadequate to take into account different future risks associated with severe asthma. They asked whether adding longitudinal data into phenotypes specifications—as routinely done during the clinic—will lead to more relevant predictive information for both patient management and physician guidance.
The Journal of Allergy and Clinical Immunology (JACI) is an official scientific journal of the AAAAI, and is the most-cited journal in the field of allergy and clinical immunology.