Distinguishing Asthma Phenotypes Using Machine Learning Approaches.

Asthma is not a single disease, but an umbrella term for a number of distinct diseases, each of which are caused by a distinct underlying pathophysiological mechanism. These discrete disease entities are often labelled as asthma endotypes. The discovery of different asthma subtypes has moved from subjective approaches in which putative phenotypes are assigned by experts to data-driven ones which incorporate machine learning. This review focuses on the methodological developments of one such machine learning technique-latent class analysis-and how it has contributed to distinguishing asthma and wheezing subtypes in childhood. It also gives a clinical perspective, presenting the findings of studies from the past 5 years that used this approach. The identification of true asthma endotypes may be a crucial step towards understanding their distinct pathophysiological mechanisms, which could ultimately lead to more precise prevention strategies, identification of novel therapeutic targets and the development of effective personalized therapies

Stability and predictiveness of multiple trigger and episodic viral wheeze in preschoolers

BackgroundIn 2008, the European Respiratory Society Task Force proposed the terms multiple-trigger wheeze (MTW) and episodic (viral) wheeze (EVW) for children with wheezing episodes. We determined MTW and EVW prevalence, their 24-month stability and predictiveness for asthma. MethodsIn total, 565 preschoolers (1-, 2- and 3-year-olds) in primary care with respiratory symptoms were followed until the age of 6years when asthma was diagnosed. MTW status and EVW status were determined using questionnaire data collected at baseline and after one and 2years. We distinguished 3 phenotypes and determined their 24-month stability, also accounting for treatment with inhaled corticosteroids (ICS). Logistic regression was used to analyse the phenotypes' associations with asthma. ResultsTwo hundred and eighty-one children had complete information. MTW and EVW were stable in 10 of 281 (3.6%) and 24 of 281 (8.5%), respectively. The odds of developing asthma for children with stable MTW and stable EVW were 14.4 (1.7-119) and 3.6 (1.2-11.3) times greater than those for children free of wheeze (for at least 1year). ICS was associated with increased stability of MTW and EVW. ConclusionsStable multiple-trigger and stable episodic viral wheeze are relatively uncommon. However, 1- to 3-year-olds with stable MTW are at much increased risk of asthma

Characterizing wheeze phenotypes to identify endotypes of childhood asthma, and the implications for future management

It is now a commonly held view that asthma is not a single disease, but rather a set of heterogeneous diseases sharing common symptoms. One of the major challenges in treating asthma is understanding these different asthma phenotypes and their underlying biological mechanisms. This review gives an epidemiological perspective of our current understanding of the different phenotypes that develop from birth to childhood that come under the umbrella term 'asthma'. The review focuses mainly on publications from longitudinal birth cohort studies where the natural history of asthma symptoms is observed over time in the whole population. Identifying distinct pathophysiological mechanisms for these different phenotypes will potentially elucidate different asthma endotypes, ultimately leading to more effective treatment and management strategies. © 2013 Informa UK Ltd