Asthma phenotypes in childhood.
Asthma is no longer thought of as a single disease, but rather a collection of varying symptoms expressing different disease patterns. One of the ongoing challenges is understanding the underlying pathophysiological mechanisms that may be responsible for the varying responses to treatment. Areas Covered: This review provides an overview of our current understanding of the asthma phenotype concept in childhood and describes key findings from both conventional and data-driven methods. Expert Commentary: With the vast amounts of data generated from cohorts, there is hope that we can elucidate distinct pathophysiological mechanisms, or endotypes. In return, this would lead to better patient stratification and disease management, thereby providing true personalised medicine.
a Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health , University of Manchester , Manchester , UK.
Matea
M
Deliu
b Department of Paediatrics , Imperial College of Science, Technology & Medicine , London , UK.
Danielle
D
Belgrave
a Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health , University of Manchester , Manchester , UK.
Matthew
M
Sperrin
a Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health , University of Manchester , Manchester , UK.
Iain
I
Buchan
b Department of Paediatrics , Imperial College of Science, Technology & Medicine , London , UK.
Adnan
A
Custovic
D001249:Asthma / Q000503:physiopathology* / Q000628:therapy; D002648:Child; D019468:Disease Management; D006801:Humans; D018579:Patient Selection; D010641:Phenotype; D057285:Precision Medicine
Asthma phenotypes; data-driven methods; endotype; machine learning