Abstract
Background Asthma in children is a heterogeneous disease
manifested by various phenotypes and endotypes. The level of disease
control as well as the effectiveness of anti-inflammatory treatment is
variable and inadequate in a significant portion of patients.
Objectives By applying machine learning algorithms, we aimed to
predict treatment success in a pediatric asthma cohort and to identify
key variables for understanding underlying mechanisms.
Methods We predicted treatment outcomes in children with mild
to severe asthma (N=365), according to changes in asthma control, lung
function (FEV1, MEF50) and FENO values after 6 months of controller
medication use, using RandomForest and AdaBoost classifiers.
Results The highest prediction power is achieved for control-
and, to lower extend, for FENO-related treatment outcomes. The most
predictive variables for asthma control are related to asthma severity
and total IgE, which was also predictive for FENO-based outcomes.
MEF50-related treatment outcomes were better predicted than FEV1-based
response and one of the best predictive variables for this response was
hsCRP.
Conclusions Our results suggest that asthma control- and
FENO-based outcomes can be more accurately predicted using machine
learning than FEV1 and MEF50. This supports the symptom control-based
asthma management approach and its complementary FENO-guided tool in
children. T2-high asthma seemed to respond best to anti-inflammatory
treatment. The prediction of MEF50-based treatment outcomes emphasizes
the role of the distal airways in childhood asthma. The results of this
study in predicting treatment success will help to enable treatment
optimization and to implement the concept of precision medicine in
pediatric asthma treatment.