Mario Lovric

and 5 more

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.