Conclusion

Our goal was to evaluate the prediction of treatment outcomes in childhood asthma after six months of medication use based on initial assessment. Our results show that asthma control (LOAC) was well predicted, while the prediction quality of lung function- based treatment outcomes (FEV1, MEF50) was rather low. These results are in concordance with the GINA control-based management approach, while lung function may not be the tool of choice to be used in guiding treatment in children with asthma21. The prediction model for FENO-based treatment response performed better in almost all aspects than lung function-related outcomes, which suggests that treatment success guided by changes in FENO might be a complementary tool in childhood asthma management21.
Our results also suggest that the current guidelines in asthma management and current expertise in clinical assessment (assessment of severity and disease control) are satisfactory in most cases. Additionally, since total IgE was one of the best predictive variables for FENO-based outcomes, this indicates that T2-high asthma subtypes in children respond best to common controller medication. Although machine learning has shown how treatment outcome prediction can be driven, it has revealed certain issues that need to be addressed in future studies:
Recently, much focus has been given to the implementation of precision medicine in asthma, and experts emphasize that the time for action is now. The use of big data and machine learning in predicting treatment success such as the one in this study might enable treatment optimization and the development of new therapies for each defined endotype.