Development of a prediction model for infants at high risk of food allergy

Artificial intelligence in pediatric allergy research

Atopic dermatitis, food allergy, allergic rhinitis, and asthma are among the most common diseases in childhood. They are heterogeneous diseases, can co-exist in their development, and manifest complex associations with other disorders and environmental and hereditary factors. Elucidating these intricacies by identifying clinically distinguishable groups and actionable risk factors will allow for better understanding of the diseases, which will enhance clinical management and benefit society and affected individuals and families. Artificial intelligence (AI) is a promising tool in this context, enabling discovery of meaningful patterns in complex data. Numerous studies within pediatric allergy have and continue to use AI, primarily to characterize disease endotypes/phenotypes and to develop models to predict future disease outcomes. However, most implementations have used relatively simplistic data from one source, such as questionnaires. In addition, methodological approaches and reporting are lacking. This review provides a practical hands-on guide for conducting AI-based studies in pediatric allergy, including (1) an introduction to essential AI concepts and techniques, (2) a blueprint for structuring analysis pipelines (from selection of variables to interpretation of results), and (3) an overview of common pitfalls and remedies. Furthermore, the state-of-the art in the implementation of AI in pediatric allergy research, as well as implications and future perspectives are discussed.

CONCLUSION

AI-based solutions will undoubtedly transform pediatric allergy research, as showcased by promising findings and innovative technical solutions, but to fully harness the potential, methodologically robust implementation of more advanced techniques on richer data will be needed.

WHAT IS KNOWN

• Pediatric allergies are heterogeneous and common, inflicting substantial morbidity and societal costs. • The field of artificial intelligence is undergoing rapid development, with increasing implementation in various fields of medicine and research.

WHAT IS NEW

• Promising applications of AI in pediatric allergy have been reported, but implementation largely lags behind other fields, particularly in regard to use of advanced algorithms and non-tabular data. Furthermore, lacking reporting on computational approaches hampers evidence synthesis and critical appraisal. • Multi-center collaborations with multi-omics and rich unstructured data as well as utilization of deep learning algorithms are lacking and will likely provide the most impactful discoveries.

Effects of delivery mode and labor duration on the development of food sensitization in infancy

BACKGROUND

The effects of delivery mode and labor duration on the development of food sensitization (FS) in infancy remain unclear.

OBJECTIVE

To elucidate the potential effects of delivery mode and labor duration on FS development by 6 months of age.

METHODS

Using data from a randomized controlled trial of a birth cohort from 4 Japanese hospitals that assessed cow's milk allergy development by 6 months of age, we performed a nested case-control trial of 462 participants who had undergone the final assessment at 6 months of age. FS was defined as positive skin prick test reactions to hen's egg, cow's milk, wheat, or soy. For the primary outcome, we calculated the adjusted odds ratio of vaginal delivery (VD) relative to cesarean delivery for FS development by 6 months of age using a multivariable logistic regression analysis. For the secondary outcome, we compared labor durations between participants with and without FS using the Mann-Whitney U test.

RESULTS

The adjusted odds ratio of VD for FS development was 2.54 (95% confidence interval, 1.32-4.87; P = .005). The median labor duration was significantly longer in participants with FS (5.7 hours, interquartile range, 2.7-10.1) than in participants without FS (4.5 hours, 1.1-8.2) (P = .01).

CONCLUSION

VD was considerably associated with an increased risk of FS in infancy, and longer labor durations may promote FS development.