Published in Annals of the American Thoracic Society. Here is a link to the article.
Regenstrief Institute author: Arthur H. Owora, PhD, MPH
The information below was provided by Dr. Owora.
Limited real-world evidence on pediatric asthma biologics
Robust real-world data on the effectiveness of biologic therapies in children with severe asthma remain limited, particularly across different ages and early-life risk profiles. This evidence gap constrains precision in treatment decisions and clinical guidance.
Children with moderate to severe asthma requiring biologic therapy are most affected, especially those initiating biologic treatment at younger ages and those with early indicators of allergic disease or high-risk asthma histories.
Earlier biologic treatment improves outcomes
Initiating biologic therapy earlier in childhood — particularly in children with significant early-life risk factors and allergic sensitization — is associated with greater reductions in severe asthma exacerbations in real-world practice.
Findings highlight the importance of treatment timing and patient history when optimizing outcomes with asthma biologics.
Risks of delayed treatment initiation
Delayed initiation of biologic therapy until adolescence or failure to account for early-childhood risk profiles may reduce potential treatment benefit. These findings highlight the risk of suboptimal outcomes when treatment timing or patient selection does not align with underlying disease risk.
Prioritizing early identification and risk-stratified treatment
Clinicians should prioritize earlier identification and risk-stratified initiation of biologics in children with severe asthma, particularly those with high early-life risk burden, to maximize treatment benefits.
Implications for future care pathways and research
Study findings support development of care pathways that incorporate earlier, risk-stratified biologic initiation. Decision-making algorithms may benefit from integrating age at treatment initiation and early-life risk indicators, such as polysensitization and high early disease burden, to better identify children most likely to benefit and reduce severe exacerbations.
Future research may also explore the role of clinical artificial intelligence in supporting these approaches. Clinical AI tools could help identify high-risk pediatric patients earlier and guide treatment timing and patient selection by detecting patterns in real-world clinical data, potentially improving precision in biologic therapy use.
This study was in part supported by NIH grants K01HL166436 (AHO), R03HS029088 (AHO), R01HL170368 (EF), and P01HL158507 (BG).
Authors
Arthur H Owora1 2, Kirsten Kloepfer1, Robert Tepper1, Nadia Krupp1, Samantha H Averill1, Ryan C Unruh1, Bowen Jiang1, Yash Shah1, Benjamin Gaston1, Erick Forno1
Affiliations
1Division of Pediatric Pulmonology, Allergy/Immunology and Sleep Medicine, Department of Pediatrics, Indiana University School of Medicine, Indiana, United States.
2Center for Biomedical Informatics, Regenstrief Institute, IN, United States.
