Journal article
Artificial intelligence biomarker detects high-risk childhood asthma subgroup for respiratory infections and exacerbations
Journal of allergy and clinical immunology, Vol.156(6), pp.1547-1555.e4
12/2025
DOI: 10.1016/j.jaci.2025.07.031
PMCID: PMC12513160
PMID: 40840861
Abstract
Background
Asthma is associated with an increased risk of acute respiratory infections (ARI). Little is known about whether natural language processing (NLP)-powered digital biomarkers can identify a high-risk asthma subgroup for ARI during early childhood.
Objective
We assessed whether a digital biomarker could identify a high-risk subgroup of childhood asthma for ARI.
Methods
We applied validated NLP algorithms for Predetermined Asthma Criteria (NLP-PAC) and Asthma Predictive Index (NLP-API) to electronic health records of the 1997-2016 Mayo Clinic Birth Cohort. We categorized the cohort into 4 subgroups: both criteria positive (NLP−PAC+/NLP−API+), PAC positive only (NLP−PAC+), API positive only (NLP−API+), and both criteria negative (NLP−PAC−/NLP−API−). We assessed the risk of 5 medically attended ARI (pneumonia, frequent group A streptococcal pharyngeal infection, Bordetella pertussis, influenza A/B, and respiratory syncytial virus infection) and asthma exacerbation defined by NLP algorithms at 3 years of age among the 4 subgroups. We also examined whether such associations emerged during the first 3 years of life.
Results
There were 22,370 eligible subjects (51% male and 81% White). The NLP−PAC+/NLP−API+ subgroup had the highest risk of pneumonia, influenza A/B, and asthma exacerbation compared to other groups. No significant differences were found in other ARI. The same subgroup had the highest occurrence of pneumonia, influenza A/B, and respiratory syncytial virus infection, compared to other groups, during the first 3 years of life.
Conclusion
NLP−PAC+/NLP−API+ can be a novel digital biomarker for a high-risk subgroup of childhood asthma for pneumonia, influenza A/B, and asthma exacerbation. This phenotype may emerge early in life.
Details
- Title: Subtitle
- Artificial intelligence biomarker detects high-risk childhood asthma subgroup for respiratory infections and exacerbations
- Creators
- Young J. JuhnChung-Il WiEuijung RyuKatherine S. KingSunghwan SohnElham SaghebGreg JenkinsDavid WatsonMiguel A. ParkSergio E. ChiarellaHirohito KitaMir AliW.Charles HuskinsElizabeth H. RistagnoImad AbsahCharles GroseKathy IhrkeElizabeth A. KrusemarkThanai PongdeeBjörn NordlundCarla M. DavisRobert J. PignoloHongfang Liu
- Resource Type
- Journal article
- Publication Details
- Journal of allergy and clinical immunology, Vol.156(6), pp.1547-1555.e4
- DOI
- 10.1016/j.jaci.2025.07.031
- PMID
- 40840861
- PMCID
- PMC12513160
- NLM abbreviation
- J Allergy Clin Immunol
- ISSN
- 0091-6749
- eISSN
- 1097-6825
- Publisher
- Elsevier; NEW YORK
- Grant note
- NHLBI NIH HHS: R01 HL126667 NIAID NIH HHS: R21 AI116839, R21 AI142702
- Language
- English
- Electronic publication date
- 08/2025
- Date published
- 12/2025
- Academic Unit
- Stead Family Department of Pediatrics; Infectious Disease (Pediatrics)
- Record Identifier
- 9984948119302771
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