Artificial intelligence biomarker detects high-risk childhood asthma subgroup for respiratory infections and exacerbations.

Abstract

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. We assessed whether a digital biomarker could identify a high-risk subgroup of childhood asthma for ARI. 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. 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. 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.