Pneumonia, influenza A\/B, and asthma exacerbation risks highest in those positive for both Predetermined Asthma Criteria and Asthma Predictive Index<\/p>\n<\/h3>\n
By Elana Gotkine HealthDay Reporter<\/b>
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MONDAY, Sept. 29, 2025 (HealthDay News) — Natural language processing (NLP) algorithms can identify a subgroup of children with asthma who have a high risk for acute respiratory infections (ARI), according to a study published online Aug. 19 in the Journal of Allergy and Clinical Immunology<\/em>.<\/p>\n Young J. Juhn, M.D., M.P.H., from the Mayo Clinic in Rochester, Minnesota, and colleagues examined whether an NLP-powered digital biomarker can identify a high-risk subgroup for ARI in children. Validated NLP algorithms for Predetermined Asthma Criteria (NLP-PAC) and the Asthma Predictive Index (NLP-API) were applied to the electronic health records of the 1997 to 2016 Mayo Clinic Birth Cohort. The cohort was categorized into four subgroups: both criteria positive, PAC positive only, API positive only, and both criteria negative (NLP-PAC+\/NLP-API+, NLP-PAC+, NLP-API+, and NLP-PAC\u2212\/NLP-API\u2212, respectively). The risks for five medically attended ARI and asthma exacerbations defined by NLP algorithms at 3 years of age were examined.<\/p>\n Data were included for 22,370 eligible individuals. The researchers found that the risks for pneumonia, influenza A\/B, and asthma exacerbations were highest in the NLP-PAC+\/NLP-API+ subgroup compared with the other groups. In other ARI, no significant differences were found. Compared with other subgroups, the NLP-PAC+\/NLP-API+ subgroup also had the highest occurrence of pneumonia, influenza A\/B, and respiratory syncytial virus infection during the first three years of life.<\/p>\n “This study takes us a step closer to precision medicine in childhood asthma, where care shifts from reactive care for advanced severe asthma to prevention and early detection of high-risk patients,” Juhn said in a statement.<\/p>\n One author disclosed ties to the pharmaceutical industry.<\/p>\n Abstract\/Full Text (subscription or payment may be required)<\/a><\/p>\n