摘要
背景：哮喘与急性呼吸道感染（ARI）的风险增加相关。但关于自然语言处理（NLP）驱动的数字生物标志物是否可以在儿童早年识别ARI的高危哮喘亚组，目前知之甚少。
目的：本研究旨在评估数字生物标志物是否可以识别儿童哮喘中ARI高危亚组。
方法：本研究对1997—2016年梅奥诊所出生队列的电子健康记录应用了经验证的用于预定义哮喘标准（NLP-PAC）和哮喘预测指数（NLP-API）的NLP算法。本研究将队列分为4个亚组：两种标准均阳性（NLP-PAC+/NLP-API+）、仅PAC阳性（NLP-PAC+）、仅API阳性（NLR-API+）及两种标准均阴性（NLP-PAC-/NLP-API-）。本研究评估了4个亚组患者中3岁时罹患5种需医学治疗的ARI（肺炎、频繁甲型溶血性链球菌咽部感染、百日咳杆菌、甲/乙型流感和呼吸道合胞病毒感染）和NLP算法定义的哮喘急性发作的风险。同时进一步研究上述关联是否在3岁前出现。
结果：本研究共纳入22370名符合条件的受试者（51%为男性，81%为白种人）。与其他组相比，NLP-PAC+/NLP-API+亚组患者罹患肺炎、乙型流感和哮喘急性发作风险最高，其他ARI未发现显著差异。与其他组相比，同一亚组在3岁内肺炎、甲/乙型流感和呼吸道合胞病毒感染的发生率最高。
结论：NLP-PAC+/NLP-API+可作为一种新型数字生物标志物，用以预测儿童哮喘中罹患肺炎、乙型流感和哮喘急性发作的高危亚组，且上述表型可能在年幼时即出现。
Artificial intelligence biomarker detects high-risk childhood asthma subgroup for respiratory infections and exacerbations.
Juhn YJ, Wi CI, Ryu E, King KS, Sohn S, Sagheb E, Jenkins G, Watson D, Park MA, Chiarella SE, Kita H, Ali M, Huskins WC, Ristagno EH, Absah I, Grose C, Ihrke K, Krusemark EA, Pongdee T, Nordlund B, Davis CM, Pignolo RJ, Liu H.
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.
背景：哮喘与急性呼吸道感染（ARI）的风险增加相关。但关于自然语言处理（NLP）驱动的数字生物标志物是否可以在儿童早年识别ARI的高危哮喘亚组，目前知之甚少。
目的：本研究旨在评估数字生物标志物是否可以识别儿童哮喘中ARI高危亚组。
方法：本研究对1997—2016年梅奥诊所出生队列的电子健康记录应用了经验证的用于预定义哮喘标准（NLP-PAC）和哮喘预测指数（NLP-API）的NLP算法。本研究将队列分为4个亚组：两种标准均阳性（NLP-PAC+/NLP-API+）、仅PAC阳性（NLP-PAC+）、仅API阳性（NLR-API+）及两种标准均阴性（NLP-PAC-/NLP-API-）。本研究评估了4个亚组患者中3岁时罹患5种需医学治疗的ARI（肺炎、频繁甲型溶血性链球菌咽部感染、百日咳杆菌、甲/乙型流感和呼吸道合胞病毒感染）和NLP算法定义的哮喘急性发作的风险。同时进一步研究上述关联是否在3岁前出现。
结果：本研究共纳入22370名符合条件的受试者（51%为男性，81%为白种人）。与其他组相比，NLP-PAC+/NLP-API+亚组患者罹患肺炎、乙型流感和哮喘急性发作风险最高，其他ARI未发现显著差异。与其他组相比，同一亚组在3岁内肺炎、甲/乙型流感和呼吸道合胞病毒感染的发生率最高。
结论：NLP-PAC+/NLP-API+可作为一种新型数字生物标志物，用以预测儿童哮喘中罹患肺炎、乙型流感和哮喘急性发作的高危亚组，且上述表型可能在年幼时即出现。
（中日友好医院呼吸与危重症医学科 张婧媛 摘译 林江涛 审校）
（J Allergy Clin Immunol. 2025 Aug 19:S0091-6749(25)00861-9. doi: 10.1016/j.jaci.2025.07.031. Epub ahead of print. PMID: 40840861.）
Artificial intelligence biomarker detects high-risk childhood asthma subgroup for respiratory infections and exacerbations.
Juhn YJ, Wi CI, Ryu E, King KS, Sohn S, Sagheb E, Jenkins G, Watson D, Park MA, Chiarella SE, Kita H, Ali M, Huskins WC, Ristagno EH, Absah I, Grose C, Ihrke K, Krusemark EA, Pongdee T, Nordlund B, Davis CM, Pignolo RJ, Liu H.
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.
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