Longitudinal Outcomes of Severe Asthma: Real-World Evidence of Multidimensional Analyses

被引：8

作者：

Lee, Youngsoo ^{[1
]}

Park, Youjin ^{[2
]}

Kim, Chungsoo ^{[2
]}

Lee, Eunyoung ^{[3
,4
]}

Lee, Hyun Young ^{[5
]}

Woo, Seong-Dae ^{[1
]}

You, Seng Chan ^{[3
]}

Park, Rae Woong ^{[2
,3
]}

Park, Hae-Sim ^{[1
]}

机构：

[1] Ajou Univ, Dept Allergy & Clin Immunol, Sch Med, 164 Worldcup Ro, Suwon 16499, South Korea

[2] Ajou Univ, Dept Biomed Sci, Grad Sch Med, Suwon, South Korea

[3] Ajou Univ, Dept Biomed Informat, Sch Med, Suwon, South Korea

[4] Ajou Univ, Med Res Collaborat Ctr, Ajou Res Inst Innovat Med, Med Ctr,Off Biostat, Suwon, South Korea

[5] Ajou Univ, Clin Trial Ctr, Dept Stat, Med Ctr, Suwon, South Korea

来源：

JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY-IN PRACTICE | 2021年 / 9卷 / 03期

关键词：

Severe asthma; Clinical outcome; Asthma exacerbation; Eosinophil; Inflammation; Real-world evidence; BLOOD EOSINOPHIL COUNT; LUNG-FUNCTION DECLINE; SEVERE EXACERBATIONS; SPUTUM EOSINOPHILS; INFLAMMATION; BIOMARKER; PATHOPHYSIOLOGY; MEPOLIZUMAB; ACTIVATION; PHENOTYPE;

D O I：

10.1016/j.jaip.2020.09.055

中图分类号：

R392 [医学免疫学];

学科分类号：

100102 ;

摘要：

BACKGROUND: There have been few studies assessing long-term outcomes of asthma based on regular follow-up data. OBJECTIVE: We aimed to demonstrate clinical outcomes of asthma by multidimensional analyses of a long-term real-world database and a prediction model of severe asthma using machine learning. METHODS: The database included 567 severe and 1337 nonsevere adult asthmatics, who had been monitored during a follow-up of up to 10 years. We evaluated longitudinal changes in eosinophilic inflammation, lung function, and the annual number of asthma exacerbations (AEs) using a linear mixed effects model. Least absolute shrinkage and selection operator logistic regression was used to develop a prediction model for severe asthma. Model performance was evaluated and validated. RESULTS: Severe asthmatics had higher blood eosinophil (P = .02) and neutrophil (P <.001) counts at baseline than nonsevere asthmatics; blood eosinophil counts showed significantly slower declines in severe asthmatics than nonsevere asthmatics throughout the follow-up (P = .009). Severe asthmatics had a lower level of forced expiratory volume in 1 second (P <.001), which declined faster than nonsevere asthmatics (P = .033). Severe asthmatics showed a higher annual number of severe AEs than nonsevere asthmatics. The prediction model for severe asthma consisted of 17 variables, including novel biomarkers. CONCLUSIONS: Severe asthma is a distinct phenotype of asthma with persistent eosinophilia, progressive lung function decline, and frequent severe AEs even on regular asthma medication. We suggest a useful prediction model of severe asthma for research and clinical purposes. (C) 2020 American Academy of Allergy, Asthma & Immunology

引用

页码：1285 / +

页数：16

共 48 条

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