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

Abstract

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.