The journal of allergy and clinical immunology. In practice · 2020
Longitudinal Outcomes of Severe Asthma: Real-World Evidence of Multidimensional Analyses.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Lee Youngsoo, Park Youjin, Kim Chungsoo, Lee Eunyoung, Lee Hyun Young, Woo Seong-Dae, You Seng Chan, Park Rae Woong, Park Hae-Sim
Аннотация
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.
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.