Korean childhood asthma study (KAS): a prospective, observational cohort of Korean asthmatic children

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Dong In, Suh; Song, Dae Jin; Baek, Hey-Sung; Shin, Meeyong; Yoo, Young; Kwon, Ji-Won; Jang, Gwang Cheon; Yang, Hyeon-Jong; Lee, Eun; Kim, Hwan Soo; Seo, Ju-Hee; Woo, Sung-Il; Kim, Hyung Young; Shin, Youn Ho; Lee, Ju Suk; Yoon, Jisun; Jung, Sungsu; Han, Minkyu; Eom, Eunjin; Yu, Jinho; Kim, Woo Kyung; Lim, Dae Hyun; Kim, Jin Tack; Chang, Woo-Sung; Lee, Jeom-Kyu
Issue Date
BioMed Central
BMC Pulmonary Medicine. 2019 Mar 15;19(1):64
AsthmaChildClusterCohort studyKoreaProspective
Asthma is a syndrome composed of heterogeneous disease entities. Although it is agreed that proper asthma endo-typing and appropriate type-specific interventions are crucial in the management of asthma, little data are available regarding pediatric asthma.

We designed a cluster-based, prospective, observational cohort study of asthmatic children in Korea (Korean childhood Asthma Study [KAS]). A total of 1000 Korean asthmatic children, aged from 5 to 15 years, will be enrolled at the allergy clinics of the 19 regional tertiary hospitals from August 2016 to December 2018. Physicians will verify the relevant histories of asthma and comorbid diseases, as well as airway lability from the results of spirometry and bronchial provocation tests. Questionnaires regarding subjects baseline characteristics and their environment, self-rating of asthma control, and laboratory tests for allergy and airway inflammation will be collected at the time of enrollment. Follow-up data regarding asthma control, lung function, and environmental questionnaires will be collected at least every 6 months to assess outcome and exacerbation-related aggravating factors. In a subgroup of subjects, peak expiratory flow rate will be monitored by communication through a mobile application during the overall study period. Cluster analysis of the initial data will be used to classify Korean pediatric asthma patients into several clusters; the exacerbation and progression of asthma will be assessed and compared among these clusters. In a subgroup of patients, big data-based deep learning analysis will be applied to predict asthma exacerbation.

Based on the assumption that asthma is heterogeneous and each subject exhibits a different subset of risk factors for asthma exacerbation, as well as a different disease progression, the KAS aims to identify several asthma clusters and their essential determinants, which are more suitable for Korean asthmatic children. Thereafter we may suggest cluster-specific strategies by focusing on subjects personalized aggravating factors during each exacerbation episode and by focusing on disease progression. The KAS will provide a good academic background with respect to each interventional strategy to achieve better asthma control and prognosis.
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College of Medicine/School of Medicine (의과대학/대학원)Pediatrics (소아과학전공)Journal Papers (저널논문_소아과학전공)
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