Classification and implementation of asthma phenotypes in elderly patients

Heung Woo Park, Woo Jung Song, Sae Hoon Kim, Hye Kyung Park, Sang Heon Kim, Yong Eun Kwon, Hyouk Soo Kwon, Tae Bum Kim, Yoon Seok Chang, You Sook Cho, Byung Jae Lee, Young Koo Jee, An Soo Jang, Dong Ho Nahm, Jung Won Park, Ho Joo Yoon, Young Joo Cho, Byoung Whui Choi, Hee Bom Moon, Sang Heon Cho

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)


Background: No attempt has yet been made to classify asthma phenotypes in the elderly population. It is essential to clearly identify clinical phenotypes to achieve optimal treatment of elderly patients with asthma.

Objectives: To classify elderly patients with asthma by cluster analysis and developed a way to use the resulting cluster in practice.

Methods: We applied k-means cluster to 872 elderly patients with asthma (aged ≥65 years) in a prospective, observational, and multicentered cohort. Acute asthma exacerbation data collected during the prospective follow-up of 2 years was used to evaluate clinical trajectories of these clusters. Subsequently, a decision-tree algorithm was developed to facilitate implementation of these classifications.

Results: Four clusters of elderly patients with asthma were identified: (1) long symptom duration and marked airway obstruction, (2) female dominance and normal lung function, (3) smoking male dominance and reduced lung function, and (4) high body mass index and borderline lung function. Cluster grouping was strongly predictive of time to first acute asthma exacerbation (log-rank P =.01). The developed decision-tree algorithm included 2 variables (percentage of predicted forced expiratory volume in 1 second and smoking pack-years), and its efficiency in proper classification was confirmed in the secondary cohort of elderly patients with asthma.

Conclusions: We defined 4 elderly asthma phenotypic clusters with distinct probabilities of future acute exacerbation of asthma. Our simplified decision-tree algorithm can be easily administered in practice to better understand elderly asthma and to identify an exacerbation-prone subgroup of elderly patients with asthma.

Original languageEnglish
Pages (from-to)18-22
Number of pages5
JournalAnnals of Allergy, Asthma and Immunology
Issue number1
Publication statusPublished - 2015 Jan 1

Bibliographical note

Publisher Copyright:
© 2015 American College of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

All Science Journal Classification (ASJC) codes

  • Immunology and Allergy
  • Immunology
  • Pulmonary and Respiratory Medicine


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