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Classification and implementation of asthma phenotypes in elderly patients.
Heung-Woo Park1, Woo-Jung Song1, Sae-Hoon Kim2
1Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea; Institute of Allergy and Clinical Immunology, Seoul National University Medical Research Center, Seoul, Korea.
Summary
Researchers identified four distinct asthma phenotypes in elderly patients using cluster analysis. A simple decision-tree algorithm aids in identifying patients prone to acute asthma exacerbations.
Area of Science:
- Geriatric Medicine
- Pulmonology
- Data Science in Healthcare
Background:
- Asthma management in the elderly requires clear phenotypic classification, which is currently lacking.
- Optimal treatment strategies for elderly asthma patients depend on precise phenotype identification.
Purpose of the Study:
- To classify elderly asthma patients (≥ 65 years) into distinct phenotypes using cluster analysis.
- To develop a practical decision-tree algorithm for implementing these asthma classifications.
Main Methods:
- K-means cluster analysis was applied to a cohort of 872 elderly asthma patients.
- Acute asthma exacerbation data over 2 years were used to evaluate clinical trajectories.
- A decision-tree algorithm was developed and validated using a secondary cohort.
Main Results:
- Four distinct elderly asthma clusters were identified: (1) long symptoms/airway obstruction, (2) female/normal lung function, (3) male smokers/reduced lung function, (4) high BMI/borderline lung function.
- Cluster grouping significantly predicted time to first acute asthma exacerbation (P = .01).
- The decision-tree algorithm, using predicted FEV1 and smoking pack-years, demonstrated efficient classification.
Conclusions:
- Four elderly asthma phenotypic clusters with varying exacerbation risks were defined.
- A simplified decision-tree algorithm facilitates practical application for understanding elderly asthma.
- This approach helps identify elderly patients at higher risk for asthma exacerbations.
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