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, Jungwon Park, Ho Joo Yoon, Young Joo Cho, Byoung Whui Choi, Hee Bom Moon, Sang Heon Cho

Research output: Contribution to journalArticle

24 Citations (Scopus)

Abstract

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
Volume114
Issue number1
DOIs
Publication statusPublished - 2015 Jan 1

Fingerprint

Asthma
Phenotype
Decision Trees
Lung
Smoking
Forced Expiratory Volume
Airway Obstruction
Cluster Analysis
Body Mass Index

All Science Journal Classification (ASJC) codes

  • Immunology and Allergy
  • Immunology
  • Pulmonary and Respiratory Medicine

Cite this

Park, H. W., Song, W. J., Kim, S. H., Park, H. K., Kim, S. H., Kwon, Y. E., ... Cho, S. H. (2015). Classification and implementation of asthma phenotypes in elderly patients. Annals of Allergy, Asthma and Immunology, 114(1), 18-22. https://doi.org/10.1016/j.anai.2014.09.020
Park, Heung Woo ; Song, Woo Jung ; Kim, Sae Hoon ; Park, Hye Kyung ; Kim, Sang Heon ; Kwon, Yong Eun ; Kwon, Hyouk Soo ; Kim, Tae Bum ; Chang, Yoon Seok ; Cho, You Sook ; Lee, Byung Jae ; Jee, Young Koo ; Jang, An Soo ; Nahm, Dong Ho ; Park, Jungwon ; Yoon, Ho Joo ; Cho, Young Joo ; Choi, Byoung Whui ; Moon, Hee Bom ; Cho, Sang Heon. / Classification and implementation of asthma phenotypes in elderly patients. In: Annals of Allergy, Asthma and Immunology. 2015 ; Vol. 114, No. 1. pp. 18-22.
@article{67c06e10fd68454f8513c166787eaba6,
title = "Classification and implementation of asthma phenotypes in elderly patients",
abstract = "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.",
author = "Park, {Heung Woo} and Song, {Woo Jung} and Kim, {Sae Hoon} and Park, {Hye Kyung} and Kim, {Sang Heon} and Kwon, {Yong Eun} and Kwon, {Hyouk Soo} and Kim, {Tae Bum} and Chang, {Yoon Seok} and Cho, {You Sook} and Lee, {Byung Jae} and Jee, {Young Koo} and Jang, {An Soo} and Nahm, {Dong Ho} and Jungwon Park and Yoon, {Ho Joo} and Cho, {Young Joo} and Choi, {Byoung Whui} and Moon, {Hee Bom} and Cho, {Sang Heon}",
year = "2015",
month = "1",
day = "1",
doi = "10.1016/j.anai.2014.09.020",
language = "English",
volume = "114",
pages = "18--22",
journal = "Annals of Allergy, Asthma and Immunology",
issn = "1081-1206",
publisher = "American College of Allergy, Asthma and Immunology",
number = "1",

}

Park, HW, Song, WJ, Kim, SH, Park, HK, Kim, SH, Kwon, YE, Kwon, HS, Kim, TB, Chang, YS, Cho, YS, Lee, BJ, Jee, YK, Jang, AS, Nahm, DH, Park, J, Yoon, HJ, Cho, YJ, Choi, BW, Moon, HB & Cho, SH 2015, 'Classification and implementation of asthma phenotypes in elderly patients', Annals of Allergy, Asthma and Immunology, vol. 114, no. 1, pp. 18-22. https://doi.org/10.1016/j.anai.2014.09.020

Classification and implementation of asthma phenotypes in elderly patients. / Park, Heung Woo; Song, Woo Jung; Kim, Sae Hoon; Park, Hye Kyung; Kim, Sang Heon; Kwon, Yong Eun; Kwon, Hyouk Soo; Kim, Tae Bum; Chang, Yoon Seok; Cho, You Sook; Lee, Byung Jae; Jee, Young Koo; Jang, An Soo; Nahm, Dong Ho; Park, Jungwon; Yoon, Ho Joo; Cho, Young Joo; Choi, Byoung Whui; Moon, Hee Bom; Cho, Sang Heon.

In: Annals of Allergy, Asthma and Immunology, Vol. 114, No. 1, 01.01.2015, p. 18-22.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Classification and implementation of asthma phenotypes in elderly patients

AU - Park, Heung Woo

AU - Song, Woo Jung

AU - Kim, Sae Hoon

AU - Park, Hye Kyung

AU - Kim, Sang Heon

AU - Kwon, Yong Eun

AU - Kwon, Hyouk Soo

AU - Kim, Tae Bum

AU - Chang, Yoon Seok

AU - Cho, You Sook

AU - Lee, Byung Jae

AU - Jee, Young Koo

AU - Jang, An Soo

AU - Nahm, Dong Ho

AU - Park, Jungwon

AU - Yoon, Ho Joo

AU - Cho, Young Joo

AU - Choi, Byoung Whui

AU - Moon, Hee Bom

AU - Cho, Sang Heon

PY - 2015/1/1

Y1 - 2015/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84918814112&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84918814112&partnerID=8YFLogxK

U2 - 10.1016/j.anai.2014.09.020

DO - 10.1016/j.anai.2014.09.020

M3 - Article

VL - 114

SP - 18

EP - 22

JO - Annals of Allergy, Asthma and Immunology

JF - Annals of Allergy, Asthma and Immunology

SN - 1081-1206

IS - 1

ER -