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

doi:10.1016/j.anai.2014.09.020

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

Department of Internal Medicine

Research output: Contribution to journal › Article › peer-review

42 Scopus citations

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 language	English
Pages (from-to)	18-22
Number of pages	5
Journal	Annals of Allergy, Asthma and Immunology
Volume	114
Issue number	1
DOIs	https://doi.org/10.1016/j.anai.2014.09.020
State	Published - 1 Jan 2015

Bibliographical note

Funding Information:
Funding: This work was supported financially by grants 2008-E33028-00 , 2009-E33022-00 , and 2011-E33005-00 from the Ministry of Health and Welfare , Republic of Korea.

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

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10.1016/j.anai.2014.09.020

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Park, H. W., Song, W. J., Kim, S. H., Park, H. K., Kim, S. H., Kwon, Y. E., Kwon, H. S., Kim, T. B., Chang, Y. S., Cho, Y. S., Lee, B. J., Jee, Y. K., Jang, A. S., Nahm, D. H., Park, J. W., Yoon, H. J., Cho, Y. J., Choi, B. W., Moon, H. B., & 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

@article{333c0d7e63324e679d8497eab496574b,

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 Park, {Jung Won} and Yoon, {Ho Joo} and Cho, {Young Joo} and Choi, {Byoung Whui} and Moon, {Hee Bom} and Cho, {Sang Heon}",

note = "Funding Information: Funding: This work was supported financially by grants 2008-E33028-00 , 2009-E33022-00 , and 2011-E33005-00 from the Ministry of Health and Welfare , Republic of Korea. Publisher Copyright: {\textcopyright} 2015 American College of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.",

year = "2015",

month = jan,

day = "1",

doi = "10.1016/j.anai.2014.09.020",

language = "English",

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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, JW, 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

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, Jung Won

AU - Yoon, Ho Joo

AU - Cho, Young Joo

AU - Choi, Byoung Whui

AU - Moon, Hee Bom

AU - Cho, Sang Heon

N1 - Funding Information: Funding: This work was supported financially by grants 2008-E33028-00 , 2009-E33022-00 , and 2011-E33005-00 from the Ministry of Health and Welfare , Republic of Korea. Publisher Copyright: © 2015 American College of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

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.

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Classification and implementation of asthma phenotypes in elderly patients

Abstract

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