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Exploring COPD patient clusters and associations with health-related quality of life using a machine learning approach: A Nationwide Cross-Sectional Study

Chronic obstructive pulmonary disease (COPD) is a complex condition marked by considerable interindividual heterogeneity. Comorbidities exacerbate this variability, worsening disease severity and reducing health-related quality of life (HRQoL). Despite the high prevalence of COPD in China, COPD pati...

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Main Authors: Wang, Chao (Author) , Yu, Fengyun (Author) , Cao, Zhong (Author) , Huang, Ke (Author) , Chen, Qiushi (Author) , Geldsetzer, Pascal (Author) , Zhao, Jinghan (Author) , Zheng, Zhoude (Author) , Bärnighausen, Till (Author) , Yang, Ting (Author) , Chen, Simiao (Author) , Wang, Chen (Author)
Format: Article (Journal)
Language:English
Published: 19 May 2025
In: Engineering
Year: 2025, Volume: 50, Pages: 220-228
ISSN:2096-0026
DOI:10.1016/j.eng.2025.05.005
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1016/j.eng.2025.05.005
Verlag, kostenfrei, Volltext: https://www.sciencedirect.com/science/article/pii/S2095809925002565
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Author Notes:Chao Wang, Fengyun Yu, Zhong Cao, Ke Huang, Qiushi Chen, Pascal Geldsetzer, Jinghan Zhao, Zhoude Zheng, Till Bärnighausen, Ting Yang, Simiao Chen, Chen Wang
Description
Summary:Chronic obstructive pulmonary disease (COPD) is a complex condition marked by considerable interindividual heterogeneity. Comorbidities exacerbate this variability, worsening disease severity and reducing health-related quality of life (HRQoL). Despite the high prevalence of COPD in China, COPD patient clusters remain poorly characterized. This study aimed to identify and validate clusters of Chinese patients with COPD, characterized primarily by comorbidity profiles, using cluster analysis. This cross-sectional, multicenter cohort study used data from the Chinese Enjoying Breathing Program (2020-2023). HRQoL was measured using the EuroQol five dimension (EQ-5D). Dimension reduction was performed via multiple correspondence analysis on 31 variables, including indicators of 27 comorbidities and four socio-demographic or health-related characteristics. Unsupervised machine learning algorithms, K-means++, and hierarchical clustering identified distinct clusters. Robustness was assessed using random forest classification. Logistic regression evaluated the association between cluster membership and EQ-5D outcomes. Among 11 145 patients, 59.4% had comorbidities. Four clusters emerged: young male smokers, biomass-exposed females, respiratory comorbidity, and elderly multimorbid. The last two clusters had notably lower HRQoL. Cluster analysis identified four clinically meaningful COPD patient clusters based on comorbidities and risk profiles, each with distinct HRQoL outcomes. These findings support targeted public health interventions and integrated care strategies for COPD management.
Item Description:Gesehen am 12.03.2026
Physical Description:Online Resource
ISSN:2096-0026
DOI:10.1016/j.eng.2025.05.005

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