2014
DOI: 10.1136/thoraxjnl-2013-203601
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Cluster analysis in the COPDGene study identifies subtypes of smokers with distinct patterns of airway disease and emphysema

Abstract: Background There is notable heterogeneity in the clinical presentation of patients with COPD. To characterize this heterogeneity, we sought to identify subgroups of smokers by applying cluster analysis to data from the COPDGene Study. Methods We applied a clustering method, k-means, to data from 10,192 smokers in the COPDGene Study. After splitting the sample into a training and validation set, we evaluated three sets of input features across a range of k (user-specified number of clusters). Stable solutions… Show more

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Cited by 140 publications
(152 citation statements)
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“…In the field of personalised medicine, metabotyping may play a role by stratifying patients for the development of tailored healthcare solutions (45) . Examples of this concept are also available from the medical literature where cluster analysis has been used to identify different phenotypes for a range of diseases including chronic obstructive pulmonary disease (46) and Parkinsons disease (47) . This concept has been particularly useful for multisymtomatic and multifactorial diseases where presentation and severity of the disease can differ from one individual to the next.…”
Section: Moving From Individuals To Groups: a Novel Use Of Phenotypinmentioning
confidence: 99%
“…In the field of personalised medicine, metabotyping may play a role by stratifying patients for the development of tailored healthcare solutions (45) . Examples of this concept are also available from the medical literature where cluster analysis has been used to identify different phenotypes for a range of diseases including chronic obstructive pulmonary disease (46) and Parkinsons disease (47) . This concept has been particularly useful for multisymtomatic and multifactorial diseases where presentation and severity of the disease can differ from one individual to the next.…”
Section: Moving From Individuals To Groups: a Novel Use Of Phenotypinmentioning
confidence: 99%
“…They found that B-cell-related genes were significantly enriched in emphysema (compared with COPD patients without emphysema), paving the way for differential therapeutic research on inflammatory pathways of the adaptive immune response. 5) Two COPD studies demonstrated the utility of unsupervised k-means clustering by identifying robust cluster associations with clinical characteristics and known COPD genetic variants [95,96]. 6) Very recently, ROSS et al [97] introduced a new Bayesian method for COPD subtyping.…”
Section: Current Applications Of Systems Approaches In Respiratory Mementioning
confidence: 99%
“…Although being called one disease, clinicians believe that COPD is a heterogeneous disease and the discovery of different disease subtypes (clusters) can lead to tailored medical care for each patient [1]. The dataset we use consists of a wide range of features extracted from patients, including their demographics, clinical information, lung function measurements, and variables extracted from computed tomography (CT) chest images.…”
Section: Motivating Problem: Disease Subtypingmentioning
confidence: 99%
“…In a recent seminal work [1], instead of manually defining COPD subtypes, some COPD investigators apply K-means clustering using a feature set they consider as important and generate a clustering solution that has significant association with some key genetic variables. This work demonstrates that it is possible to define meaningful COPD subtypes using data-driven approaches, such as clustering algorithms.…”
Section: Goldmentioning
confidence: 99%
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