Abstract
Chronic Obstructive Pulmonary Disease (COPD) is a highly heterogeneous condition projected to become the third leading cause of death worldwide by 2030. To better characterize this condition, clinicians have classified patients sharing certain symptomatic characteristics, such as symptom intensity and history of exacerbations, into distinct phenotypes. In recent years, the growing use of machine learning algorithms, and cluster analysis in particular, has promised to advance this classification through the integration of additional patient characteristics, including comorbidities, biomarkers, and genomic information. This combination would allow researchers to more reliably identify new COPD phenotypes, as well as better characterize existing ones, with the aim of improving diagnosis and developing novel treatments. Here, we systematically review the last decade of research progress, which uses cluster analysis to identify COPD phenotypes. Collectively, we provide a systematized account of the extant evidence, describe the strengths and weaknesses of the main methods used, identify gaps in the literature, and suggest recommendations for future research
Original language | English |
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Article number | 106093 |
Pages (from-to) | 106093 |
Number of pages | 20 |
Journal | Respiratory Medicine |
Volume | 171 |
Early online date | 28 Jul 2020 |
DOIs | |
Publication status | Published - Sept 2020 |
Bibliographical note
FundingThis research did not receive any specific grant from funding agencies in the public, commercial, or ot-for-profit sectors.
Keywords
- chronic respiratory disease
- subtypes
- statistical analysis
- Statistical analysis
- Chronic respiratory disease
- Subtypes
- SUBTYPES
- CLINICAL PHENOTYPES
- AIRWAYS DISEASE
- IDENTIFICATION
- MILD
- OBSTRUCTIVE PULMONARY-DISEASE
- DOUBLE-BLIND
- EXACERBATIONS
- LUNG-DISEASE
- ASTHMA