Journal article
Machine Learning Characterization of COPD Subtypes: Insights From the COPDGene Study
Chest, Vol.157(5), pp.1147-1157
05/2020
DOI: 10.1016/j.chest.2019.11.039
PMCID: PMC7242638
PMID: 31887283
Abstract
COPD is a heterogeneous syndrome. Many COPD subtypes have been proposed, but there is not yet consensus on how many COPD subtypes there are and how they should be defined. The COPD Genetic Epidemiology Study (COPDGene), which has generated 10-year longitudinal chest imaging, spirometry, and molecular data, is a rich resource for relating COPD phenotypes to underlying genetic and molecular mechanisms. In this article, we place COPDGene clustering studies in context with other highly cited COPD clustering studies, and summarize the main COPD subtype findings from COPDGene. First, most manifestations of COPD occur along a continuum, which explains why continuous aspects of COPD or disease axes may be more accurate and reproducible than subtypes identified through clustering methods. Second, continuous COPD-related measures can be used to create subgroups through the use of predictive models to define cut-points, and we review COPDGene research on blood eosinophil count thresholds as a specific example. Third, COPD phenotypes identified or prioritized through machine learning methods have led to novel biological discoveries, including novel emphysema genetic risk variants and systemic inflammatory subtypes of COPD. Fourth, trajectory-based COPD subtyping captures differences in the longitudinal evolution of COPD, addressing a major limitation of clustering analyses that are confounded by disease severity. Ongoing longitudinal characterization of subjects in COPDGene will provide useful insights about the relationship between lung imaging parameters, molecular markers, and COPD progression that will enable the identification of subtypes based on underlying disease processes and distinct patterns of disease progression, with the potential to improve the clinical relevance and reproducibility of COPD subtypes.
Details
- Title: Subtitle
- Machine Learning Characterization of COPD Subtypes: Insights From the COPDGene Study
- Creators
- Peter J Castaldi - Brigham and Women's HospitalAdel Boueiz - Brigham and Women's HospitalJeong Yun - Brigham and Women's HospitalRaul San Jose Estepar - Brigham and Women's HospitalJames C Ross - Brigham and Women's HospitalGeorge Washko - Brigham and Women's HospitalMichael H Cho - Brigham and Women's HospitalCraig P Hersh - Brigham and Women's HospitalGregory L Kinney - University of Colorado DenverKendra A Young - University of Colorado DenverElizabeth A Regan - National Jewish HealthDavid A Lynch - Department of Radiology, National Jewish Health, Denver, COGerald J Criner - Temple UniversityJennifer G Dy - Northeastern UniversityStephen I Rennard - University of Nebraska Medical CenterRichard Casaburi - The Lundquist InstituteBarry J Make - National Jewish HealthJames Crapo - National Jewish HealthEdwin K Silverman - Brigham and Women's HospitalJohn E Hokanson - University of Colorado DenverCOPDGene Investigators
- Contributors
- Eric A Hoffman (Contributor) - University of Iowa, Radiology
- Resource Type
- Journal article
- Publication Details
- Chest, Vol.157(5), pp.1147-1157
- DOI
- 10.1016/j.chest.2019.11.039
- PMID
- 31887283
- PMCID
- PMC7242638
- NLM abbreviation
- Chest
- ISSN
- 0012-3692
- eISSN
- 1931-3543
- Grant note
- R01 HL124233 / NHLBI NIH HHS U01 HL089856 / NHLBI NIH HHS K08 HL141601 / NHLBI NIH HHS U01 HL089897 / NHLBI NIH HHS K08 HL146972 / NHLBI NIH HHS S10 OD018526 / NIH HHS
- Language
- English
- Date published
- 05/2020
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Psychiatry; Internal Medicine
- Record Identifier
- 9984318803102771
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