ReviewChest2020
Machine Learning Characterization of COPD Subtypes: Insights From the COPDGene Study.
Review in Chest, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
49 citing papers in PubMed, 75 citations in OpenAlex.
- Trial
- Preclinical models of COPD: a state of the art review.The European respiratory journal · 2026Review
- Plasma metabolomic signatures enable the diagnosis and prognosis of chronic obstructive pulmonary disease.Nature communications · 2026Article
- Advancing Precision Approaches to Chronic Obstructive Pulmonary Disease.Tuberculosis and respiratory diseases · 2026Review
- Wearable Sleep Measures May Improve Machine Learning Prediction of Home-Based Pulmonary Rehabilitation Engagement Among Patients With Chronic Obstructive Pulmonary Disease: A Proof-of-Concept Study.Mayo Clinic proceedings. Digital health · 2026Article
- Joint clinical and molecular subtyping of COPD with variational autoencoders.Nature communications · 2026Article
- New approaches to uncover COPD pathobiology and develop therapies.JCI insight · 2026Review
- Integrative multi-omic analyses identify major axes of heterogeneity in chronic obstructive pulmonary disease and uncover their molecular contributors.medRxiv : the preprint server for health sciences · 2026Article
- Construction and Validation of Active Case-Finding Tool in Community Participants with Chronic Obstructive Pulmonary Disease Using an Interpretable Machine Learning Approach.International journal of chronic obstructive pulmonary disease · 2026Article
- Mechanical Forces and Mechanotransduction in COPD: Pathogenesis, Clinical Phenotypes, and Therapeutic Implications.International journal of chronic obstructive pulmonary disease · 2026Review
- Airway microbiota and immunity associated with chronic obstructive pulmonary disease severity.Journal of translational medicine · 2025Article
- A comprehensive investigation of the relationship between dietary fatty acid intake and preserved ratio impaired spirometry: multimethodology based on NHANES.Lipids in health and disease · 2025Article
- Deep learning integration of chest computed tomography and plasma proteomics to identify novel aspects of severe COVID-19 pneumonia.Journal of intensive medicine · 2025Article
- Pharmacogenetic Factors Shaping Treatment Outcomes in Chronic Obstructive Pulmonary Disease.Genes · 2025Review
- Identifying chronic obstructive pulmonary disease subtypes using multi-trait genetics.EBioMedicine · 2025Article
- Quantitative CT and COPD: cluster analysis reveals five distinct subtypes with varying exacerbation risks.BMC pulmonary medicine · 2025Article
- Developing an interpretable machine learning predictive model of chronic obstructive pulmonary disease by serum PFAS concentration.Frontiers in public health · 2025Article
- Differentiating Emphysema From Emphysema-Dominated COPD Patients with CT Imaging Feature and Machine Learning.International journal of chronic obstructive pulmonary disease · 2025Article
- Differential Association of COPD Subtypes With Cardiovascular Events and COPD Exacerbations.Chest · 2024Article
- Artificial intelligence in COPD CT images: identification, staging, and quantitation.Respiratory research · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
21 authors at 8 institutions in 1 country.
Funding
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.