ReviewBioMed research international2014
Identification of clinical phenotypes using cluster analyses in COPD patients with multiple comorbidities.
Review in BioMed research international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
What it found
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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.
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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.
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Who cites it
34 citing papers in PubMed.
- Article
- A Narrative Review of the Correlation Between Comorbidity and Acute Exacerbation of COPD Patients.International journal of chronic obstructive pulmonary disease · 2026Review
- Identification of clinically meaningful, overlapping obstructive respiratory disease subtypes via data-driven approaches in a primary care population.BMC pulmonary medicine · 2025Article
- Comorbidity profiles in chronic obstructive pulmonary disease: a multicohort study.ERJ open research · 2025Article
- Article
- Identification of two hidden clinical subgroups among men with idiopathic cryptozoospermia.Human reproduction (Oxford, England) · 2024Article
- Evaluation of data processing pipelines on real-world electronic health records data for the purpose of measuring patient similarity.PloS one · 2023Article
- Severity of Lung Function Impairment Drives Transcriptional Phenotypes of COPD and Relates to Immune and Metabolic Processes.International journal of chronic obstructive pulmonary disease · 2023Article
- Computational phenotyping of obstructive airway diseases: protocol for a systematic review.Systematic reviews · 2022Article
- Identifying subgroups of community-dwelling older adults and their prospective associations with long-term knee osteoarthritis outcomes.Clinical rheumatology · 2020Article
- Different Clusters in Patients with Chronic Obstructive Pulmonary Disease (COPD): A Two-Center Study in Brazil.International journal of chronic obstructive pulmonary disease · 2020Article
- A Machine Learning Approach for Studying the Comorbidities of Complex Diagnoses.Behavioral sciences (Basel, Switzerland) · 2019Article
- It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data.Briefings in bioinformatics · 2019Review
- Review
- Identifying clinically important COPD sub-types using data-driven approaches in primary care population based electronic health records.BMC medical informatics and decision making · 2019Article
- Comorbidities and COPD severity in a clinic-based cohort.BMC pulmonary medicine · 2018Observational
- COPD stands for complex obstructive pulmonary disease.European respiratory review : an official journal of the European Respiratory Society · 2018Review
- Is osteoarthritis one disease or a collection of many?Rheumatology (Oxford, England) · 2018Review
- Polysomnographic phenotypes and their cardiovascular implications in obstructive sleep apnoea.Thorax · 2018Observational
- Informatics and machine learning to define the phenotype.Expert review of molecular diagnostics · 2018Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chronic obstructive pulmonary disease (COPD) is characterized by persistent airflow limitation, the severity of which is assessed using forced expiratory volume in 1 sec (FEV1, % predicted). Cohort studies have confirmed that COPD patients with similar levels of airflow limitation showed marked heterogeneity in clinical manifestations and outcomes. Chronic coexisting diseases, also called comorbidities, are highly prevalent in COPD patients and likely contribute to this heterogeneity. In recent years, investigators have used innovative statistical methods (e.g., cluster analyses) to examine the hypothesis that subgroups of COPD patients sharing clinically relevant characteristics (phenotypes) can be identified. The objectives of the present paper are to review recent studies that have used cluster analyses for defining phenotypes in observational cohorts of COPD patients. Strengths and weaknesses of these statistical approaches are briefly described. Description of the phenotypes that were reasonably reproducible across studies and received prospective validation in at least one study is provided, with a special focus on differences in age and comorbidities (including cardiovascular diseases). Finally, gaps in current knowledge are described, leading to proposals for future studies.
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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.