ArticleBMC medical informatics and decision making2019
Identifying clinically important COPD sub-types using data-driven approaches in primary care population based electronic health records.
Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 5 of them syntheses that pooled it.
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Who cites it
43 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Chronic Obstructive Pulmonary Disease and the Management of Cardiopulmonary Risk in the UK: A Systematic Literature Review and Modified Delphi Study.International journal of chronic obstructive pulmonary disease · 2025Pooled it
- Pooled it
- Prognostic risk factors for moderate-to-severe exacerbations in patients with chronic obstructive pulmonary disease: a systematic literature review.Respiratory research · 2022Pooled it
- Pooled it
- The characteristics of the frequent exacerbator with chronic bronchitis phenotype and non-exacerbator phenotype in patients with chronic obstructive pulmonary disease: a meta-analysis and system review.BMC pulmonary medicine · 2020Pooled it
- Distribution of Muscle Mass and Fat Mass to Identify the Risk of Sarcopenia and Sarcopenic Obesity in Adults via Machine Learning.Healthcare (Basel, Switzerland) · 2026Article
- Sociodemographic differences in clinical phenotypes among patients with COPD: a latent class analysis.BMJ open respiratory research · 2026Article
- Cluster Analysis of Healthcare Utilization Patterns in Patients with Comorbid Chronic Obstructive Pulmonary Disease and Atrial Fibrillation.Journal of clinical medicine · 2026Article
- Identification of clinically meaningful, overlapping obstructive respiratory disease subtypes via data-driven approaches in a primary care population.BMC pulmonary medicine · 2025Article
- Automated Chronic Obstructive Pulmonary Disease Phenotyping and Control Assessment in Primary Care: Retrospective Multicenter Study Using the Seleida Model.JMIR medical informatics · 2025Article
- Comorbidity profiles in chronic obstructive pulmonary disease: a multicohort study.ERJ open research · 2025Article
- Respiratory Rehabilitation Index (R2I): Unsupervised Clustering Approach to Identify COPD Subgroups Associated with Rehabilitation Outcomes.Diagnostics (Basel, Switzerland) · 2025Article
- Identification of a Novel Subtype of Ocular Hypertensive Eyes with the Lowest Risk of Progression Under Treatment.Ophthalmology and therapy · 2025Article
- Machine Learning in Primary Health Care: The Research Landscape.Healthcare (Basel, Switzerland) · 2025Review
- Clustering of electronic health records in atrial fibrillation patients and impact on prognosis and patient trajectories: a UK linked-dataset study.European heart journal. Digital health · 2025Article
- Computational Phenotyping of Obstructive Airway Diseases: A Systematic Review.Journal of asthma and allergy · 2025Review
- Creating a next-generation phenotype library: the health data research UK Phenotype Library.JAMIA open · 2024Article
- The role of diet and nutrition in the management of COPD.European respiratory review : an official journal of the European Respiratory Society · 2023Review
- Article
- Evaluation of data processing pipelines on real-world electronic health records data for the purpose of measuring patient similarity.PloS one · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundCOPD is a highly heterogeneous disease composed of different phenotypes with different aetiological and prognostic profiles and current classification systems do not fully capture this heterogeneity. In this study we sought to discover, describe and validate COPD subtypes using cluster analysis on data derived from electronic health records.
methodsWe applied two unsupervised learning algorithms (k-means and hierarchical clustering) in 30,961 current and former smokers diagnosed with COPD, using linked national structured electronic health records in England available through the CALIBER resource. We used 15 clinical features, including risk factors and comorbidities and performed dimensionality reduction using multiple correspondence analysis. We compared the association between cluster membership and COPD exacerbations and respiratory and cardiovascular death with 10,736 deaths recorded over 146,466 person-years of follow-up. We also implemented and tested a process to assign unseen patients into clusters using a decision tree classifier.
resultsWe identified and characterized five COPD patient clusters with distinct patient characteristics with respect to demographics, comorbidities, risk of death and exacerbations. The four subgroups were associated with 1) anxiety/depression; 2) severe airflow obstruction and frailty; 3) cardiovascular disease and diabetes and 4) obesity/atopy. A fifth cluster was associated with low prevalence of most comorbid conditions.
conclusionsCOPD patients can be sub-classified into groups with differing risk factors, comorbidities, and prognosis, based on data included in their primary care records. The identified clusters confirm findings of previous clustering studies and draw attention to anxiety and depression as important drivers of the disease in young, female patients.
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