ArticleInternational journal of chronic obstructive pulmonary disease2019
Prediction of first acute exacerbation using COPD subtypes identified by cluster analysis.
Article in International journal of chronic obstructive pulmonary disease, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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
14 citing papers in PubMed, 1 synthesis or guideline 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
- Irisin, BMI, and SII as Predictors of Frequent Acute Exacerbations in COPD: Development of a Predictive Nomogram.International journal of chronic obstructive pulmonary disease · 2026Observational
- Clinical and Paraclinical Characteristics and Predictive Factors of Chronic Obstructive Pulmonary Exacerbation.Materia socio-medica · 2025Article
- Computational Phenotyping of Obstructive Airway Diseases: A Systematic Review.Journal of asthma and allergy · 2025Review
- A Delphi Consensus Project to Capture Greek Experts' Opinion on the Position of Triple Therapies in COPD: Why, When and to Whom.International journal of chronic obstructive pulmonary disease · 2025Article
- Clinical Characteristics and Frequency of Chronic Obstructive Pulmonary Disease Exacerbations in Korean Patients: Findings From the KOCOSS Cohort 2012-2021.Journal of Korean medical science · 2024Article
- Comparison of STAR and GOLD in Assessing Disease Severity Among High-Risk and COPD Patients: Evidence from Enjoying Breathing Program in China.International journal of chronic obstructive pulmonary disease · 2024Article
- Cluster features in fibrosing interstitial lung disease and associations with prognosis.BMC pulmonary medicine · 2023Article
- Prediction value of neutrophil and eosinophil count at risk of COPD exacerbation.Annals of medicine · 2023Article
- Evaluation of data processing pipelines on real-world electronic health records data for the purpose of measuring patient similarity.PloS one · 2023Article
- Stratification of COPD patients towards personalized medicine: reproduction and formation of clusters.Respiratory research · 2022Article
- Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease.International journal of medical sciences · 2021Review
- Cluster Analysis: A New Approach for Identification of Underlying Risk Factors and Demographic Features of First Trimester Pregnancy Women.Journal of clinical medicine · 2020Article
- Neurological and Psychiatric Comorbidities in Chronic Obstructive Pulmonary Disease.International journal of chronic obstructive pulmonary diseaseArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: In patients with COPD, acute exacerbation (AE) is not only an important determinant of prognosis, but also an important factor in choosing therapeutic agents. In this study, we evaluated the usefulness of COPD subtypes identified through cluster analysis to predict the first AE. Patients and methods: Among COPD patients in the Korea COPD Subgroup Study (KOCOSS) cohort, 1,195 who had follow-up data for AE were included in our study. We selected seven variables for cluster analysis - age, body mass index, smoking status, history of asthma, COPD assessment test (CAT) score, post-bronchodilator (BD) FEV Results: K-means clustering identified four clusters for COPD that we named putative asthma-COPD overlap (ACO), mild COPD, moderate COPD, and severe COPD subtypes. The ACO group (n=196) showed the second-best post-BD FEV Conclusion: Our cluster analysis revealed an exacerbator subtype of COPD independent of FEV
Indexed as
Identifiers
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.