ArticleBMC medicine2021
Impact of clinical phenotypes on management and outcomes in European atrial fibrillation patients: a report from the ESC-EHRA EURObservational Research Programme in AF (EORP-AF) General Long-Term Registry.
Article in BMC medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed, 52 citations in OpenAlex.
- AI-Based Phenotyping of Atrial Fibrillation Through Generative Topographic Mapping: Prospective Murcia Atrial Fibrillation Project III Cohort Study.Journal of medical Internet research · 2026Observational
- Quality of life, care needs and priorities in atrial fibrillation: the impact of number and patterns of comorbidities.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026Article
- Age-Related Differences in the Clinical Profile and Management of Atrial Fibrillation: Results from the Multicentre REGUEIFA Registry.Journal of clinical medicine · 2026Article
- Additional Stroke Risk Factors Beyond the CHAJournal of clinical medicine · 2026Article
- Deriving novel atrial fibrillation phenotypes using a tree-based artificial intelligence-enhanced electrocardiography approach.NPJ digital medicine · 2025Article
- Comorbidity patterns and mortality in atrial fibrillation: a latent class analysis of the EURopean study of Older Subjects with Atrial Fibrillation (EUROSAF).Annals of medicine · 2025Article
- Patterns of comorbidities, clinical course, and impact of the ABC Pathway for Integrated Care in patients with atrial fibrillation: a report from the prospective Optimal Thromboprophylaxis in Elderly Chinese Patients with Atrial Fibrillation (ChiOTEAF) Registry.European heart journal. Quality of care & clinical outcomes · 2025Article
- 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
- Clinical phenotypes of severe atrial cardiomyopathy and their outcome: A cluster analysis.International journal of cardiology. Heart & vasculature · 2025Article
- Development and validation of risk stratification and shared decision-making tool for catheter ablation for atrial fibrillation in patients with heart failure: a multicentre cohort study.EClinicalMedicine · 2025Article
- Media consumption patterns and depressive and anxiety symptoms in the Chinese general population during the COVID-19 outbreak.World journal of psychiatry · 2025Article
- Evaluation of the ABC pathway in patients with atrial fibrillation: A machine learning cluster analysis.International journal of cardiology. Heart & vasculature · 2025Article
- Multimorbidity Is Associated With Symptom Severity and Disease Progression in Patients with Paroxysmal Atrial Fibrillation-Data From the RACE V Study.Journal of the American Heart Association · 2025Article
- Review
- Outcomes in Atrial Fibrillation Patients with Different Clinical Phenotypes: Insights from the French Population.Journal of clinical medicine · 2025Article
- Phenotypes of South Asian patients with atrial fibrillation and holistic integrated care management: cluster analysis of data from KERALA-AF Registry.The Lancet regional health. Southeast Asia · 2024Article
- The absolute lactate levels versus clearance for prognostication of post-cardiotomy patients on veno-arterial ECMO.ESC heart failure · 2024Article
- Impacts of Five Different Drying Methods on Volatile Organic Compounds in Mulberry Fruits.Foods (Basel, Switzerland) · 2024Article
- The Application of Artificial Intelligence in Atrial Fibrillation Patients: From Detection to Treatment.Reviews in cardiovascular medicine · 2024Review
- Article
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Authors and funding
12 authors at 9 institutions in 8 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEpidemiological studies in atrial fibrillation (AF) illustrate that clinical complexity increase the risk of major adverse outcomes. We aimed to describe European AF patients' clinical phenotypes and analyse the differential clinical course.
methodsWe performed a hierarchical cluster analysis based on Ward's Method and Squared Euclidean Distance using 22 clinical binary variables, identifying the optimal number of clusters. We investigated differences in clinical management, use of healthcare resources and outcomes in a cohort of European AF patients from a Europe-wide observational registry.
resultsA total of 9363 were available for this analysis. We identified three clusters: Cluster 1 (n = 3634; 38.8%) characterized by older patients and prevalent non-cardiac comorbidities; Cluster 2 (n = 2774; 29.6%) characterized by younger patients with low prevalence of comorbidities; Cluster 3 (n = 2955;31.6%) characterized by patients' prevalent cardiovascular risk factors/comorbidities. Over a mean follow-up of 22.5 months, Cluster 3 had the highest rate of cardiovascular events, all-cause death, and the composite outcome (combining the previous two) compared to Cluster 1 and Cluster 2 (all P < .001). An adjusted Cox regression showed that compared to Cluster 2, Cluster 3 (hazard ratio (HR) 2.87, 95% confidence interval (CI) 2.27-3.62; HR 3.42, 95%CI 2.72-4.31; HR 2.79, 95%CI 2.32-3.35), and Cluster 1 (HR 1.88, 95%CI 1.48-2.38; HR 2.50, 95%CI 1.98-3.15; HR 2.09, 95%CI 1.74-2.51) reported a higher risk for the three outcomes respectively.
conclusionsIn European AF patients, three main clusters were identified, differentiated by differential presence of comorbidities. Both non-cardiac and cardiac comorbidities clusters were found to be associated with an increased risk of major adverse outcomes.
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