ArticleInternational journal of cardiology. Heart & vasculature2021
Clinical phenotypes of patients with non-valvular atrial fibrillation as defined by a cluster analysis: A report from the J-RHYTHM registry.
Article in International journal of cardiology. Heart & vasculature, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 17 citations in OpenAlex.
- Efficacy of the ABC Pathway for Integrated Care Across Phenotypes of Patients with Atrial Fibrillation: A Latent-Class Analysis Report from the mAFA-II Clinical Trial.Journal of general internal medicine · 2025Trial
- AI-Based Phenotyping of Atrial Fibrillation Through Generative Topographic Mapping: Prospective Murcia Atrial Fibrillation Project III Cohort Study.Journal of medical Internet research · 2026Observational
- 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
- 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
- Phenotypes of atrial fibrillation in a Taiwanese longitudinal cohort: Insights from an Asian perspective.Heart rhythm O2 · 2025Article
- The Application of Artificial Intelligence in Atrial Fibrillation Patients: From Detection to Treatment.Reviews in cardiovascular medicine · 2024Review
- Characteristics and outcomes in elderly patients with non-valvular atrial fibrillation and high bleeding risk: subanalysis of the J-RHYTHM Registry.Heart and vessels · 2024Article
- A Comparison of the Impact of Pharmacological Treatments on Cardioversion, Rate Control, and Mortality in Data-Driven Atrial Fibrillation Phenotypes in Critical Care.Bioengineering (Basel, Switzerland) · 2024Article
- Phenotyping of atrial fibrillation with cluster analysis and external validation.Heart (British Cardiac Society) · 2023Article
- Recent highlights on specific aspects of oral anticoagulation in difficult clinical scenarios from theInternational journal of cardiology. Heart & vasculature · 2023Article
- Assessment of the atrial fibrillation burden in Holter electrocardiogram recordings using artificial intelligence.Cardiovascular digital health journal · 2023Article
- Article
- Metabolic Inflexibility as a Pathogenic Basis for Atrial Fibrillation.International journal of molecular sciences · 2022Review
- Clinical phenotypes of atrial fibrillation: A review of machine learning applications in personalized treatment.JRSM cardiovascular diseaseReview
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Authors and funding
9 authors at 8 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAtrial fibrillation (AF) is a heterogeneous condition caused by various underlying disorders and comorbidities. A cluster analysis is a statistical technique that attempts to group populations by shared traits. Applied to AF, it could be useful in classifying the variables and complex presentations of AF into phenotypes of coherent, more tractable subpopulations.
objectivesThis study aimed to characterize the clinical phenotypes of AF using a national AF patient registry using a cluster analysis.
methodsWe used data of an observational cohort that included 7406 patients with non-valvular AF enrolled from 158 sites participating in a nationwide AF registry (J-RHYTHM). The endpoints analyzed were all-cause mortality, thromboembolisms, and major bleeding.
resultsThe optimal number of clusters was found to be 4 based on 40 characteristics. They were those with (1) a younger age and low rate of comorbidities (n = 1876), (2) a high rate of hypertension (n = 4579), (3) high bleeding risk (n = 302), and (4) prior coronary artery disease and other atherosclerotic comorbidities (n = 649). The patients in the younger/low comorbidity cluster demonstrated the lowest risk for all 3 endpoints. The atherosclerotic comorbidity cluster had significantly higher adjusted risks of total mortality (odds ratio [OR], 3.70; 95% confidence interval [CI], 2.37-5.80) and major bleeding (OR, 5.19; 95% CI, 2.58-10.9) than the younger/low comorbidity cluster.
conclusionsA cluster analysis identified 4 distinct groups of non-valvular AF patients with different clinical characteristics and outcomes. Awareness of these groupings may lead to a differentiated patient management for AF.
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