دورية أكاديمية
Phenotyping of atrial fibrillation with cluster analysis and external validation
| العنوان: | Phenotyping of atrial fibrillation with cluster analysis and external validation |
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| المؤلفون: | Saito, Yuki, Omae, Yuto, Nagashima, Koichi, Miyauchi, Katsumi, Nishizaki, Yuji, Miyazaki, Sakiko, Hayashi, Hidemori, Nojiri, Shuko, Daida, Hiroyuki, Minamino, Tohru, Okumura, Yasuo |
| بيانات النشر: | BMJ Publishing Group Ltd |
| سنة النشر: | 2023 |
| المجموعة: | HighWire Press (Stanford University) |
| مصطلحات موضوعية: | Arrhythmias and sudden death |
| الوصف: | Objectives Atrial fibrillation (AF) is a heterogeneous condition. We performed a cluster analysis in a cohort of patients with AF and assessed the prognostic implication of the identified cluster phenotypes. Methods We used two multicentre, prospective, observational registries of AF: the SAKURA AF registry (Real World Survey of Atrial Fibrillation Patients Treated with Warfarin and Non-vitamin K Antagonist Oral Anticoagulants) (n=3055, derivation cohort) and the RAFFINE registry (Registry of Japanese Patients with Atrial Fibrillation Focused on anticoagulant therapy in New Era) (n=3852, validation cohort). Cluster analysis was performed by the K-prototype method with 14 clinical variables. The endpoints were all-cause mortality and composite cardiovascular events. Results The analysis subclassified derivation cohort patients into five clusters. Cluster 1 (n=414, 13.6%) was characterised by younger men with a low prevalence of comorbidities; cluster 2 (n=1003, 32.8%) by a high prevalence of hypertension; cluster 3 (n=517, 16.9%) by older patients without hypertension; cluster 4 (n=652, 21.3%) by the oldest patients, who were mainly female and with a high prevalence of heart failure history; and cluster 5 (n=469, 15.3%) by older patients with high prevalence of diabetes and ischaemic heart disease. During follow-up, the risk of all-cause mortality and composite cardiovascular events increased across clusters (log-rank p<0.001, p<0.001). Similar results were found in the external validation cohort. Conclusions Machine learning-based cluster analysis identified five different phenotypes of AF with unique clinical characteristics and different clinical outcomes. The use of these phenotypes may help identify high-risk patients with AF. |
| نوع الوثيقة: | text |
| وصف الملف: | text/html |
| اللغة: | English |
| العلاقة: | http://heart.bmj.com/cgi/content/short/109/23/1751Test; http://dx.doi.org/10.1136/heartjnl-2023-322447Test |
| DOI: | 10.1136/heartjnl-2023-322447 |
| الإتاحة: | https://doi.org/10.1136/heartjnl-2023-322447Test http://heart.bmj.com/cgi/content/short/109/23/1751Test |
| حقوق: | Copyright (C) 2023, BMJ Publishing Group Ltd |
| رقم الانضمام: | edsbas.E94640D7 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1136/heartjnl-2023-322447 |
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