Phenotyping of atrial fibrillation with cluster analysis and external validation.
atrial fibrillation
Journal
Heart (British Cardiac Society)
ISSN: 1468-201X
Titre abrégé: Heart
Pays: England
ID NLM: 9602087
Informations de publication
Date de publication:
10 11 2023
10 11 2023
Historique:
received:
24
01
2023
accepted:
15
05
2023
medline:
13
11
2023
pubmed:
2
6
2023
entrez:
1
6
2023
Statut:
epublish
Résumé
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. 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. 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. 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.
Identifiants
pubmed: 37263768
pii: heartjnl-2023-322447
doi: 10.1136/heartjnl-2023-322447
pmc: PMC10715480
doi:
Substances chimiques
Anticoagulants
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1751-1758Commentaires et corrections
Type : CommentIn
Informations de copyright
© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
Déclaration de conflit d'intérêts
Competing interests: HD received honoraria from Novartis Pharma KK and Bayer Yakuhin; research grants from Philips Japan, FUJIFILM Holdings Corporation, Asahi Kasei, Inter Reha and Toho Holdings; a scholarship grant from Eisai and Bayer Yakuhin; and had courses endowed by Philips Japan, Resmed Japan, Fukuda Denshi and Paramount Bed Holdings. YOk received research funding from Bayer Healthcare, Daiichi Sankyo, Bristol Myers Squibb, Nippon Boehringer Ingelheim, Pfizer Japan, TORAY and Boston Scientific Japan; accepted remuneration from Bayer Healthcare, Daiichi Sankyo and Bristol Myers Squibb; and belongs to the endowed departments of Boston Scientific Japan, Abbott Medical Japan, Japan Lifeline, Medtronic Japan and Nihon Kohden.
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