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. 2023 Nov 10;109(23):1751-1758.
doi: 10.1136/heartjnl-2023-322447.

Phenotyping of atrial fibrillation with cluster analysis and external validation

Yuki Saito  1 , Yuto Omae  2 , Koichi Nagashima  3 , Katsumi Miyauchi  4 , Yuji Nishizaki  4 , Sakiko Miyazaki  4 , Hidemori Hayashi  4 , Shuko Nojiri  5 , Hiroyuki Daida  4 , Tohru Minamino  4 , Yasuo Okumura  3
Affiliations

Phenotyping of atrial fibrillation with cluster analysis and external validation

Yuki Saito et al. Heart. .

Abstract

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.

Keywords: atrial fibrillation.

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Conflict of interest statement

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.

Figures

Figure 1
Figure 1
Description of characteristics in each cluster. AF, atrial fibrillation; BMI, body mass index; BNP, B-type natriuretic peptide; CKD, chronic kidney disease; IHD, ischaemic heart disease.
Figure 2
Figure 2
Kaplan-Meier curves for the incidence of all-cause mortality (A) and composite events (B) during the follow-up period according to the clusters in the derivation cohort (SAKURA AF registry). SAKURA AF, Real World Survey of Atrial Fibrillation Patients Treated with Warfarin and Non-vitamin K Antagonist Oral Anticoagulants.
Figure 3
Figure 3
Kaplan-Meier curves for the incidence of all-cause mortality (A) and composite events (B) during the follow-up period according to the clusters in the external validation cohort (RAFFINE registry). RAFFINE, Registry of Japanese Patients with Atrial Fibrillation Focused on anticoagulant therapy in New Era.
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Comment in

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