Characteristics and clinical outcomes in atrial fibrillation patients classified using cluster analysis: the Fushimi AF Registry. Issue 9 (30th April 2021)

• Record Type: Journal Article
• Authors: Ogawa, Hisashi; An, Yoshimori; Nishi, Hidehisa; Fukuda, Shunichi; Ishigami, Kenjiro; Ikeda, Syuhei; Doi, Kosuke; Ide, Yuya; Hamatani, Yasuhiro; Fujino, Akiko; Ishii, Mitsuru; Iguchi, Moritake; Masunaga, Nobutoyo; Esato, Masahiro; Tsuji, Hikari; Wada, Hiromichi; Hasegawa, Koji; Abe, Mitsuru; Tsukahara, Tetsuya; Lip, Gregory Y H; Akao, Masaharu
• Is Part Of: Europace. Volume 23:Issue 9(2021)
• Page Start: 1369
• Page End: 1379
• Publication Date: 2021-04-30
• Subjects: Atrial fibrillation -- Outcome -- Cluster analysis -- Comorbidity -- Registry; Arrhythmia -- Treatment -- Periodicals; Cardiac pacing -- Periodicals; Catheter ablation -- Periodicals; Heart -- Physiology -- Periodicals; Electrophysiology -- Periodicals; 617.4120645
• DOI: 10.1093/europace/euab079
• Languages: English
• ISSNs: 1099-5129

Abstract

Abstract: Aims: The risk of adverse events in atrial fibrillation (AF) patients was commonly stratified by risk factors or clinical risk scores. Risk factors often do not occur in isolation and are often found in multimorbidity 'clusters' which may have prognostic implications. We aimed to perform cluster analysis in a cohort of AF patients and to assess the outcomes and prognostic implications of the identified comorbidity cluster phenotypes. Methods and results: The Fushimi AF Registry is a community-based prospective survey of the AF patients in Fushimi-ku, Kyoto, Japan. Hierarchical cluster analysis was performed on 4304 patients (mean age: 73.6 years, female; 40.3%, mean CHA2 DS2 -VASc score 3.37 ± 1.69), using 42 baseline clinical characteristics. On hierarchical cluster analysis, AF patients could be categorized into six statistically driven comorbidity clusters: (i) younger ages (mean age: 48.3 years) with low prevalence of risk factors and comorbidities ( n = 209); (ii) elderly (mean age: 74.0 years) with low prevalence of risk factors and comorbidities ( n = 1301); (iii) those with high prevalence of atherosclerotic risk factors, but without atherosclerotic disease ( n = 1411); (iv) those with atherosclerotic comorbidities ( n = 440); (v) those with history of any-cause stroke ( n = 681); and (vi) the very elderly (mean age: 83.4 years) ( n = 262). Rates of all-cause mortality and major adverse cardiovascular or neurological events can be stratified by these six identified clusters (log-rank test; P < 0.001 and P < 0.001, respectively). Conclusions: We identified six clinically relevant phenotypes of AF patients on cluster analysis. These phenotypes can be associated with various types of comorbidities and associated with the incidence of clinical outcomes. Clinical Trial Registration Information: https://www.umin.ac.jp/ctr/index.htm . Unique identifier: UMIN000005834.