Characteristics and clinical outcomes in atrial fibrillation patients classified using cluster analysis: the Fushimi AF Registry

医学 心房颤动 共病 内科学 星团(航天器) 队列 冲程(发动机) 机械工程 计算机科学 工程类 程序设计语言
作者
Hisashi Ogawa,Yoshimori An,Hidehisa Nishi,Shunichi Fukuda,Kenjiro Ishigami,Syuhei Ikeda,Kosuke Doi,Yuya Ide,Yasuhiro Hamatani,Akiko Fujino,Mitsuru Ishii,Moritake Iguchi,Nobutoyo Masunaga,Masahiro Esato,Hikari Tsuji,Hiromichi Wada,Koji Hasegawa,Mitsuru Abe,Tetsuya Tsukahara,Gregory Y.H. Lip,Masaharu Akao
出处
期刊:Europace [Oxford University Press]
卷期号:23 (9): 1369-1379 被引量:19
标识
DOI:10.1093/europace/euab079
摘要

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.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 CHA2DS2-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).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.https://www.umin.ac.jp/ctr/index.htm. Unique identifier: UMIN000005834.
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