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基于聚类分析的房颤患者特征和临床结局:伏见房颤注册研究。

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

机构信息

Department of Cardiology, National Hospital Organization Kyoto Medical Center, 1-1, Mukaihata-cho, Fukakusa, Fushimi-ku, Kyoto 612-8555, Japan.

Department of Neurosurgery, National Hospital Organization Kyoto Medical Center, Kyoto, Japan.

出版信息

Europace. 2021 Sep 8;23(9):1369-1379. doi: 10.1093/europace/euab079.

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 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).

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.

摘要

目的

房颤(AF)患者的不良事件风险通常通过危险因素或临床风险评分进行分层。危险因素通常不是孤立发生的,而是经常出现在多病症“簇”中,这可能具有预后意义。我们旨在对一组 AF 患者进行聚类分析,并评估所确定的合并症簇表型的结果和预后意义。

方法和结果

富山 AF 登记处是日本京都富山市的一项基于社区的 AF 患者前瞻性调查。对 4304 例患者(平均年龄:73.6 岁,女性 40.3%,平均 CHA2DS2-VASc 评分 3.37±1.69)进行了 42 项基线临床特征的层次聚类分析。在层次聚类分析中,AF 患者可分为六个统计学驱动的合并症簇:(i)年龄较小(平均年龄:48.3 岁),危险因素和合并症患病率低(n=209);(ii)年龄较大(平均年龄:74.0 岁),危险因素和合并症患病率低(n=1301);(iii)高动脉粥样硬化危险因素但无动脉粥样硬化疾病的患者(n=1411);(iv)存在动脉粥样硬化合并症的患者(n=440);(v)有任何原因中风病史的患者(n=681);(vi)非常高龄的患者(平均年龄:83.4 岁)(n=262)。这些六个确定的簇可以对全因死亡率和主要不良心血管或神经事件的发生率进行分层(对数秩检验;P<0.001 和 P<0.001)。

结论

我们通过聚类分析确定了六个具有临床意义的 AF 患者表型。这些表型可以与各种类型的合并症相关,并与临床结局的发生相关。

临床试验注册信息

https://www.umin.ac.jp/ctr/index.htm。独特标识符:UMIN000005834。

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