Hsu Jung-Chi, Yang Yen-Yun, Chuang Shu-Lin, Lin Lian-Yu
Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital, Jinshan Branch, New Taipei City, Taiwan.
Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan.
Heart Rhythm O2. 2024 Nov 19;6(2):129-138. doi: 10.1016/j.hroo.2024.11.009. eCollection 2025 Feb.
Atrial fibrillation (AF) is a condition with heterogeneous underlying causes, often involving multiple cardiovascular comorbidities. Large-scale studies examining the heterogeneity of patients with AF in the Asian population are limited.
The purpose of this study was to identify distinct phenotypic clusters of patients with AF and evaluate their associated risks of ischemic stroke, heart failure hospitalization, cardiovascular mortality, and all-cause mortality.
We analyzed 5002 adult patients with AF from the National Taiwan University Hospital between 2014 and 2019 using an unsupervised hierarchical cluster analysis based on the CHADS-VASc score.
We identified 4 distinct groups of patients with AF: cluster I included diabetic patients with heart failure preserved ejection fraction as well as chronic kidney disease (CKD); cluster II comprised older patients with low body mass index and pulmonary hypertension; cluster III consisted of patients with metabolic syndrome and atherosclerotic disease; and cluster IV comprised patients with left heart dysfunction, including reduced ejection fraction. Differences in the risk of ischemic stroke across clusters (clusters I, II, and III vs cluster IV) were statistically significant (hazard ratio [HR] 1.87, 95% confidence interval [CI] 1.00-3.48; HR 2.06, 95% CI 1.06-4.01; and HR 1.70, 95% CI 1.02-2.01). Cluster II was independently associated with the highest risk of hospitalization for heart failure (HR 1.19, 95% CI 0.79-1.80), cardiovascular mortality (HR 2.51, 95% CI 1.21-5.22), and overall mortality (HR 2.98, 95% CI 1.21-4.2).
A data-driven algorithm can identify distinct clusters with unique phenotypes and varying risks of cardiovascular outcomes in patients with AF, enhancing risk stratification beyond the CHADS-VASc score.
心房颤动(AF)病因多样,常伴有多种心血管合并症。针对亚洲人群房颤患者异质性的大规模研究有限。
本研究旨在识别房颤患者的不同表型聚类,并评估其发生缺血性卒中、心力衰竭住院、心血管死亡和全因死亡的相关风险。
我们对2014年至2019年间台湾大学附属医院的5002例成年房颤患者进行分析,采用基于CHADS-VASc评分的无监督分层聚类分析。
我们识别出4组不同的房颤患者:聚类I包括射血分数保留的心力衰竭合并糖尿病以及慢性肾脏病(CKD)患者;聚类II由低体重指数和肺动脉高压的老年患者组成;聚类III由代谢综合征和动脉粥样硬化疾病患者组成;聚类IV由左心功能不全患者组成,包括射血分数降低患者。各聚类间缺血性卒中风险存在差异(聚类I、II和III与聚类IV相比),具有统计学意义(风险比[HR]1.87,95%置信区间[CI]1.00-3.48;HR 2.06,95%CI 1.06-4.01;HR 1.70,95%CI 1.02-2.01)。聚类II与心力衰竭住院(HR 1.19,95%CI 0.79-1.80)、心血管死亡(HR 2.51,95%CI 1.21-5.22)和全因死亡(HR 2.98,95%CI 1.21-4.2)的最高风险独立相关。
数据驱动算法可识别出房颤患者具有独特表型和不同心血管结局风险的不同聚类,超越CHADS-VASc评分增强风险分层。