Vitolo Marco, Proietti Marco, Shantsila Alena, Boriani Giuseppe, Lip Gregory Y H
Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool L7 8TX, UK.
Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, 41125 Modena, Italy.
Biomedicines. 2021 Jul 20;9(7):843. doi: 10.3390/biomedicines9070843.
Given the great clinical heterogeneity of atrial fibrillation (AF) patients, conventional classification only based on disease subtype or arrhythmia patterns may not adequately characterize this population. We aimed to identify different groups of AF patients who shared common clinical phenotypes using cluster analysis and evaluate the association between identified clusters and clinical outcomes.
We performed a hierarchical cluster analysis in AF patients from AMADEUS and BOREALIS trials. The primary outcome was a composite of stroke/thromboembolism (TE), cardiovascular (CV) death, myocardial infarction, and/or all-cause death. Individual components of the primary outcome and major bleeding were also assessed.
We included 3980 AF patients treated with the Vitamin-K Antagonist from the AMADEUS and BOREALIS studies. The analysis identified four clusters in which patients varied significantly among clinical characteristics. Cluster 1 was characterized by patients with low rates of CV risk factors and comorbidities; Cluster 2 was characterized by patients with a high burden of CV risk factors; Cluster 3 consisted of patients with a high burden of CV comorbidities; Cluster 4 was characterized by the highest rates of non-CV comorbidities. After a mean follow-up of 365 (standard deviation 187) days, Cluster 4 had the highest cumulative risk of outcomes. Compared with Cluster 1, Cluster 4 was independently associated with an increased risk for the composite outcome (hazard ratio (HR) 2.43, 95% confidence interval (CI) 1.70-3.46), all-cause death (HR 2.35, 95% CI 1.58-3.49) and major bleeding (HR 2.18, 95% CI 1.19-3.96).
Cluster analysis identified four different clinically relevant phenotypes of AF patients that had unique clinical characteristics and different outcomes. Cluster analysis highlights the high degree of heterogeneity in patients with AF, suggesting the need for a phenotype-driven approach to comorbidities, which could provide a more holistic approach to management aimed to improve patients' outcomes.
鉴于心房颤动（AF）患者存在巨大的临床异质性，仅基于疾病亚型或心律失常模式的传统分类可能无法充分描述这一人群。我们旨在通过聚类分析识别具有共同临床表型的不同AF患者群体，并评估所识别聚类与临床结局之间的关联。
我们对来自AMADEUS和BOREALIS试验的AF患者进行了层次聚类分析。主要结局是卒中/血栓栓塞（TE）、心血管（CV）死亡、心肌梗死和/或全因死亡的复合结局。还评估了主要结局的各个组成部分和大出血情况。
我们纳入了AMADEUS和BOREALIS研究中3980例接受维生素K拮抗剂治疗的AF患者。分析确定了四个聚类，其中患者在临床特征上有显著差异。聚类1的特征是CV危险因素和合并症发生率低的患者；聚类2的特征是CV危险因素负担高的患者；聚类3由CV合并症负担高的患者组成；聚类4的特征是非CV合并症发生率最高。在平均随访365（标准差187）天后，聚类4的结局累积风险最高。与聚类1相比，聚类4与复合结局（风险比（HR）2.43，95%置信区间（CI）1.70 - 3.46）、全因死亡（HR 2.35，95% CI 1.58 - 3.49）和大出血（HR 2.18，95% CI 1.19 - 3.96）风险增加独立相关。
聚类分析确定了AF患者四种不同的临床相关表型，这些表型具有独特的临床特征和不同的结局。聚类分析突出了AF患者的高度异质性，表明需要一种基于表型的合并症处理方法，这可以为旨在改善患者结局的管理提供更全面的方法。
