共 50 条
Phenotypes and outcomes in non-anticoagulated patients with atrial fibrillation: An unsupervised cluster analysis
被引:5
|作者:
Bisson, Arnaud
[1
,2
,3
,4
,5
,10
]
Fawzy, Ameenathul M.
[4
,5
]
Romiti, Giulio Francesco
[4
,5
,6
]
Proietti, Marco
[4
,5
,7
,8
]
Angoulvant, Denis
[1
,3
]
El-Bouri, Wahbi
[4
,5
]
Lip, Gregory Y. H.
[4
,5
,9
]
Fauchier, Laurent
[1
]
机构:
[1] Ctr Hosp Reg Univ, Fac Med Tours, Serv Cardiol, F-37000 Tours, France
[2] Ctr Hosp Reg Univ Orleans, Serv Cardiol, F-45100 Orleans, France
[3] Univ Tours, EA4245, Transplantat Immun Inflammat, F-37032 Tours, France
[4] Univ Liverpool, Liverpool John Moores Univ, Liverpool Ctr Cardiovasc Sci, Liverpool L7 8TX, England
[5] Liverpool Heart & Chest Hosp, Liverpool L7 9NJ, Merseyside, England
[6] Sapienza Univ Rome, Dept Translat & Precis Med, I-00185 Rome, Italy
[7] Univ Milan, Dept Clin Sci & Community Hlth, I-20122 Milan, Italy
[8] IRCCS Ist Clin Sci Maugeri, I-20138 Milan, Italy
[9] Aalborg Univ, Dept Clin Med, DK-9000 Aalborg, Denmark
[10] Ctr hosp reg Univ, Fac med Tours, Serv cardiol, 2 Blvd Tonnelle, F-37000 Tours, France
关键词:
Atrial fibrillation;
Cluster analysis;
Outcomes;
Machine learning;
EURO HEART SURVEY;
RISK-FACTOR;
STROKE;
THROMBOEMBOLISM;
IMPACT;
SCORE;
D O I:
10.1016/j.acvd.2023.06.001
中图分类号:
R5 [内科学];
学科分类号:
1002 ;
100201 ;
摘要:
Background: Patients with atrial fibrillation are characterized by great clinical heterogeneity and com-plexity. The usual classifications may not adequately characterize this population. Data-driven cluster analysis reveals different possible patient classifications.Aims: To identify different clusters of patients with atrial fibrillation who share similar clinical pheno-types, and to evaluate the association between identified clusters and clinical outcomes, using cluster analysis.Methods: An agglomerative hierarchical cluster analysis was performed in non-anticoagulated patients from the Loire Valley Atrial Fibrillation cohort. Associations between clusters and a composite out-come comprising stroke/systemic embolism/death and all-cause death, stroke and major bleeding were evaluated using Cox regression analyses.Results: The study included 3434 non-anticoagulated patients with atrial fibrillation (mean age 70.3 & PLUSMN; 17 years; 42.8% female). Three clusters were identified: cluster 1 was composed of younger patients, with a low prevalence of co-morbidities; cluster 2 included old patients with permanent atrial fibrillation, cardiac pathologies and a high burden of cardiovascular co-morbidities; cluster 3 identified old female patients with a high burden of cardiovascular co-morbidities. Compared with cluster 1, clusters 2 and 3 were independently associated with an increased risk of the composite outcome (hazard ratio 2.85, 95% confidence interval 1.32-6.16 and hazard ratio 1.52, 95% confidence interval 1.09-2.11, respectively) and all-cause death (hazard ratio 3.54, 95% confidence interval 1.49-8.43 and hazard ratio 1.88, 95% confidence interval 1.26-2.79, respectively). Cluster 3 was independently associated with an increased risk of major bleeding (hazard ratio 1.72, 95% confidence interval 1.06-2.78).Conclusion: Cluster analysis identified three statistically driven groups of patients with atrial fibrillation, with distinct phenotype characteristics and associated with different risks for major clinical adverse events. & COPY; 2023 Elsevier Masson SAS. All rights reserved.
引用
收藏
页码:342 / 351
页数:10
相关论文