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Phenotypes and outcomes in non-anticoagulated patients with atrial fibrillation: An unsupervised cluster analysis
Arnaud Bisson1, Ameenathul M Fawzy2, Giulio Francesco Romiti3
1Service de cardiologie, centre hospitalier régional universitaire et faculté de médecine de Tours, 37000 Tours, France; Service de cardiologie, centre hospitalier régional universitaire d'Orléans, 45100 Orléans, France; EA4245, transplantation immunité inflammation, université de Tours, 37032 Tours, France; Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, L7 8TX Liverpool, United Kingdom.
Insights
Cluster analysis revealed three distinct patient groups in atrial fibrillation, each with unique characteristics and varying risks for major adverse events like stroke and death.
Area of Science:
- Cardiology
- Clinical Data Science
Background:
- Atrial fibrillation (AF) patient populations exhibit significant clinical heterogeneity.
- Existing classifications may not fully capture AF patient complexity.
- Data-driven approaches like cluster analysis offer novel patient stratification.
Purpose of the Study:
- To identify distinct patient clusters within the atrial fibrillation population using cluster analysis.
- To evaluate the association between these identified clusters and key clinical outcomes.
Main Methods:
- Agglomerative hierarchical cluster analysis was applied to non-anticoagulated AF patients.
- Cox regression analyses assessed associations between clusters and composite outcomes (stroke/systemic embolism/death, all-cause death, major bleeding).
Main Results:
- Three patient clusters were identified from 3434 non-anticoagulated AF patients.
- Cluster 1: younger, fewer comorbidities. Cluster 2: older, permanent AF, cardiac/CV comorbidities. Cluster 3: older females, high CV comorbidities.
- Clusters 2 and 3 showed significantly increased risks for composite outcomes and all-cause death compared to Cluster 1. Cluster 3 also had higher major bleeding risk.
Conclusions:
- Cluster analysis successfully identified three distinct, statistically-driven groups of atrial fibrillation patients.
- These identified clusters possess unique phenotypic characteristics.
- The distinct clusters are associated with differential risks for major adverse clinical events.
Background:
Patients with atrial fibrillation are characterized by great clinical heterogeneity and complexity. 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 phenotypes, 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 outcome 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±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.
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