Abstract
BackgroundEpidemiological studies in atrial fibrillation (AF) illustrate that clinical complexity increase the risk of major adverse outcomes. We aimed to describe European AF patients’ clinical phenotypes and analyse the differential clinical course.MethodsWe performed a hierarchical cluster analysis based on Ward’s Method and Squared Euclidean Distance using 22 clinical binary variables, identifying the optimal number of clusters. We investigated differences in clinical management, use of healthcare resources and outcomes in a cohort of European AF patients from a Europe-wide observational registry.ResultsA total of 9363 were available for this analysis. We identified three clusters: Cluster 1 (n = 3634; 38.8%) characterized by older patients and prevalent non-cardiac comorbidities; Cluster 2 (n = 2774; 29.6%) characterized by younger patients with low prevalence of comorbidities; Cluster 3 (n = 2955;31.6%) characterized by patients’ prevalent cardiovascular risk factors/comorbidities. Over a mean follow-up of 22.5 months, Cluster 3 had the highest rate of cardiovascular events, all-cause death, and the composite outcome (combining the previous two) compared to Cluster 1 and Cluster 2 (all P < .001). An adjusted Cox regression showed that compared to Cluster 2, Cluster 3 (hazard ratio (HR) 2.87, 95% confidence interval (CI) 2.27–3.62; HR 3.42, 95%CI 2.72–4.31; HR 2.79, 95%CI 2.32–3.35), and Cluster 1 (HR 1.88, 95%CI 1.48–2.38; HR 2.50, 95%CI 1.98–3.15; HR 2.09, 95%CI 1.74–2.51) reported a higher risk for the three outcomes respectively.ConclusionsIn European AF patients, three main clusters were identified, differentiated by differential presence of comorbidities. Both non-cardiac and cardiac comorbidities clusters were found to be associated with an increased risk of major adverse outcomes.
Highlights
Epidemiological studies in atrial fibrillation (AF) illustrate that clinical complexity increase the risk of major adverse outcomes
In European AF patients, three main clusters were identified, differentiated by differential presence of comorbidities. Both non-cardiac and cardiac comorbidities clusters were found to be associated with an increased risk of major adverse outcomes
By examining the dendrogram produced by the clustering process and considering the Ward Linkage coefficients, we found that the distance between the points in which the elements grouped together became larger and the groupings became more heterogeneous after being expanded to 3 clusters
Summary
Epidemiological studies in atrial fibrillation (AF) illustrate that clinical complexity increase the risk of major adverse outcomes. Cluster analysis helps to identify the relevant clinical phenotypes, but has been applied to AF in relatively few studies [10,11,12,13] In those studies which investigated this particular approach, cluster analysis helped to identify patients with similar clinical characteristics which were different between the various groups ‘clinical phenotypes’), entailing differential management approach and differential risk for adverse outcomes, demonstrating how in groups of patients with different clinical characteristics AF can have a different clinical course [10,11,12,13] In those studies which investigated this particular approach, cluster analysis helped to identify patients with similar clinical characteristics which were different between the various groups (more or less prevalence of risk factors and comorbidities combined together, i.e. ‘clinical phenotypes’), entailing differential management approach and differential risk for adverse outcomes, demonstrating how in groups of patients with different clinical characteristics AF can have a different clinical course [10,11,12,13]
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