Impact of clinical phenotypes on management and outcomes in European atrial fibrillation patients: a report from the ESC-EHRA EURObservational Research Programme in AF (EORP-AF) General Long-Term Registry.
Atrial fibrillation
Clinical management
Clinical phenotypes
Cluster analysis
Major adverse outcomes
Journal
BMC medicine
ISSN: 1741-7015
Titre abrégé: BMC Med
Pays: England
ID NLM: 101190723
Informations de publication
Date de publication:
20 10 2021
20 10 2021
Historique:
received:
01
05
2021
accepted:
08
09
2021
entrez:
20
10
2021
pubmed:
21
10
2021
medline:
3
11
2021
Statut:
epublish
Résumé
Epidemiological 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. We 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. A 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. 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.
Sections du résumé
BACKGROUND
Epidemiological 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.
METHODS
We 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.
RESULTS
A 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.
CONCLUSIONS
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.
Identifiants
pubmed: 34666757
doi: 10.1186/s12916-021-02120-3
pii: 10.1186/s12916-021-02120-3
pmc: PMC8527730
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
256Investigateurs
G Boriani Chair
(G)
G Y H Lip
(GYH)
L Tavazzi
(L)
A P Maggioni
(AP)
G-A Dan
(GA)
T Potpara
(T)
M Nabauer
(M)
F Marin
(F)
Z Kalarus
(Z)
L Fauchier
(L)
R Ferrari
(R)
A Shantsila
(A)
A Goda
(A)
G Mairesse
(G)
T Shalganov
(T)
L Antoniades
(L)
M Taborsky
(M)
S Riahi
(S)
P Muda
(P)
I García Bolao
(I)
O Piot
(O)
M Nabauer
(M)
K Etsadashvili
(K)
E N Simantirakis
(EN)
M Haim
(M)
A Azhari
(A)
J Najafian
(J)
M Santini
(M)
E Mirrakhimov
(E)
K Kulzida
(K)
A Erglis
(A)
L Poposka
(L)
M R Burg
(MR)
H Crijns
(H)
Ö Erküner
(Ö)
D Atar
(D)
R Lenarczyk
(R)
M Martins Oliveira
(M)
D Shah
(D)
G-A Dan
(GA)
E Serdechnaya
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T Potpara
(T)
E Diker
(E)
G Y H Lip
(GYH)
D Lane
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E Zëra
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U Ekmekçiu
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V Paparisto
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M Tase
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H Gjergo
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J Dragoti
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A Goda
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M Ciutea
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N Ahadi
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Z El Husseini
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M Raepers
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J Leroy
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P Haushan
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A Jourdan
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L Desteghe
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J Vijgen
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P Koopman
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G Van Genechten
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A Friart
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J Boreux
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Informations de copyright
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