TY - JOUR
T1 - Impact of clinical phenotypes on management and outcomes in European atrial fibrillation patients
T2 - a report from the ESC-EHRA EURObservational Research Programme in AF (EORP-AF) General Long-Term Registry
AU - the ESC-EHRA EORP-AF Long-Term General Registry Investigators
AU - Proietti, Marco
AU - Vitolo, Marco
AU - Harrison, Stephanie L.
AU - Lane, Deirdre A.
AU - Fauchier, Laurent
AU - Marin, Francisco
AU - Nabauer, Michael
AU - Potpara, Tatjana S.
AU - Dan, Gheorghe Andrei
AU - Boriani, Giuseppe
AU - Lip, Gregory Y.H.
AU - Qoriani, G.
AU - Tavazzi, L.
AU - Maggioni, A. P.
AU - Dan, G. A.
AU - Nabauer, M.
AU - Kalarus, Z.
AU - Fauchier, L.
AU - Ferrari, R.
AU - Shantsila, A.
AU - Goda, A.
AU - Mairesse, G.
AU - Shalganov, T.
AU - Antoniades, L.
AU - Taborsky, M.
AU - Riahi, S.
AU - Muda, P.
AU - García Bolao, I.
AU - Piot, O.
AU - Nabauer, M.
AU - Etsadashvili, K.
AU - Simantirakis, E. N.
AU - Haim, M.
AU - Azhari, A.
AU - Najafian, J.
AU - Santini, M.
AU - Mirrakhimov, E.
AU - Kulzida, K.
AU - Erglis, A.
AU - Poposka, L.
AU - Burg, M. R.
AU - Crijns, H. J.G.M.
AU - Erküner,
AU - Atar, D.
AU - Lenarczyk, R.
AU - Martins Oliveira, M.
AU - Shah, D.
AU - Dan, G. A.
AU - Serdechnaya, E.
AU - Diker, E.
N1 - Funding Information:
DL has received investigator-initiated educational grants from Bristol-Myers Squibb (BMS), has been a speaker for Boehringer Ingelheim and BMS/Pfizer and has consulted for BMS, Boehringer Ingelheim and Daiichi Sankyo. LF has been a consultant or speaker for Bayer, BMS/Pfizer, Boehringer Ingelheim, Medtronic, Novartis; GB received small speaker’s fees from Medtronic, Boston, Boehringer Ingelheim and Bayer; GYHL has been a consultant and speaker for BMS/Pfizer, Boehringer Ingelheim and Daiichi Sankyo. No fees are directly received personally. All the disclosures happened outside the submitted work. All other authors have nothing to declare.
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/12/1
Y1 - 2021/12/1
N2 - 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.
AB - 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.
KW - Atrial fibrillation
KW - Clinical management
KW - Clinical phenotypes
KW - Cluster analysis
KW - Major adverse outcomes
UR - http://www.scopus.com/inward/record.url?scp=85117929197&partnerID=8YFLogxK
U2 - 10.1186/s12916-021-02120-3
DO - 10.1186/s12916-021-02120-3
M3 - Article
C2 - 34666757
AN - SCOPUS:85117929197
SN - 1741-7015
VL - 19
JO - BMC Medicine
JF - BMC Medicine
IS - 1
M1 - 256
ER -